How do I filter and sanitize AI agent outputs before they reach users or downstream systems?

To filter and sanitize AI agent outputs, implement validation gates, re-verify intent, and apply dynamic intervention strategies before outputs reach users or downstream systems. These measures help prevent malicious or erroneous outputs from causing harm or corrupting further processes.

• Output Schema Validation is the most effective and cheapest runtime check for tool calls, ensuring structured outputs conform to expected formats and interrupting attacks that produce malformed responses. This addresses the OWASP LLM Top 10 risk of LLM07: Insecure Output Handling by validating the structure of the output.
• Intent Re-verification should occur before any consequential action, where the system re-derives whether the action aligns with the originally attested intent, rather than the agent's potentially corrupted current reasoning. This helps mitigate LLM01: Prompt Injection and LLM05: Goal Misalignment by ensuring actions align with intended goals.
• Output Filtering and Content Classification on outgoing data are necessary to prevent agents from including sensitive context content in tool calls or external responses, which could lead to data exfiltration. This directly addresses LLM07: Insecure Output Handling and LLM04: Sensitive Information Disclosure.
• Review Gates and Provenance Checks are crucial for memory outputs, especially from "dreaming" processes, to ensure that only verified and safe information is promoted into trusted memory stores. This helps prevent LLM02: Supply Chain Vulnerabilities and LLM04: Sensitive Information Disclosure by ensuring the integrity and safety of stored data.
• Enforcement Options when verification fails include blocking, redacting, transforming, escalating, or quarantining outputs. This provides a range of responses to detected issues, aligning with the NIST AI RMF function of Protect and Respond.
• Human Oversight and Override (NIST AI RMF function H) should be incorporated, especially for high-risk operations, requiring explicit human approval or a second model invocation with adversarial framing to challenge proposed actions. This provides a critical layer of control and intervention.