Monitoring and Maintenance AI Mitigations

Implement logging of inputs and outputs of deployed AI models. When deploying AI agents, implement logging of the intermediate steps of agentic actions and decisions, data access and tool use, installation commands, and identity of the agent. Monitoring logs can help to detect security threats and mitigate impacts.

Additionally, having logging enabled can discourage adversaries who want to remain undetected from utilizing AI resources.

Detect and block adversarial inputs or atypical queries that deviate from known benign behavior, exhibit behavior patterns observed in previous attacks or that come from potentially malicious IPs. Incorporate adversarial detection algorithms into the AI system prior to the AI model.

Require users to verify their identities before accessing a production model. Require authentication for API endpoints and monitor production model queries to ensure compliance with usage policies and to prevent model misuse.

Apply deepfake detection algorithms against any untrusted or user-provided data, especially in impactful applications such as biometric verification, to block generated content.

Detectors may use a combination of approaches, including:

• AI models trained to differentiate between real and deepfake content.
• Identifying common inconsistencies in deepfake content, such as unnatural facial movements, audio mismatches, or pixel-level artifacts.
• Biometrics analysis, such blinking, eye movements, and microexpressions.