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 Model Drift: AI models can become outdated, reducing effectiveness.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.