LLM-Driven Cybersecurity: Risks & Governance
Word Count : 3500
Objectives to cover:
Introduction: LLMs are transforming cybersecurity by assisting in intelligent threat detection and automated defense operations.
Background & Need: Increasing cyber-attacks and sophisticated adversaries demand advanced AI-driven security mechanisms.
Role of LLMs: LLMs analyze logs, detect anomalies, assist in incident response, and support security analysts with actionable intelligence.
Key Benefits: Improved detection accuracy, faster response, reduced manual workload, and enhanced SOC efficiency.
Cyber Risks: Vulnerable to prompt injection, model manipulation, adversarial inputs, and data poisoning attacks.
Data Privacy Challenges: Sensitive logs and user data processed by LLMs require strict privacy and regulatory compliance.
Ethical & Legal Considerations: Ensuring transparency, accountability, fairness, and legal compliance in AI-based cybersecurity systems.
Governance Strategies: Implement strong AI policies, continuous monitoring, access controls, audit trails, and human-in-loop validation.
Conclusion: Secure and ethical deployment with strong governance is essential for reliable and safe LLM-based cybersecurity.