AI and Cyber Security

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1.Data Quality: AI requires high-quality, relevant data to learn and improve. 2.Model Drift: AI models can become outdated, reducing effectiveness. 3.Explainability: AI decisions may be difficult to interpret, making it challenging to understand detection logic. 4.Adversarial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.