AI and Cyber Security

by Dhanya Menon | Dec 9, 2024 | Cyber Security | 0 comments

AI and cyber security are interconnected, making data all the more relevant and important.

Challenges and Limitations for AI Functioning

1. Data Quality: AI requires high-quality, relevant data to learn and improve.
2. Model Drift: AI models can become outdated, reducing effectiveness.
3. Explainability: AI decisions may be difficult to interpret, making it challenging to understand detection logic.
4. Adversarial Attacks: Attackers can manipulate AI systems using adversarial tactics.
5. Bias and Fairness: AI systems can perpetuate existing biases if not designed with fairness in mind.

Cybersecurity Threats to AI Systems

1. Data Poisoning: Manipulating training data to compromise AI model integrity.
2. Model Inversion: Reverse-engineering AI models to extract sensitive information.
3. Adversarial Attacks: Crafting inputs to mislead or deceive AI systems.
4. AI Model Theft: Stealing or exploiting AI models for malicious purposes.
5. AI System Compromise: Exploiting vulnerabilities in AI systems to gain unauthorized access.

Types of Attacks

1. Evasion Attacks: Manipulating inputs to evade detection or classification.
2. Poisoning Attacks: Contaminating training data to compromise model integrity.
3. Replay Attacks: Reusing previously recorded data to deceive AI systems.
4. Impersonation Attacks: Mimicking legitimate users or systems to gain unauthorized access.
5. Data Manipulation Attacks: Altering data to influence AI decision-making.

Regulations and Guidelines

1. GDPR: EU’s General Data Protection Regulation.
2. AI Ethics Guidelines: EU’s guidelines for trustworthy AI.
3. Fairness, Accountability, and Transparency (FAT): Principles for AI development.
4. IEEE Global Initiative on Ethics in Action: AI ethics standards.
5. ACM Fairness, Accountability, and Transparency (FAT): Conference series.

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