- Track 1: Network Security: Intrusion detection, anomaly detection, malware analysis, AI-driven threat intelligence, secure network protocols, cloud/edge/IoT security, adversarial traffic analysis, AI-assisted red-team operations.
- Track 2: Information Security: AI-based data protection, cryptography and key management, IAM, digital forensics, privacy-preserving ML, secure data pipelines, risk modelling, automated incident response.
- Track 3: Software Security: Vulnerability detection using AI, automated code review, secure DevOps, secure SDLC, fuzzing, static/dynamic analysis, supply-chain security, GenAI security risks in coding tools.
- Track 4: Security of AI: Adversarial ML, model extraction/inversion, data poisoning, watermarking, secure training pipelines, LLM robustness, federated learning security, trustworthy AI deployment.
- Track 5: AI Ethics and Security Compliance: Responsible AI, explainability, bias and fairness audits, governance frameworks, regulatory compliance (EU AI Act, IndiaAI), societal impacts, accountability mechanisms
