AI and Cybersecurity: A Comprehensive Analysis

Introduction to AI in Cybersecurity

Artificial Intelligence (AI) has revolutionized various industries, and cybersecurity is no exception. The integration of AI into cybersecurity frameworks has introduced a dynamic approach to combating cyber threats. With the increasing sophistication of cyber-attacks, traditional methods have proven inadequate, necessitating the adoption of AI-driven solutions. This article explores the intricate relationship between AI and cybersecurity, highlighting the benefits, challenges, and future prospects of this synergy.

The Role of AI in Enhancing Cybersecurity

Proactive Threat Detection

AI excels in proactive threat detection, leveraging machine learning algorithms to identify potential threats before they manifest into full-blown attacks. Traditional security systems rely on known threat signatures, but AI systems can analyze patterns and behaviors, allowing them to detect anomalies that indicate new or evolving threats. This proactive stance is crucial in today's fast-paced digital landscape.

Automated Response and Mitigation

AI-powered systems are capable of automated response and mitigation. Upon detecting a threat, these systems can autonomously take actions to neutralize it, minimizing the damage and reducing response times. Automated responses range from isolating affected systems to deploying patches and updates without human intervention, thereby enhancing overall security posture.

Predictive Analysis and Risk Assessment

Through predictive analysis and risk assessment, AI can forecast potential vulnerabilities and attacks. By analyzing historical data and current threat trends, AI models can predict where and how cybercriminals might strike next. This foresight enables organizations to bolster their defenses preemptively, prioritizing resources and measures where they are most needed.

Machine Learning in Cybersecurity

Anomaly Detection

Machine learning algorithms are adept at anomaly detection, which is essential for identifying deviations from normal behavior within networks. These algorithms continuously learn and adapt, improving their accuracy over time. By recognizing what constitutes normal activity, machine learning can highlight suspicious actions that warrant further investigation.

Behavioral Analysis

Behavioral analysis is another critical application of machine learning in cybersecurity. By monitoring user behavior and system interactions, machine learning models can identify unusual patterns that may signify malicious intent. This type of analysis is particularly effective in detecting insider threats, where the perpetrator is within the organization and has legitimate access to systems.

Threat Intelligence

AI enhances threat intelligence by aggregating and analyzing data from multiple sources, including social media, dark web forums, and threat databases. This comprehensive analysis provides a holistic view of the threat landscape, allowing security teams to stay ahead of emerging threats and adapt their strategies accordingly.

Challenges of Implementing AI in Cybersecurity

Data Privacy and Security

One of the primary challenges in integrating AI with cybersecurity is data privacy and security. AI systems require vast amounts of data to function effectively, raising concerns about the storage, handling, and protection of sensitive information. Ensuring that AI-driven solutions comply with data protection regulations is paramount.

Algorithm Bias

Algorithm bias poses a significant risk in AI applications. Biased algorithms can lead to false positives or negatives, undermining the efficacy of security measures. Continuous monitoring and updating of AI models are necessary to mitigate bias and ensure accurate threat detection and response.

Adversarial Attacks

AI systems themselves can be targets of adversarial attacks, where cybercriminals manipulate inputs to deceive the AI. These attacks can compromise the integrity of AI models, leading to incorrect threat assessments and responses. Developing robust AI models that can withstand adversarial tactics is an ongoing challenge.

Future Prospects of AI in Cybersecurity

Advanced Threat Hunting

The future of AI in cybersecurity lies in advanced threat hunting. AI will enable more sophisticated and proactive hunting techniques, identifying threats that are often missed by conventional methods. This will involve deeper integration of AI with security information and event management (SIEM) systems, providing real-time insights and enhanced situational awareness.

Enhanced User Authentication

Enhanced user authentication mechanisms, such as biometric verification and behavioral biometrics, will become more prevalent with AI advancements. These methods offer higher security levels by analyzing unique user characteristics, making it more difficult for unauthorized users to gain access.

Zero Trust Architecture

AI will play a pivotal role in the implementation of zero trust architecture, a security model that assumes no user or system is trustworthy by default. AI can continuously monitor and validate user actions, ensuring strict access controls and minimizing the risk of breaches.

Integrated Security Ecosystems

The future will see the emergence of integrated security ecosystems, where AI seamlessly integrates with various security tools and platforms. This holistic approach will provide comprehensive protection, from endpoint security to network defense, creating a cohesive and resilient cybersecurity framework.

Conclusion

The integration of AI in cybersecurity is not just a trend but a necessity in the modern digital age. As cyber threats become more sophisticated, the need for advanced, adaptive, and proactive security measures grows. AI offers unparalleled capabilities in threat detection, response, and prevention, making it an indispensable tool in the cybersecurity arsenal. However, addressing challenges such as data privacy, algorithm bias, and adversarial attacks is crucial for maximizing the potential of AI-driven solutions. The future of cybersecurity lies in the continued evolution and integration of AI technologies, paving the way for a safer digital world.

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AI and Cybersecurity: Trends, FREE AI Courses, Countermeasures, and Expert Insights

In today’s dynamic digital era, cybersecurity has become the need of the hour. Security teams constantly encounter challenges that require them to stay agile and leverage advanced strategies to mitigate malicious activities. Security frameworks are continuously threatened, with advancements paving the way for exploitation. AI and cybersecurity collectively have completely transformed the digital landscape, creating robust defense mechanisms. By using AI’s unmatched capacity to handle large volumes of data, we can now identify and evaluate threats with unprecedented accuracy.

When it comes to strengthening defense systems, AI has definitely partnered with security experts. Explore this article to learn how artificial intelligence in security can revolutionize cybersecurity and how you can take advantage of the FREE AI in Cybersecurity courses with every major EC-Council certification.

What is the Role of AI in Cybersecurity?

Before AI, conventional systems were less efficient in detecting and tackling unknown attacks, resulting in misleading outputs that proved hazardous to an organization’s security framework. However, the traditional approach saw a significant advancement with AI in addressing these challenges and delivering result-driven outcomes. Nowadays, AI cybersecurity solutions are seen as a driving force, owing to their capability to navigate threats in advance and suggest solutions to gain the upper hand over cybercriminals.

The rise of AI in the face of escalating threats has been instrumental in dealing with ever-evolving security challenges and developing strategies ahead of time. Organizations are now leveraging artificial intelligence and security along with professional expertise and new tools to protect their sensitive data and critical systems. AI-based solutions can help keep pace with emerging threats, detect and respond to new threats, and offer better cyber protection. However, like two sides of a coin, AI can be both a blessing and a curse.

What Are the Potential Threats Posed by AI in Cybersecurity

It is important to understand that while cybersecurity and AI together can enhance security, AI can also be exploited by threat actors. Hackers can leverage AI in several ways to pose significant threats to cybersecurity. AI can automate and enhance social engineering attacks, psychologically tricking individuals into revealing sensitive information and compromising data integrity and confidentiality. Deepfake technology, powered by AI, can manipulate visual or audio content to impersonate individuals, leading to identity theft, misinformation, and other malicious activities. Hackers can also manipulate AI algorithms by feeding them deceptive information, resulting in incorrect outputs and potentially undermining the effectiveness of AI-based security systems. Furthermore, attackers can develop targeted malware that can evade AI-based detection systems, making it harder for traditional security measures to identify and mitigate these threats. Let’s explore some potential risks associated with AI from the perspective of cybersecurity professionals worldwide.

Potential Risks of AI in Cybersecurity: EC-Council C|EH Threat Report 2024 Findings

• 77.02% believe that AI could automate the creation of highly sophisticated attacks.
• 69.72% think AI could facilitate the development of autonomous and self-learning malware
• 68.26% perceive the risk of AI exploiting vulnerabilities rapidly.
• 68.06% are concerned about AI enhancing phishing and social engineering attacks.
• 55.40% highlight the challenge of detecting and mitigating AI-powered attacks.
• 50.83% worry about AI manipulating data on a large scale.
• 42.45% are concerned about AI creating sophisticated evasion signatures to avoid detection.
• 36.51% note the lack of accountability and attribution in AI-driven attacks.
• 31.74% believe AI could facilitate highly targeted attacks.

How AI Enhances Threat Detection

Despite the potential risks, AI also offers substantial advantages in enhancing threat detection and response. In a survey of cybersecurity professionals worldwide, approximately 67% of respondents stated that AI applications would assist with threat detection (EC-Council, 2024). In another survey, approximately 60% of participants identified enhanced threat detection as the foremost advantage of integrating AI into their daily cybersecurity practices (Borgeaud, 2024).

Artificial intelligence in security provides numerous benefits, particularly in how threats are detected and remediated. AI algorithms work on a proactive approach to analyzing data and identifying threats and malicious activities. Moreover, understanding the foundational elements of AI’s role in threat detection is essential for leveraging its full potential. Threat detection by AI stands on two main pillars, which are as follows:

- Behavioral Analysis: By using AI, cybersecurity tools can develop insights into normal user behavior patterns. This helps them determine changes and detect any loopholes that may cause a breach.

- Real-time Monitoring and Incident Response: AI-powered systems can continuously monitor network traffic to identify signs of malware and raise alerts. Once a threat has been detected, AI helps launch an effective incident response that can initiate actions to reduce the overall impact.

How are AI-Powered Cybersecurity Solutions Defending Organizations?

AI and cybersecurity have become intricately linked, with AI-powered cybersecurity solutions forming the backbone of an organization’s defense systems. The effectiveness of AI-powered cybersecurity solutions relies on a set of core technologies that drive their capabilities and applications. Apart from advanced threat detection and simulated incident response, here are some other ways in which AI contributes to enhanced organizational security framework:

• Predictive Analysis: This leverages data analysis, machine learning, artificial intelligence, and statistical models to recognize patterns and predict future behavior, enabling proactive security measures.
• Phishing Detection: AI-powered anti-phishing tools use techniques like Natural Language Processing (NLP) to thoroughly analyze email content, attachments, and embedded links, assessing authenticity and detecting potential threats.
• Network Security: AI employs techniques such as anomaly detection and deep packet inspection to analyze network traffic and behavior. It identifies suspicious anomalies to facilitate immediate response and enhance network security.
• Threat Intelligence Integration: AI systems integrate threat intelligence by continuously analyzing and correlating data on the latest attack strategies, tactics, and techniques to stay updated and improve defensive measures.
• Endpoint Protection: AI assesses the entire endpoint behavior to detect and respond to malicious activities. Endpoint security uses machine learning to look for suspicious activities and immediately block them.

As AI continues to enhance various aspects of cybersecurity, it also finds applications in more specific areas, such as ethical hacking. One notable example is ChatGPT, which has been adapted to assist ethical hackers in numerous ways, showing how versatile and adaptable AI can be in addressing modern cybersecurity challenges.

ChatGPT in Ethical Hacking

ChatGPT can be utilized in ethical hacking for various purposes. It can assist ethical hackers in gathering information and summarizing key points, developing automated responses, analyzing datasets, and highlighting potential weaknesses in a system. It can also prove beneficial in planning incident response and improving preparedness for security incidents. However, while ChatGPT enhances many aspects of ethical hacking, human expertise is crucial for interpreting results, making final decisions, and managing complex, context-specific situations that AI cannot fully understand.

Free AI Cybersecurity Toolkit with EC-Council Certifications

Enhance your cybersecurity skills with free AI-focused courses included in the Certified Ethical Hacker (C|EH) and other major EC-Council certification programs for Active Certified Members. Access cutting-edge training to stay ahead in the evolving landscape of AI in cybersecurity. Below are three essential courses in the AI Cybersecurity toolkit:

1. ChatGPT for Ethical Hackers

Explore ChatGPT’s applications in ethical hacking, from fundamentals to advanced exploitation and best practices. Here’s what you’ll learn:

• ChatGPT 101: Fundamentals for Ethical Hackers
• ChatGPT Prompts in Action: Reconnaissance and Scanning
• ChatGPT for Social Engineering
• Exploring Credentials: Passwords and Fuzzing with ChatGPT
• Web Security: Perform SQL Injection, Blind Injection, and XSS with ChatGPT
• Exploiting Application Functions with ChatGPT
• Advanced Exploit Development with ChatGPT
• Analyse Code with ChatGPT: Detecting and Exploiting Vulnerabilities
• Enhancing Cyber Defense with ChatGPT
• Ethical Hacking Reporting and ChatGPT Best Practices

2. ChatGPT for Threat Intelligence and Detection

Master ChatGPT’s use in cyber threat intelligence, from optimizing for threat detection to practical application and futureproofing. Here’s what you’ll learn:

• Introduction to ChatGPT in Cybersecurity
• Optimizing ChatGPT for Cyber Threats
• Mastering Threat Intelligence with ChatGPT
• ChatGPT for Intelligence Gathering and Analysis
• Futureproofing Against AI Cyber Threats
• Putting Knowledge into Practice

3. Generative AI for Cybersecurity

Understand generative AI and large language models, focusing on their architecture, security controls, and practical implementation in cybersecurity. Here’s what you’ll learn:

• Decoding Generative AI and Large Language Models
• LLM Architecture: Design Patterns and Security Controls
• LLM Technology Stacks and Security Considerations
• Open-sourced vs. Closed-sourced LLMs: Making the Choice
• Hands-on: Prompt Engineering and LLM Fine-tuning

*The above FREE courses are available post-course completion only to EC-Council Active Certified Members. Active Certified Members whose certifications are in good standing can access these courses by logging in to their EC-Council Aspen account.

What Are the In-Demand Skills Professionals Need to Implement AI in Cybersecurity?

A strong foundation in cybersecurity and its related fundamentals is essential to comprehend the threat landscape, emerging vulnerabilities, and attack vectors. Implementing AI in cybersecurity requires an amalgamation of technical and strategic skills with hands-on experience. Here are some in-demand skills professionals must be well-versed in:

• Machine Learning (ML) and Data Science: Proficiency in ML and data science is important for developing AI models that can examine databases and identify potential threats. These skills enable cybersecurity professionals to leverage AI for predictive analytics and automated threat detection, making them indispensable for implementing AI-driven cybersecurity solutions.
• Statistics and Frameworks: A strong grasp of statistics is necessary for understanding and interpreting data, which is the foundation of AI model development. Familiarity with frameworks such as Scikit-Learn, Keras, TensorFlow, and OpenAI is essential for crafting AI-powered applications with faster coding and accuracy, enabling professionals to develop robust models and deploy them effectively in cybersecurity contexts.
• Programming Skills: Knowledge of programming languages such as Python, R, or Julia is instrumental in developing and implementing AI algorithms and will help professionals customize and optimize AI solutions to meet specific security needs.
• Natural Language Processing (NLP): NLP skills are crucial for analyzing textual data and written content to identify security intrusions and enhance AI-driven threat detection and response.
• Network Security: AI plays a significant role in enhancing threat detection capabilities within a network, but to apply AI models effectively, professionals must have a solid grasp of network security protocols, architecture, and design. Experience with configuring and managing firewalls and Intrusion Detection Systems (IDS) is crucial, as AI can enhance these systems to better detect and respond to security incidents, providing a stronger defense against cyber threats.
• Cloud Security: Cloud computing skills are essential for implementing AI in cybersecurity. Professionals must be familiar with major cloud platforms and their security features. Additionally, knowledge of cloud-based AI tools, understanding the security implications of service models, and expertise in encryption, IAM, and regulatory compliance are necessary to ensure robust cloud security and the effective deployment of AI solutions.
• Ethical Hacking: Ethical hacking is essential for identifying vulnerabilities and reinforcing security measures with AI. Professionals need skills in penetration testing, vulnerability assessment, risk mitigation, and exploit development to uncover weaknesses and strengthen AI security measures. These abilities are crucial for effectively implementing AI in cybersecurity and ensuring robust protection against evolving threats.

With a comprehensive understanding of the in-demand skills required to implement AI in cybersecurity, it is essential to examine the current landscape and the evolving threats that professionals face. As AI continues to evolve within the cyber domain, it introduces both opportunities and challenges. The EC-Council C|EH threat report highlights the increasing use of AI by adversaries to automate and enhance their attacks, necessitating a higher level of awareness and preparedness among cybersecurity professionals while emphasizing the importance of understanding AI’s capabilities, limitations, and future direction.

Source: eccouncil.org

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Guide to Cryptanalysis: Learn the Art of Breaking Codes

What is Cryptanalysis?

Cryptanalysis is the field of studying a cryptographic system, learning to decipher and understand hidden messages without having the original decryption key. Cryptanalysis involves observing the properties of encrypted messages and discovering weaknesses and vulnerabilities in the encryption protocol that can be exploited to reveal the original contents.

The terms cryptography and cryptanalysis are closely linked and often even confused. Cryptography is the practice of hiding or encoding information so that only its intended recipient(s) will be able to understand it. Cryptography is related to other fields, such as steganography, which attempts to hide information “in plain sight” (disguising not only the message but also the fact that there is a hidden message, to begin with). On the other hand, cryptanalysis attempts to decode the messages that have been encoded using cryptography. Cryptanalysis and cryptography, therefore, play complementary roles: cryptography turns plaintext information into ciphertext, while cryptanalysis seeks to convert this ciphertext back into plaintext (Bone, 2023).

Cryptanalysis plays a crucial role in evaluating the security of cryptographic systems. In general, the more difficult it is to crack a cryptographic system using cryptanalysis, the more secure the system is.

How Does Cryptanalysis Work?

Cryptanalysis uses a wide range of tools, techniques, and methodologies to decode encrypted messages. These include:

◉ Mathematical analysis: The use of mathematical principles and algorithms to find weaknesses in a cryptographic system. This may involve using mathematical properties to find certain patterns or relationships in the encrypted message and detect vulnerabilities in the encryption protocol itself.

◉ Frequency analysis: The study of the frequency of different letters and symbols in an encrypted message. This technique is particularly effective against so-called “substitution ciphers,” in which each letter or symbol in the original message is simply replaced with another.

◉ Pattern recognition: Identifying repetitive sequences or patterns within an encrypted message. Recurring patterns may correspond to common words or phrases (such as “the” or “and”), helping cryptanalysts partially or fully decrypt the message.

Cryptanalysis techniques vary depending on the type of cipher being used. As mentioned above, for example, basic substitution ciphers can be attacked by calculating the most common letters in the message and comparing the output with a list of the most frequent letters in English. Transposition ciphers are another cryptography method in which the letters of the message are rearranged without being changed. These ciphers are vulnerable to “anagramming” techniques: trying different permutations of letters and hunting for patterns or recognizable words in the results.

What Are the Types of Cryptanalysis?

Cryptanalysis is a tremendously rich and complex field with many different approaches. The types of cryptanalysis include:

◉ Known-plaintext attack: In a known-plaintext attack (KPA), the cryptanalyst has access to pairs of messages in both their original and encrypted forms. This allows the attacker to analyze how the encryption algorithm works and produce a corresponding decryption algorithm.

◉ Chosen-plaintext attack: A chosen-plaintext attack (CPA) is even more powerful than a KPA—the cryptanalyst can choose the plaintext and observe the corresponding ciphertext. This allows the attacker to gather more information about the algorithm’s behavior and potential weaknesses.

◉ Differential cryptanalysis: In differential cryptanalysis, the cryptanalyst has access to pairs of messages that are closely related (for example, they differ only by one letter or bit), as well as their encrypted forms. This allows the attacker to examine how changes in the original text affect the algorithm’s ciphertext output.

For relatively basic ciphers, a so-called ”brute-force attack” may be enough to crack the code (Georgescu, 2023). In a brute-force attack, the attacker simply tries all possible cryptographic keys until the right combination is discovered.

The efficacy of brute-force attacks is highly dependent on the computational complexity of the encryption algorithm. The more complex the algorithm is and the more keys to analyze, the harder it will be to crack the code.

As computers become faster, however, cryptographic algorithms that were previously secure have now become more vulnerable. For example, organizations such as NIST have retired their use of SHA-1, one of the first widely used encryption algorithms, in favor of its more complex successors, SHA-2 and SHA-3 (NIST, 2022).

What Are the Challenges in Cryptanalysis?

Cryptanalysis is a dynamic and challenging area of study. Below are just a few of the major difficulties for today’s cryptanalysts:

◉ Key size and algorithm complexity: The larger the key used to encrypt information, the higher the number of possible keys used in the encryption algorithm. This makes algorithms more complex and brute-force attacks more difficult or even impossible (at least on a human timescale).

◉ Encryption protocols: Cryptanalysis focuses not only on the mathematical properties of the encryption algorithm but also on how the algorithm is implemented in real-world encryption protocols. Vulnerabilities in this implementation are often easier to attack than the algorithm itself.

◉ Lack of KPA or CPA attacks: Known-plaintext and chosen-plaintext attacks are often the best-case scenarios for attackers seeking to understand an algorithm’s behavior. In the real world, however, cryptanalysts rarely have access to large amounts of this data — for example, they may only have ciphertext and not plaintext to analyze.

Organizations seeking to keep their information safe should follow a number of tips and best practices to make cryptanalysis harder for an attacker. For one, they should choose robust cryptographic algorithms that are computationally difficult to solve. In addition, they should store their encryption keys in a safe location using strong access control to prevent them from being compromised.

What Are the Ethical Considerations in Cryptanalysis?

Like many other topics in IT security, cryptanalysis comes with its own set of issues, controversies, and considerations. Would-be cryptanalysts need to obey ethical boundaries and responsibilities, following guidelines such as:

◉ Getting authorization: Cryptanalysis should only be carried out with the target’s permission, which is a best practice observed in ethical hacking. Attempting to break encryption schemes without authorization is often considered illegal.

◉ Privacy and data protection: Information is often encrypted because it is sensitive or confidential (such as personal data, healthcare records, or financial details). Cryptanalysts must preserve data privacy even when the encryption algorithm is successfully cracked.

◉ Responsible disclosure: When cryptanalysts discover a weakness in a cryptographic system, this vulnerability should be appropriately reported as soon as possible. For example, responsible disclosure typically involves notifying the affected parties so they can discreetly fix the issue rather than making a public announcement.

Source: eccouncil.org

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The Importance of IoT Security: Understanding and Addressing Core Security Issues

Artificial Intelligence (AI) powered tools have become prevalent in the cybersecurity landscape. AI-powered tools are crucial in identifying cyberattacks, mitigating future threats, automating security operations, and identifying potential risks. On the one hand, introducing AI in the global cybersecurity industry has led to the automation of various tasks. Still, on the other hand, it has also enabled threat actors to design and attempt more sophisticated attacks. Additionally, AI is recognized as a fundamental element in the future of cybersecurity as researchers continue to develop sophisticated computing systems capable of effectively detecting and isolating cyber threats. The advancement of AI in cybersecurity holds great promise for enhancing the resilience and effectiveness of defense mechanisms against evolving cyber risks.

With the increased use of AI, an increase in potential risks and challenges in the form of privacy concerns, ethical considerations around autonomous decision-making, the need for continuous monitoring and validation, etc., could also be observed. Thus, the question of whether the industry needs to regulate the use of AI in the cybersecurity domain also arises. Cybersecurity Exchange got in touch with Rakesh Sharma, Enterprise Security Architect at National Australia Bank, to learn his views on the role of artificial intelligence in cybersecurity and the need for AI regulation. Rakesh Sharma is a cyber security expert with over 17 years of multi-disciplinary experience and has worked with global financial institutions and cyber security vendors. Throughout his career, Rakesh Sharma has consistently accomplished notable professional achievements, demonstrating expertise in designing and implementing resilient security strategies. His extensive experience and strong leadership qualities position him as a critical driver of innovation in safeguarding organizations against emerging cyber threats, prioritizing preserving data integrity and confidentiality.

1. How would you describe the current role of artificial intelligence in cybersecurity? What are some critical areas where AI is being applied effectively?

AI has the potential to revolutionize the way organizations defend themselves against ever-evolving cyber threats. By leveraging the power of AI, organizations can automate many tasks that were previously performed by human security analysts, resulting in faster threat detection and remediation. AI can adapt to new threats and constantly update its algorithms, ensuring that organizations stay one step ahead of cybercriminals.

AI is being applied in a number of critical areas of cybersecurity, such as automating incident response, improving vulnerability identification and management, strengthening user authentication and enhancing behavioral analysis for malware detection. It has helped security teams in detection of unknown malware, suspicious patterns, fraudulent activities, anomalous behaviours, insider threats, unauthorized access attempts and a lot more. With the actionable insights provided by AI-enabled cybersecurity systems, organizations can make better security decisions and effectively protect their networks, data, and users.

2. In your opinion, what are the significant advantages that AI brings to cybersecurity? Can you provide any specific examples or use cases?

One of the key advantages of AI-powered cybersecurity systems is their ability to analyze vast amounts of data in real-time. This allows organizations to detect and respond to threats more rapidly, minimizing the potential damage caused by attacks. Traditional manual methods of threat detection and analysis can be time-consuming and prone to errors.

Another significant advantage is continuous learning and adaptation. AI technologies, particularly unsupervised machine learning, have the ability to learn from new data and adapt to changing threat landscapes. This enables AI systems to improve their detection capabilities over time, staying up-to-date with emerging threats and evolving attack techniques.

One of the common use cases where AI is playing a vital role are SIEM, Security Analytics and SOAR platforms in cloud where it is enabling faster and more accurate threat detection, leveraging threat intelligence, analyzing behavior, automating response actions, and facilitating proactive threat hunting. It helps organizations strengthen their cybersecurity defenses and respond effectively to evolving threats.

3. Conversely, what are the potential risks or challenges associated increased use of AI in cybersecurity? How can these be mitigated?

AI in cybersecurity has the potential to be a major force for good but comes with certain challenges. We have been hearing a lot about Adversarial AI which means AI systems themselves can become targets of adversarial attacks where attackers can manipulate or trick AI systems to make incorrect decisions. These systems can be complex and may lead to unknown vulnerabilities.

Since these systems rely heavily on input data, the accuracy and biasness in data are important factors to be considered when training AI models. Additionally, there are privacy and ethical concerns on the usage of sensitive data for making decisions and a governance and oversight is required to involve humans in decision making process than relying solely on AI systems because they seem to be black boxes to end users. The explainability of AI systems pose a challenge because it could be system complexity or intellectual property issue associated with AI algorithms which end-users want to understand to determine how AI system is making a decision or performing a task with fairness.

Other concerns are around regulatory compliance or legal requirements which are still evolving and may not be applicable across all industries and countries.

4. How can security teams mitigate these AI-enabled risks with threat actors ramping up to include automation and AI in their invasive efforts?

Security teams must adopt security solutions with AI capabilities to detect and respond to emerging threats in real-time and stay ahead in the game of cyber security. AI systems can automate repetitive security operations activities and free up security analysts to focus on higher-priority tasks, such as threat hunting.

They also need to maintain constant vigilance and keep up-to-date with the latest advancements in AI technology and the tactics used by threat actors. By applying adversarial machine learning techniques to detect and counter AI-generated attacks, they can improve the security and resilience of AI systems.

Regular penetration testing and red teaming exercises should be conducted to identify vulnerabilities in AI systems and assess their effectiveness against AI-driven attacks. Compliance with relevant regulations and frameworks governing AI and cybersecurity is crucial to ensure adherence to standards and protect against legal and operational risks. Collaboration with other organizations, security vendors, and industry groups is important to foster information sharing and exchange insights about AI-enabled threats.

5. Why do you believe there is a need to regulate the use of AI in the cybersecurity domain? Should these regulations also expand to cover AI’s impact on workforce substitution?

I believe that as AI systems become more capable over time, they become attractive targets for malicious actors seeking to exploit their potential. AI can be used to launch targeted attacks, make mission critical decisions and potentially endanger lives or cause physical harm, so they need to be designed with the principles of responsible and Ethical AI and need robust governance framework and oversight.

AI can also be misused in spreading misinformation and disinformation. AI algorithms can be utilized to generate fake news articles, social media posts, or even deepfake videos, which can be used to manipulate public opinion, sow discord, or foster distrust leading to social, political, and economic disruptions. It is therefore important to have regulations around applications of AI.

Although there will be some workforce displacement due to AI but at the same time new jobs will be created too to develop, maintain and secure AI systems. Regulations surely can strike a balance between fostering AI innovation and safeguarding the interests of the workforce.

6. With AI evolving rapidly, do you believe current regulations adequately address the potential risks and ethical concerns surrounding AI in cybersecurity? Why or why not?

Current regulations may not sufficiently address the potential risks and ethical concerns posed by the evolving AI in cybersecurity. This is primarily due to the lack of specificity in existing regulations, the rapid pace of technological advancements, and the interdisciplinary nature of AI and cybersecurity. The language and scope of current regulations may not comprehensively cover the unique challenges of AI-driven cyber threats. Moreover, the rapid evolution of AI technology often outpaces the development of regulations, making it difficult to keep up with emerging AI-enabled risks.

7. What, in your view, should be the key elements of AI regulation in the context of cybersecurity? Are there any specific principles or guidelines that should be implemented?

It is crucial for regulations to address some key elements to promote responsible and secure use of AI in cybersecurity while considering jurisdiction-specific requirements and industry dynamics.

These regulations should emphasize the need for transparency and explainability in AI systems, ensure the data privacy, promote ethical use of AI and prohibit misuse, establish accountability and liability frameworks, require independent audits, involve human oversight, encourage collaboration and information sharing, and training users so that they are equipped to use AI systems safely and responsibly.

Source: eccouncil.org

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Is AI Really a Threat to Cybersecurity?

It is true to say that the introduction of Artificial Intelligence, also known as AI models are a blessing to the IT industry. It has very well connected humans and the natural environment with technology in such a way that no one has ever expected. Machines have got enough power to replace humans. All thanks to AI!!!

But, now the question arises is what is AI (Artificial Intelligence)? If it’s playing an important role in the IT sector then why it’s taking so much time to take over all the security management of the industry? Why do people consider it a threat? Is it really a threat?

What is Artificial Intelligence?

The field of science that is mainly concerned with getting computers to Think, Learn, and Do – all these being performed without any human intelligence is termed Artificial Intelligence (AI). But, only training any computer just on the provided dataset and information and then asking that machine for a valid prediction is generally termed Machine Learning which is the initial phase of any Artificial Intelligent Model. 

A Machine learning model which learns from the data (also past experiences) has been provided. But, using that data when the model is capable of making its prediction whether it belongs to that data or not is termed an AI model. AI models have just upgraded Machine Learning models which after training learn from their mistakes and then backpropagate to rectify the data or the values that were responsible for that wrong prediction. This way the model keeps on learning from the predictions as well and also with the real-world data that it encounters during its further phases.

Just like a human learns to walk or talk just by failing multiple times in his attempts and then eventually becomes the expert in walking or talking. These models tend to become more mature and powerful over time as they keep on learning, keeps on making new conclusions, and guessing the future prediction. There is no doubt that it will take over the world one day as the calculations, the analysis that a machine can perform in a fraction of seconds will take years to get solved via a human.

What is the State of AI Now?

For the best analysis of this question, it’s best to just look around ourselves. We all can notice a drastic change and progress in our surroundings, who is responsible for this? It’s today’s technology, due to this Artificial Intelligence or AI-based machinery only now the productivity of every task has increased by multiple times, the goods are now available much quicker and reasonable rates anywhere over the world. From manufacturing to transportation to development and security every field has been flourished with the introduction of AI-themed products and appliances. But is also true that we humans have not even scratched the surface of AI till now, It still has a lot to discover. We have understood its importance, its use, and its demand, but we still can’t predict how much potential an AI model has. For now, large factories, machinery, robotic arms, and many more are controlled via AI. Today the whole world’s house is being automated using AI-based Siri and Alexa.

But truly speaking it’s not even 10% of what the AI model can serve us. Engineers are working on unlocking much more merits of the Artificial Intelligence model and as the termed machine will automatically become more intelligent and experienced over time. affecting every industry at a scale that a human can just have dreamed for.

Artificial Intelligence in Cybersecurity

If every industry is getting affected by this Artificial Intelligence, then their safety and security are also a major concern because if a model is getting educated for any industry it must be exploring through multiple past, present, and future planned data of that industry and therefore as a machine it’s well predicted that a machine will always have those data in its storage unit, not like humans who will forget any information as per the passage of time. So keeping the data secure and not letting that vital data to any wrong hand is a big responsibility. This is done very efficiently by Cybersecurity companies till now, but a blend of AI in this is also quite new and questionable as well. 

Cybersecurity companies use complex algorithms to train their AI models on how to detect viruses and malware so that AI can run its pattern recognition software and stop that. We can train AI to catch the smallest ever ransomware and isolate it from the system. Considering the strength and potential of AI models, what if AI leads our security system i.e fully automated, quick, and efficient. There will be no need for a passcode, an automatic face recognition system for whoever is entering the department, How about the system which can directly track which person is using the account and the location too, and all his biometrics just in one click, no cyberattacks, no data hacking. Won’t it be amazing? Yes, this is the future, but why not today?

Artificial Intelligence as a Threat

Till now Artificial Intelligence has served a lot in cybersecurity like in credit card fraud detection, spam filter, credit scoring, user authentication, and hacking incident forecasting. Even after serving this much in cybersecurity the role of AI is still limited in their field just because:

1. Implementation of AI in cybersecurity will cost more power consumption, more skilled developers, proper server set up raising the expense of that company.

2. If security is totally handed over to AI, there are chances that hackers introduce more skill models of AI resulting in a much more destructive hacking beyond anyone’s imagination causing a threat.

3. Data provided to AI if altered or guided wrongly will result in false predictions of their models which will serve as a path to the hackers.

4. Every AI model is provided with a large amount of data set to learn and then predict and that data can be helpful for hackers if they can retrieve the data provided to the model via any means.

Future of AI in Cybersecurity

For any country or even organization that matters, data is their real treasure, and no one can afford to lose it anyhow, as the max potential of AI is not defined, and no one can risk their security fully to Artificial Intelligence. Considering the future, yes, the world will be dominated via AI technology, But in a general sense, it can never take over Cybersecurity, as there is no finish line to AI learning skills. With time, it will keep on enhancing which can lead to a path for hackers bringing up more skilled and experienced AI models that will be leading the security. Though AI will be always an important part of Cybersecurity because without it, it won’t be able to keep up with the upcoming technologies for its prevention.

Source: geeksforgeeks.org

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Difference Between Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence: Artificial Intelligence is basically the mechanism to incorporate human intelligence into machines through a set of rules(algorithm). AI is a combination of two words: “Artificial” meaning something made by humans or non-natural things and “Intelligence” meaning the ability to understand or think accordingly. Another definition could be that “AI is basically the study of training your machine(computers) to mimic a human brain and it’s thinking capabilities”. AI focuses on 3 major aspects(skills): learning, reasoning and self-correction to obtain maximum efficiency possible.

Machine Learning: Machine Learning is basically the study/process which provides the system(computer) to learn automatically on its own through experiences it had and improve accordingly without being explicitly programmed. ML is an application or subset of AI. ML focuses on the development of programs so that it can access data to use it for themselves. The entire process makes observations on data to identify the possible patterns being formed and make better future decisions as per the examples provided to them. The major aim of ML is to allow the systems to learn by themselves through the experience without any kind of human intervention or assistance.

Deep Learning: Deep Learning is basically a sub-part of the broader family of Machine Learning which makes use of Neural Networks(similar to the neurons working in our brain) to mimic human brain-like behavior. DL algorithms focus on information processing patterns mechanism to possibly identify the patterns just like our human brain does and classifies the information accordingly. DL works on larger sets of data when compared to ML and prediction mechanism is self-administered by machines.

Below is a table of differences between Artificial Intelligence, Machine Learning and Deep Learning:

Artificial Intelligence	Machine Learning	Deep Learning
AI stands for Artificial Intelligence, and is basically the study/process which enables machines to mimic human behaviour through particular algorithm.	ML stands for Machine Learning, and is the study that uses statistical methods enabling machines to improve with experience.	DL stands for Deep Learning, and is the study that makes use of Neural Networks(similar to neurons present in human brain) to imitate functionality just like a human brain.
AI is the broader family consisting of ML and DL as it’s components.	ML is the subset of AI.	DL is the subset of ML.
AI is a computer algorithm which exhibits intelligence through decision making.	ML is an AI algorithm which allows system to learn from data.	DL is a ML algorithm that uses deep(more than one layer) neural networks to analyze data and provide output accordingly.
Search Trees and much complex math is involved in AI.	If you have a clear idea about the logic(math) involved in behind and you can visualize the complex functionalities like K-Mean, Support Vector Machines, etc., then it defines the ML aspect.	If you are clear about the math involved in it but don’t have idea about the features, so you break the complex functionalities into linear/lower dimension features by adding more layers, then it defines the DL aspect.
The aim is to basically increase chances of success and not accuracy.	The aim is to increase accuracy not caring much about the success ratio.	It attains the highest rank in terms of accuracy when it is trained with large amount of data.
Three broad categories/types Of AI are: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI)	Three broad categories/types Of ML are: Supervised Learning, Unsupervised Learning and Reinforcement Learning	DL can be considered as neural networks with a large number of parameters layers lying in one of the four fundamental network architectures: Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks and Recursive Neural Networks
The efficiency Of AI is basically the efficiency provided by ML and DL respectively.	Less efficient than DL as it can’t work for longer dimensions or higher amount of data.	More powerful than ML as it can easily work for larger sets of data.
Examples of AI applications include: Google’s AI-Powered Predictions, Ridesharing Apps Like Uber and Lyft, Commercial Flights Use an AI Autopilot, etc.	Examples of ML applications include: Virtual Personal Assistants: Siri, Alexa, Google, etc., Email Spam and Malware Filtering.	Examples of DL applications include: Sentiment based news aggregation, Image analysis and caption generation, etc.

Source: geeksforgeeks.org

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How can Artificial Intelligence Impact Cyber Security in the Future?

What are the most important apps on your smartphone? Maybe it is your bank app? Or even your Gmail account? Whatever important app it is, chances are it is password protected or secured in multiple ways.

Read More: EC-Council Certified Security Analyst (ECSA v10)

This cybersecurity becomes even more important in a professional setting where your company account has multiple barriers such as antivirus, password protection, etc. These are all elements of cybersecurity that are extremely necessary to secure both your personal and professional accounts from malicious users and hackers.

But conventional cybersecurity methods are not enough these days. Hackers can access accounts remotely and cause data breaches, identity thefts, loss of money, etc. In this data age, data is the most unsafe and easily targeted commodity. In such a situation, Artificial Intelligence can provide immense help to the cybersecurity industry, especially since many cybercriminals are already using this technology. As they say, it is only possible to fight fire with fire! But how can artificial intelligence be so important in cybersecurity? Let’s understand this in detail and then see the applications of artificial intelligence in cybersecurity.

Why is Artificial Intelligence so important in Cyber Security?

Artificial Intelligence is a double-edged sword in cybersecurity. There is a lot of concern that hackers can use AI to automate cyber-attacks while simultaneously reducing the numbers of humans needed. After all, if there are complex AI algorithms then hackers need fewer people to coordinate and implement their cyber-attacks which have more chances of being successful. Another advantage that AI provides hackers is that it is much easier to find out system vulnerabilities and attack networks such that the attacks are never even recognized.  

But fear not! If Artificial Intelligence can help hackers then it can also help companies in improving their cybersecurity so that there is a lesser chance of cyber-attacks. There are many facets of cybersecurity in which AI can have multiple applications such as intruder detection, prevention of phishing attacks, user behavior analysis, etc. Let’s see these in detail now:

1. Intruder Detection

Artificial Intelligence methods can be a big help in the field of intruder detection in cybersecurity. They can help in detecting and defending against any intruders in the system using past insights into intruder activity patterns. For example, intruders in the system may be engaging in unnatural behaviors such as sending and receiving large amounts of data or suddenly changing the communication patterns. These signs of intruders in the system are very difficult to catch for cybersecurity professionals, especially in large companies where there is a lot of network traffic. Here, the AI-powered intruder detection systems can be used to monitor the network for any unwanted intruders.

2. User Behavior Analysis

Sudden changes in the behaviors of existing users can be a sign of a cyber-attack in the network. This could happen if a malicious user stole the login credentials of a legitimate user and then illegally logged into the network using those credentials. But these behavioral changes are extremely hard to identify, especially in a large network. In such a situation, artificial intelligence can be used to detect and block the compromised user accounts which depict suspicious behavioral changes. AI can do this by creating a user behavioral profile of all the users which includes their login and logout patterns, data transfer patterns, etc. Then user behavior analysis on these profiles can help in identifying whenever a user behaves out of their normal behavioral profile which can be used to alert the cybersecurity team that something is out of the ordinary.

3. Prevention of Phishing Attacks

Artificial Intelligence can also be extremely helpful in preventing phishing attacks on users in a particular network. These phishing attacks are extremely common in many companies where the employees are sent fraud Emails with the intention of obtaining their company sensitive information such as company passwords, their banking and credit card details, etc. Artificial Intelligence methods such as Natural Language Processing can be used to monitor the employee Emails in their company account and see if there is anything suspicious such as some patterns and phrases that may indicate that the Email is a phishing attempt. This is an extremely important application of AI as phishing attempts are extremely common, so much so that every 1 in 99 Emails might be a phishing attempt.

4. Antivirus Software

Traditional antivirus software may not be able to keep up with all the changes to viruses, especially AI-enhanced viruses that are cropping up these days. This is because antivirus software protects the system by scanning all the files on the network to identify if they might actually match with a known virus or malware signature. But this is very difficult to do if the known viruses keep changing and evolving continuously. In that situation, it is best to use Antivirus software that is integrated with artificial intelligence. This type of antivirus detects viruses in the system not by checking if they match with a known virus or malware signature but by identifying their abnormal behavior that is outside the current window of normality for the network. AI-based antivirus systems can do this by leveraging mathematical AI algorithms and data science as well.

5. Password Protection

Password protection is an integral part of cybersecurity. After all, many times passwords are the only things standing between a hacker and complete access to the account. However, sometimes passwords are not adequate protection for companies. There may be employees that have very simple passwords on their accounts or even have the same password for different type accounts. So hacking those accounts is a child’s play for hackers. In such situations, Artificial Intelligence can be used to provide much better password protection by using features such as facial recognition for opening the accounts. The facial recognition technology can use infra-red sensors and neural networks to map out the distinct patterns on each individual face so that only they are able to open their account and no one else. This advanced AI facial recognition technology can also account for changes such as different hairstyles, wearing a hat, etc. so that no one can fool the system and enter the account illegally.

Is Artificial Intelligence a solution to all Cybersecurity problems?

As already stated, Artificial Intelligence is a double-edged sword. While it has provided many new functionalities in cybersecurity, it has also provided many new methodologies for cyber threats to hackers. So artificial intelligence can immensely boost cybersecurity but it can also become the biggest threat to cybersecurity in the future.

Having said that, Artificial Intelligence in Cybersecurity is still relatively new. There are many more possible applications of AI in cybersecurity in the future than just those discussed here. So companies can work even more deeply with their cybersecurity specialists using artificial intelligence to obtain the best solutions in identifying and managing new and different types of cyber-attacks. And only time will tell if artificial intelligence will take cybersecurity to new heights or become a tool of destruction in the hands of hackers and cyber-terrorists.

Source: geeksforgeeks.org

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The Impact of AI on Cybersecurity

Experts believe that Artificial Intelligence (AI) and Machine Learning (ML) have both negative and positive effects on cybersecurity. AI algorithms use training data to learn how to respond to different situations. They learn by copying and adding additional information as they go along. This article reviews the positive and the negative impacts of AI on cybersecurity.

Main Challenges Cybersecurity Faces Today

Attacks are becoming more and more dangerous despite the advancements in cybersecurity. The main challenges of cybersecurity include:

◉ Geographically-distant IT systems—geographical distance makes manual tracking of incidents more difficult. Cybersecurity experts need to overcome differences in infrastructure to successfully monitor incidents across regions.

◉ Manual threat hunting—can be expensive and time-consuming, resulting in more unnoticed attacks.

◉ Reactive nature of cybersecurity—companies can resolve problems only after they have already happened. Predicting threats before they occur is a great challenge for security experts.

◉ Hackers often hide and change their IP addresses—hackers use different programs like Virtual Private Networks (VPN), Proxy servers, Tor browsers, and more. These programs help hackers stay anonymous and undetected.

AI and Cybersecurity

Cybersecurity is one of the multiple uses of artificial intelligence. A report by Norton showed that the global cost of typical data breach recovery is $3.86 million. The report also indicates that companies need 196 days on average to recover from any data breach. For this reason, organizations should invest more in AI to avoid waste of time and financial losses and.

AI, machine learning, and threat intelligence can recognize patterns in data to enable security systems learn from past experience. In addition, AI and machine learning enable companies to reduce incident response times and comply with security best practices.

How AI Improves Cybersecurity

Threat hunting

Traditional security techniques use signatures or indicators of compromise to identify threats. This technique might work well for previously encountered threats, but they are not effective for threats that have not been discovered yet.

Signature-based techniques can detect about 90% of threats. Replacing traditional techniques with AI can increase the detection rates up to 95%, but you will get an explosion of false positives. The best solution would be to combine both traditional methods and AI. This can result in 100% detection rate and minimize false positives.

Companies can also use AI to enhance the threat hunting process by integrating behavioral analysis. For example, you can leverage AI models to develop profiles of every application within an organization’s network by processing high volumes of endpoint data.

Vulnerability management

20,362 new vulnerabilities were reported in 2019, up 17.8% compared to 2018. Organizations are struggling to prioritize and manage the large amount of new vulnerabilities they encounter on a daily basis. Traditional vulnerability management methods tend to wait for hackers to exploit high-risk vulnerabilities before neutralizing them.

While traditional vulnerability databases are critical to manage and contain known vulnerabilities, AI and machine learning techniques like User and Event Behavioral Analytics (UEBA) can analyze baseline behavior of user accounts, endpoint and servers, and identify anomalous behavior that might signal a zero-day unknown attack. This can help protect organizations even before vulnerabilities are officially reported and patched.

Data centers

AI can optimize and monitor many essential data center processes like backup power, cooling filters, power consumption, internal temperatures, and bandwidth usage. The calculative powers and continuous monitoring capabilities of AI provide insights into what values would improve the effectiveness and security of hardware and infrastructure.

In addition, AI can reduce the cost of hardware maintenance by alerting on when you have to fix the equipment. These alerts enable you to repair your equipment before it breaks in a more severe manner. In fact, Google reported a 40 percent reduction in cooling costs at their facility and a 15 percent reduction in power consumption after implementing AI technology within data centers in 2016

Network security

Traditional network security has two time-intensive aspects, creating security policies and understanding the network topography of an organization.

◉ Policies—security policies identify which network connections are legitimate and which you should further inspect for malicious behavior. You can use these policies to effectively enforce a zero-trust model. The real challenge lies in creating and maintaining the policies given the large amount of networks.

◉ Topography—most organizations don’t have the exact naming conventions for applications and workloads. As a result, security teams have to spend a lot of time determining what set of workloads belong to a given application.

Companies can leverage AI to improve network security by learning network traffic patterns and recommending both functional grouping of workloads and security policy.

Drawbacks and Limitations of Using AI for Cybersecurity

There are also some limitations that prevent AI from becoming a mainstream security tool:

◉ Resources—companies need to invest a lot of time and money in resources like computing power, memory, and data to build and maintain AI systems.

◉ Data sets—AI models are trained with learning data sets. Security teams need to get their hands on many different data sets of malicious codes, malware codes, and anomalies. Some companies just don’t have the resources and time to obtain all of these accurate data sets.

◉ Hackers also use AI—attackers test and improve their malware to make it resistant to AI-based security tools. Hackers learn from existing AI tools to develop more advanced attacks and attack traditional security systems or even AI-boosted systems.

◉ Neural fuzzing—fuzzing is the process of testing large amounts of random input data within software to identify its vulnerabilities. Neural fuzzing leverages AI to quickly test large amounts of random inputs. However, fuzzing has also a constructive side. Hackers can learn about the weaknesses of a target system by gathering information with the power of neural networks. Microsoft developed a method to apply this approach to improve their software, resulting in more secure code that is harder to exploit.

Source: computer.org

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Machine Learning – Types of Artificial Intelligence

The word Artificial Intelligence comprises two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non-natural thing and Intelligence means the ability to understand or think. AI is not a system but it is implemented in the system.

There can be so many definitions of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.” Therefore It is an intelligence where we want to add all the capabilities to machines that humans contain. Artificial Intelligence can be classified into two types:

1. Based on the Capabilities of AI. 

◉ Artificial narrow Intelligence.

◉ Artificial General Intelligence.

◉ Artificial Super Intelligence.

2. Based on Functionality of AI.  

◉ Reactive machines.

◉ Limited memory.

◉ Theory of mind.

◉ Self-awareness.

Let’s discuss all of them one by one. 

Based on the Capabilities of AI

1. Artificial Narrow Intelligence: ANI also called “Weak” AI is that the AI that exists in our world today. Narrow AI is AI that programmed to perform one task whether it’s checking the weather, having the ability to play chess, or analyzing data to write down the journalistic report. It can attend a task in real-time, but they pull information from a selected perform outside of the only task that they’re designed to perform.ANI system can attend to a task in the period however they pull info from a specific data set. These systems don’t perform outside of the sole task that they’re designed to perform. 

2. Artificial General Intelligence: AGN also called strong AI it refers to machines that exhibit human intelligence. we will say that AGI can successfully perform any intellectual; a task that a person’s being can. this is often the type of AI that we see in movies like “Her” or other sci-fi movies during which humans interact with machines and OS that are conscious, sentiment, and driven by emotional and self-awareness. It is expected to be ready to reason, solve problems, make judgments under uncertainty in decision-making and artistic, imaginative.but for machines to realize true human-like intelligence. 

3. Artificial Super Intelligence: ASI will be human intelligence in all aspects from creativity, to general wisdom, to problem-solving. Machines are going to be capable of exhibiting intelligence that we have a tendency to haven’t seen within the brightest amongst. This is the kind of AI that a lot of individuals square measure upset concerning, and also the form of AI that individuals like Elon musk assume can cause the extinction of the human race.

Based on Functionality of AI

1. Reactive Machines: Reactive machines created by IBM in the mid-1980s.These machines are the foremost basic sort of AI system. this suggests that they can’t form memories or use past experiences to influence present -made a choice, they will only react to currently existing situations hence “Reactive”. An existing sort of a reactive machine is deep blue, chess playing by the supercomputer. 

2. Limited Memory: It is comprised of machine learning models that device derives knowledge from previously-learned information, stored data, or events. Unlike reactive machines, limited memory learns from the past by observing actions or data fed to them to create experiential knowledge. 

3. Theory of Mind: In this sort of AI decision-making ability adequate to the extent of the human mind, but by machines. while some machines currently exhibit humanlike capabilities like voice assistants, as an example, none are fully capable of holding conversations relative to human standards. One component of human conversation has the emotional capacity or sounding and behaving sort of a person would in standard conversations of conversation. 

4. Self-Awareness: This AI involves machines that have human-level consciousness. this type of AI isn’t currently alive but would be considered the foremost advanced sort of AI known to man.

Source: geeksforgeeks.org

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What Are The Ethical Problems in Artificial Intelligence?

Artificial Intelligence is a new revolution in the technology industry. But nobody knows exactly how it is going to develop! Some people believe that AI needs to be controlled and monitored otherwise robots may take over the world in the future! Other people think that AI will improve the quality of life for humans and maybe make them an even more advanced species. But who knows what will actually happen until it happens!!!

Currently, tech giants such as Google, Microsoft, Amazon, Facebook, IBM, etc. are all trying to develop cutting-edge AI technology. But this means that the Ethical Problems in Artificial Intelligence also need to be discussed. What are the dangers associated with developing AI? What should be their role in society? What sort of responsibilities should be given to them and what if they make mistakes? All of these questions (and more!) need to be addressed by companies before investing heavily in AI research. So now, let’s see some of these Ethical Problems that need to be dealt with in the world of Artificial Intelligence.

1. How to Remove Artificial Intelligence Bias?

It is an unfortunate fact that human beings are sometimes biased against other religions, genders, nationalities, etc. And this bias may unconsciously also enter into the Artificial Intelligence Systems that are developed by human beings. The bias may also creep into the systems because of the flawed data that is generated by human beings. For example, Amazon recently found out that their Machine Learning based recruiting algorithm was biased against women. This algorithm was based on the number of resumes submitted over the past 10 years and the candidates hired. And since most of the candidates were men, so the algorithm also favored men over women.

So the question is “How to tackle this Bias?” How to make sure that Artificial Intelligence is not racist or sexist like some humans in this world. Well, it is important that AI researchers specifically try to remove bias while developing and training the AI systems and selecting the data. There are many companies that are working towards creating unbiased AI systems such as IBM Research. IBM scientists have also created an independent bias rating system to calculate the fairness of an AI system so that the disasters given above can be avoided in the future.

2. What rights should be provided to Robots? And to what extent?

Robots are currently just machines. But what about when Artificial Intelligence becomes more advanced? There may come a time when robots not only look like human beings but may also have advanced intelligence. Then what rights should be given to robots? If robots become advanced enough emotionally, should they be given equal rights like humans or lesser rights? And what if robots kill someone. Should it be considered murder or a machine malfunction? All these are ethical questions that need to be answered as Artificial Intelligence becomes and more intelligent.

There is also the question of citizenship. Should robots be given citizenship of the country they are created in? This question was raised quite strongly in 2017 when the humanoid robot Sophia was granted citizenship in Saudi Arabia. While this was considered more of a publicity stunt than actual citizenship, it is still a question that governments may have to take seriously in the future.

3. How to make sure that Artificial Intelligence remains in Human Control?

Currently, human beings are the dominant species on Earth. And this is not because they are the fastest or the strongest species. No, human beings are dominant because of their intelligence. So the critical question is, “What happens when Artificial Intelligence becomes more intelligent than Human Beings?” This is known as “Technological singularity” or the point at which Artificial Intelligence may become more intelligent than humans and so become unstoppable. Humans could not even destroy that intelligence as it may even anticipate all our methods. This would make AI the dominant species on Earth and lead to huge changes in human existence or even human extinction.

However, is “Technological singularity” is even a possibility or just a myth? Ray Kurzweil, Google’s Director of Engineering believes it is very real and may even happen as early as 2045. However, he believes it is nothing to fear and would just lead to an expansion in the intelligence of human beings if they merge with artificial intelligence. Well, whatever the case, it is obvious that humans need to prepare for “Technological singularity” and how to deal with it. (Just in case!)

4. How to handle Human Unemployment because of Artificial Intelligence?

As Artificial Intelligence becomes more and more advanced, it will obviously take over jobs that were once performed by humans. According to a report published by the McKinsey Global Institute, around 800 million jobs could be lost worldwide because of automation by 2030. But then the question arises “What about the humans that are left unemployed because of this?” Well, some people believe that many jobs will also be created because of Artificial Intelligence and that may balance the scales a bit. People could move from physical and repetitive jobs to jobs that actually require creative and strategic thinking. And people could also get more time to spend with their friends and family with less physically demanding jobs.

But this is more likely to happen to people who are already educated and fall in the richer bracket. This might increase the gap between the rich and poor even further. If robots are employed in the workforce, this means that they don’t need to be paid like human employees. So the owners of AI-driven companies will make all the profits and get richer while the humans who were replaced will get even poorer. So a new societal setup will have to be generated so that all human beings are able to earn money even in this scenario.

5. How to Handle Mistakes made by Artificial Intelligence?

Artificial Intelligence may evolve into a super-intelligence in a few years but right now it is basic! And so it makes mistakes. For example, IBM Watson partnered with Texas MD Anderson Cancer Center to detect and eventually finish cancer in patients. But this AI system failed horribly as it gave totally wrong medicine suggestions to patients. In another failure, Microsoft developed an AI chatbot that was released on Twitter. But this chatbot soon learned Nazi propaganda and racist insults from other Twitter users and it was soon destroyed. And who knows, it may make even complicated mistakes in the future. And these were relatively safe failures that were easily handled. Who knows, Artificial Intelligence may make even more complicated mistakes in the future. Then what is to be done?

The question is about relativity. Do Artificial Intelligence systems make lesser or more mistakes than humans? Do their mistakes lead to actually lose of life or just embarrassment for companies like in the above cases? And if there is a loss of life, is it more or less than when humans make mistakes? All of these questions need to be taken into account when developing AI systems for different applications so that their mistakes are bearable and not catastrophic!

Source: geeksforgeeks.org

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5 Mistakes to Avoid While Learning Artificial Intelligence

Artificial Intelligence imitates reasoning, learning, and perception of human intelligence towards the simple or complex tasks performed. Such intelligence is seen in industries like healthcare, finance, manufacturing, logistics, and many other sectors. But there is a thing common – mistakes while using the AI concepts. Making mistakes is quite generic and one can’t hide himself/herself from the consequences. So, instead of paying attention to its repercussions, we need to understand the reason why such mistakes may occur and then, modify the practices we usually perform in real-time scenarios.

Let’s spare some time in knowing about the mistakes we must be avoiding while getting started with learning Artificial Intelligence:

1. Starting Your AI Journey Directly with Deep Learning

Deep Learning is a subpart of Artificial Intelligence whose algorithms are inspired by the function, structure of our brain. Are you trying to link our brain’s structure and its functioning with neural networks? Yes, you can (in the context of AI) because there are neurons present in our brains that collect signals and split them into structures residing in the brain. This lets our brain understand what the task is and how it must be done. Now, you may try to begin your AI journey with Deep Learning (or DL) directly after knowing a bit about neural networks!!  

No doubt there will be a lot of fun, but the point is that it’s better not to introduce DL initially because it fails to achieve higher performance while working with smaller datasets. Also, practicing DL isn’t only harder but expensive too, as the resources and computing power required for creating and monitoring DL algorithms are available at higher costs, thereby creating overheads while managing the expenses. Even at times when you try to begin interpreting the network designs and hyper-parameters involved with DL Algorithms, you feel like banging your heads because it is quite difficult to interpret the exact interpretation of the sequence of actions that a DL Algorithm wants to convey. All such challenges will come amidst the path of your AI journey and thus, it is beneficial not to introduce Deep Learning directly.          

2. Making Use of an Influenced AI Model

An Influenced AI model will always be biased in an unfair manner as the data garnered by it will be inclined towards the existing prejudices of reality. Such an inclination won’t let the artificially intelligent algorithms identify the relevant features which reciprocate better analysis and decision-making for real-life scenarios. As a result, the datasets (trained or untrained) will map unfair patterns and never adopt egalitarian perspectives somewhere supporting fairness and loyalty in the decision-making process followed by AI-based systems.  

To understand the negative impact of an influenced AI Model, we may take a look at the COMPAS case study. COMPAS is an AI-influenced tool whose full form is Correctional Offender Management Profiling for Alternative Sanctions. It is used by the US courts for predicting if or not the defendant may become a recidivist (criminal reoffending different sorts of crimes). When this tool examined the data, the results were really shocking. It predicted false recidivism by concluding that 45 percent of black defendants were recidivists, while 23 percent of white defendants were classified as recidivists. This case study questioned the overall accuracy of the AI model used by the tool and clearly describes how such bias invites race discrimination amongst the people of the United States. Henceforth, it is better not to use a biased AI model as it may worsen the current situation by creating an array of errors in the process of making impactful decisions.

3. Trying to Fit Accuracy of AI Algorithms with Every Biz. Domain

Every biz. (business) domain won’t try to fit accuracy in every of its ongoing or forthcoming AI processes either related to software development or customer service. This is because there are other traits business ventures consider, like robustness, flexibility, innovation, and so on. Still thinking what the reason could be!! The answer is – Accuracy is foremost, but interpretability has its own potential!  

For instance, clients responsible for generating good revenue for business ventures check accuracy at a limit of say 90 percent, but they also check the robustness and flexibility of the AI algorithms while understanding the current business problem and then, predicting the outcomes much closer to their actual values. If the algorithms fail to factorize problems and do not realize the importance of data interpretation at times they are predicting the conclusions, clients straightaway reject such analysis. Here, what they are actually looking for is that AI algorithms are interpreting the input datasets well and showcasing robustness and flexibility in evaluating the decision-matrix suitably. Henceforth, you prefer not to fit accuracy with every domain generating visibility for businesses in the current or futuristic times.

4. Wasting Time in Mugging Up the AI Concepts  

Mugging up the AI concepts won’t let you acquire a deeper understanding of the AI algorithms. This is because those theoretical concepts are bound to certain conditions and won’t reciprocate the same explanation in real-time situations. For example, when you enroll yourself for a course, say Data Science course, there are various terminologies embedded in the curriculum. But do they behave the same when applied to real-time scenarios?  

Of course not! Their results vary because the terminologies when exposed to situations are affected by various factors whose results one can only understand after being involved in how these practical techniques fit well into a larger context and the way they work. So, if you keep mugging up the AI concepts, it would be difficult to remain connected with its practical meaning for a longer period. Consequently, solving the existing real-world problem will become challenging and this will negatively impact your decision-making process.  

5. Trying to Snap Up all Swiftly

Snapping up swiftly here means hurrying up learning a maximum number of AI concepts practically and trying to create AI models (consisting of different characteristics) in a shorter span. Such a hurry won’t be advantageous. Rather, this will be forcing you to jump to conclusions without validating the current datasets modeled for understanding the business requirements well. Besides, such a strategy will be landing your minds in utter confusion and you will be having more problems, instead of solutions, in your pocket.  

We may understand this through a real-life example. Suppose you are in the kitchen and preparing your meal. Now, your brother enters and asks you to prepare snacks within 20 minutes. Thinking if I am trapped or confused!! Yes, you will face endless confusion in deciding if you should be preparing your meal or the snacks for your brother. As a result, this will impact your accuracy of preparing quality meals/snacks because now, you have a time-boundation of 20 minutes. Such a situation occurs when one tries to snap up all the terminologies and notions embedded within an AI-based system/model. Therefore, instead of trying to grab everything quickly, you need to follow the SLOW AND STEADY principle. It will be helping you solve the existing AI challenge by selecting appropriately validated datasets not bound to non-accurate results.

Source: geeksforgeeks.org

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