Artificial Intelligence and Cybersecurity in quick view
Eduardo Dionisio de Vasconcelos, CISM
Global Head of Information Security | DPO Brazil at NSG Group
This article is not intended to be a guide, nor to indicate the correct path, as there is still a lot to be discussed on this topic, both due to the shroud of uncertainty that remains in the technology used internally by major artificial intelligence tools, as well as the possibilities of reach and influence that it is possible to have over these tools.
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Recent research has identified several dangers associated with poisoning artificial intelligence (AI) systems with threats and malicious codes in the cybersecurity area:
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1.?????? Malfunction of AI Systems: AI systems can malfunction when exposed to untrustworthy data. Attackers are exploiting this issue by injecting malicious samples into the training data to generate vulnerable code. This can lead to serious consequences, such as a driverless car veering into oncoming traffic due to errant markings on the road.
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2.?????? Adversarial Attacks: Adversaries can deliberately confuse or even "poison" AI systems to make them malfunction. These adversarial attacks can be hard to detect and prevent, making them a significant threat to AI systems.
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3.?????? Lack of Foolproof Defense: Currently, there is no foolproof method for protecting AI from misdirection. While there are mitigation strategies reported in the literature, these available defenses currently lack robust assurances that they fully mitigate the risks.
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4.?????? Increased Risks with AI Tools: The risks of artificial intelligence to cybersecurity are expected to increase rapidly with AI tools becoming cheaper and more accessible. For example, AI can be tricked into writing malicious code or creating deepfake audio tracks or video clips with very little training data.
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5.?????? Privacy Concerns: There are also growing privacy concerns as more users grow comfortable sharing sensitive information with AI.
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Protecting AI systems from adversarial attacks is a complex task that requires a multi-faceted approach. Some strategies that can be employed:
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1.?????? Continuous Monitoring and Adaptation: Regularly monitor network activities and system behaviors to detect any unusual patterns or deviations. Implement adaptive cybersecurity measures that can evolve alongside the dynamic tactics employed by AI-powered cyber attacks.
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2.?????? Employee Training and Awareness: Provide comprehensive training programs to educate employees about the evolving nature of AI in cybersecurity. This can help them understand the threats and take appropriate actions when they encounter them.
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3.?????? Advanced Threat Detection Solutions: Use advanced threat detection solutions to identify and mitigate potential threats. These solutions can help in detecting adversarial attacks early and respond to them effectively.
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4.?????? Robust Authentication and Access Controls: Implement robust authentication mechanisms and access controls to prevent unauthorized access to AI systems. This can help in reducing the risk of adversarial attacks.
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5.?????? Incident Response Planning: Have a well-defined incident response plan in place. This can help in quickly responding to adversarial attacks and minimizing their impact.
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6.?????? Developing Robust AI Systems: To combat adversarial attacks, it is crucial to develop robust AI systems that can detect and mitigate such threats. This involves implementing defensive measures which can enhance the resilience of AI systems against adversarial attacks.
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Remember, securing AI and machine learning systems poses significant challenges. Some are not unique to AI, while others are new, including defending against adversarial machine learning. Therefore, a combination of the above strategies can help in protecting AI systems from adversarial attacks.
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While AI has the potential to greatly enhance cybersecurity, it also poses significant risk. It's crucial for AI developers and users to be aware of these threats and take appropriate measures to mitigate them. However, it's important to note that the field of AI security is still evolving, and there is a pressing need for both technical defenses and rigorous data governance protocols.
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NIST Identifies Types of Cyberattacks That Manipulate Behavior of AI in the article NIST Identifies Types of Cyberattacks That Manipulate Behavior of AI Systems | NIST. And also created an Artificial Intelligence Risk Management Framework NIST AIRC - AI RMF.