Detect adversarial input

Establishing a taxonomy for adversarial risks. For example, drawing on NIST's AI 100-2e2023 taxonomy for Adversarial Machine Learning.

Establishing detection and alerting. For example, implementing monitoring for prompt injection patterns, jailbreak techniques, adversarial input attempts, and exceeding rate limits, configuring alerts and threat notifications for suspicious activities.

Implementing incident logging and response procedures. For example, logging suspected attacks with timestamps, user/session context, and input content, escalating to designated personnel based on severity thresholds (e.g. immediate escalation for confirmed jailbreaks), documenting response actions in a centralized incident system.

Maintaining detection effectiveness through quarterly reviews. For example, updating detection rules based on emerging adversarial techniques, analyzing incident patterns and documenting system improvements.

Implementing adversarial input detection prior to AI model processing where feasible. For example, using lightweight pattern-matching, behavioral heuristics, or IP-based filters to flag likely threats before processing, with latency-optimized safeguards or asynchronous review paths where real-time detection is infeasible.

Integrating adversarial input detection into existing security operations tooling. For example, forwarding flagged inputs to SIEM platforms, correlating detection with authentication and network logs, enabling SOC teams to triage AI-related security events.