Agentic AI systems retrieve data, call tools, and take real actions — with your credentials, on your behalf. AISecOps is the runtime control plane for governing them: local enforcement, capability-gated execution, execution governance, structured audit logging, and deterministic execution boundaries.
Enterprises are deploying AI agents that browse the web, read email, query databases, and execute code. Traditional application security was not designed for autonomous execution systems.
Your AI agent calls a tool. You see a log entry. There's no policy engine — the call either succeeds or fails at the API level. You have no visibility into what the model was instructed to do, why it chose that tool, or whether the retrieved context was clean.
Every execution plan passes through a runtime control plane. Retrieved context is sanitized before the model sees it. Capability gates validate requested actions before policy evaluation. Every runtime decision emits a structured audit event. Replayable JSONL logs enable explainability, forensics, and governance.
Indirect prompt injection via RAG. Tool parameter manipulation. Memory context poisoning. Policy drift as models and prompts evolve. These are not theoretical — they have been demonstrated in production agentic systems.
A layered runtime governance architecture: local enforcement, context validation, capability containment, execution governance, deterministic execution boundaries, and replayable observability. Open-source reference implementations. Enterprise adoption guidance. Threat models aligned to OWASP LLM risks.
Securing an agentic AI system requires runtime governance across every transition boundary — from input to planning, evaluation, execution, and audit.
Optional local / edge guards stop obvious injection patterns before cloud model invocation. Validate and sanitize all external data before it enters the model's context window. Treat every retrieved document, memory chunk, and tool response as untrusted input that must be inspected for injection patterns.
Enforce capability-gated execution and parameter validation before any external call is executed. The policy engine — not the model — decides what actions are permitted. Deny by default.
Separate planning, evaluation, and execution into distinct runtime boundaries. Deterministic executors run only approved or allowed execution plans. High-risk actions require approval workflows and emit explainable audit trails.
Emit structured JSONL runtime events at every decision point. Support replay, explainability, forensic reconstruction, governance analytics, and policy drift analysis across deployments.
Framework documentation, threat models, reference architecture, and working open-source code. No account required.
The disambiguation page — how AISecOps for agentic AI differs from legacy "AI for SecOps" definitions.
Read the definition →MCP, A2A, swarm systems — a structured threat model covering all major agentic AI attack vectors with OWASP LLM mapping.
View threat model →A runtime control plane blueprint: local enforcement, capability gates, execution governance, deterministic execution boundaries, and replayable audit architecture.
View architecture →AISecOps v0.5 replay foundation for reconstructing agent execution history, instruction provenance, policy outcomes, and final runtime decisions from JSONL audit evidence.
View runtime forensics →AISecOps v0.2 framework document covering runtime control planes, execution splitting, capability-gated execution, replayable audit logging, and enterprise adoption guidance.
Download →Reference implementation: runtime control plane, execution splitting, capability-gated evaluation, optional local enforcement, structured JSONL audit logging, and CI security gates.
View on GitHub →Enterprise adapter for routing OpenClaw execution plans through the AISecOps runtime control plane with capability validation, policy evaluation, and structured audit events.
View on GitHub →