Introduction
AI agent security and governance require comprehensive frameworks addressing technical security, operational governance, and regulatory compliance. This multi-layered approach ensures safe, reliable, and compliant AI deployment.
Security Architecture
Multi-layered security includes: Identity and Access Management (IAM), encrypted data transmission, secure API endpoints, audit logging, sandboxed execution environments, network segmentation, and regular security assessments. Each layer provides defense against different threat vectors.
Governance Framework
Establish AI governance boards, define clear policies for AI decision-making, implement human oversight protocols, create approval workflows for AI changes, maintain model registries, and establish accountability structures. Governance ensures responsible AI use.
Risk Management
Identify AI-specific risks: model bias, adversarial attacks, data poisoning, privacy breaches, and operational failures. Implement risk assessment procedures, mitigation strategies, incident response plans, and continuous monitoring systems.
Compliance Monitoring
Ensure adherence to regulations like GDPR, EU AI Act, industry standards. Implement explainable AI for transparency, maintain data lineage, conduct regular bias testing, and establish compliance reporting mechanisms.
Operational Security
Secure model deployment pipelines, implement version control, establish testing protocols, monitor for anomalies, maintain backup and recovery procedures, and ensure secure model updates. Security must be integrated throughout the AI lifecycle.