AI model security & guardrails for agentic systems

Nebul Guardrails protects system prompts and context by preventing instruction hijacking and reducing exposure to injection attacks. It detects and blocks both direct and indirect prompt injection, separates trusted instructions from untrusted user input, and sanitizes obfuscation attempts such as encoding tricks, whitespace manipulation, or hidden markup. Retrieved content in RAG pipelines is also secured against malicious instructions.

Agentic workflows are enabled safely by applying governance to tools and actions. Guardrails validates every tool call against permissions, session context, and policy, enforces parameter validation and allowlisting for sensitive operations, and applies least-privilege defaults to connectors and APIs. For high-risk actions, human-in-the-loop approval can be required before execution.

Responses are validated to ensure they remain reliable, safe, and consistent across models. Guardrails scans outputs for policy violations, secrets, and sensitive data exposure, applies reliability checks such as format validation and groundedness constraints, and enforces controlled refusal behavior with safe redirection paths. Tone and brand alignment are applied consistently across teams and use cases.

Because security is not a one-time configuration, Guardrails includes observability and continuous hardening by default. It provides audit logs for prompts, decisions, and tool actions, real-time alerts for suspicious behavior, and supports red-team testing workflows and regression checks. These feedback loops enable safe rollouts and ongoing improvement as threats evolve.