AI and cyber security important pointers: 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 Model Drift: AI models can become outdated, reducing effectiveness.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.