AISecOps · aisecops.net

The Runtime Governance Platform
for Agentic AI.

AISecOps Interceptor governs agentic AI across Security, Compliance, Cost Control, and Observability. It adds plan extraction, capability validation, policy enforcement, runtime budgets, agent identity, replay diff, and compliance evidence export to the execution path.

Explore Runtime Forensics → Read the Framework
Current OSS release: v1.0.0 · Replay Diff Engine + Evidence Export
aisecops-runtime · replay.trace
// Local enforcement mode - precheck
{
  "event": "agent_identity_verified",
  "severity": "high",
  "action": "identity_attached",
  "agent_id": "agent-finance-01"
}

// Runtime governance - policy enforcement and budgets
{
  "plan": "extract-revenue-summary",
  "capability": "cap_finance_read",
  "policy": "allow_with_budget",
  "runtime_budget": "tracked"
}

// Runtime controls - execution, audit, replay diff, evidence
aisecops_runtime_governance_total{decision="allow"} 1
jsonl persisted -> replay diff + evidence export

$ _
Framework covers
Security Compliance Cost Control Observability Replay Diff Evidence Export Agent Identity MCP Policy Proxy
The Problem

Agentic AI changed the operating model.
Most teams have not caught up.

Enterprises are deploying AI agents that browse the web, read email, query databases, and execute code. Traditional application security was not designed for governed autonomous execution systems with identity, policy, evidence, and cost controls.

Before AISecOps

Your AI agent calls a tool. You see a log entry. There is no identity layer, policy engine, evidence package, or replay diff to explain what changed, why the action was allowed, or whether it stayed within budget.

With AISecOps

Every execution plan passes through a runtime governance platform. Agent identity, capability gates, policy enforcement, runtime budgets, replay diff, and compliance evidence export are all part of the same path. Structured audit logs enable explainability, forensics, and governance.

What attackers exploit

Indirect prompt injection via RAG. Tool parameter manipulation. Missing identity. Missing evidence export. Uncontrolled runtime cost. Policy drift as models and prompts evolve. These are not theoretical and have been demonstrated in production agentic systems.

What this framework provides

A layered runtime governance architecture: plan extraction, local enforcement mode, agent identity, MCP policy proxy, policy enforcement, runtime budgets, replay diff, evidence export, and replayable observability. Open-source reference implementations, enterprise adoption guidance, and threat models aligned to OWASP LLM risks.

Runtime Governance Model

Four pillars for governed agentic AI.

AISecOps governs the runtime path from plan extraction to evidence export with security, compliance, cost control, and observability as first-class operating concerns.

L1 Security

Local enforcement mode, agent identity, capability validation, policy enforcement, and MCP policy proxy controls keep execution inside declared runtime boundaries.

L2 Compliance

Compliance evidence export, structured audit, replay diff review, and explicit governance results create an auditable record for regulated workflows.

L3 Cost Control

Runtime budgets, bounded agent activity, and policy-aware execution tracking keep agent behavior within spending limits.

L4 Observability

Replay audit UI, execution graphs, replay diff, and risk explanation turn runtime events into operational visibility.

View the Reference Architecture →
Runtime Investigation

Replayable runtime forensics for governed agents.

AISecOps reconstructs execution history from structured runtime events: structured plan extraction, capability validation, policy enforcement, runtime budgets, runtime controls, execution, audit, replay diff, and final governance outcomes.

Agent
→
Plan
→
Capability
→
Policy
→
Runtime Controls
→
Execution
→
Audit
→
Replay Diff
→
Evidence
Replay Trace List

Investigate runtime decisions across traces.

Replay summaries expose runtime decisions, provenance trust levels, event counts, and execution outcomes for forensic investigation.

AISecOps replay trace list
Timeline Reconstruction

Replay execution history step-by-step.

Structured replay timelines reconstruct planning, evaluation, approval, execution, and final governance decisions in execution order.

AISecOps replay timeline
Execution Graphs

Visualize provenance-aware execution flow.

Execution graphs reconstruct causal runtime relationships between provenance sources, planning stages, policy enforcement, approvals, tool execution, and final outcomes.

AISecOps execution graph
Explore Runtime Forensics →
Featured Article

I Built a Runtime Forensics Layer for AI Agents

A deep dive into AISecOps Interceptor, runtime governance, replay diff, evidence export, execution graphs, and why AI agents need a governance layer once they start taking actions.

Read on Medium →
AISecOps runtime forensics execution graph
Resources

Everything is open and free.

Framework documentation, threat models, reference architecture, and working open-source code. No account required.

Definition
What Is AISecOps?

The disambiguation page — how AISecOps for agentic AI differs from legacy "AI for SecOps" definitions.

Read the definition →
Threat Model
Agentic AI Attack Surface

MCP, A2A, swarm systems — a structured threat model covering all major agentic AI attack vectors with OWASP LLM mapping.

View threat model →
Architecture
Reference Architecture

A runtime governance blueprint: local enforcement mode, agent identity, capability validation, policy enforcement, runtime budgets, replay diff, and evidence export.

View architecture →
Runtime Forensics
Replayable Audit and Provenance

Runtime investigation workflows for agent identity, replay diff, compliance evidence export, timeline reconstruction, execution graph analysis, and forensic review of AI agent decisions.

View runtime forensics →
Whitepaper
The AISecOps Framework

Framework document covering AISecOps v1.0 foundations, runtime governance, replay diff, evidence export, capability-gated execution, and enterprise adoption guidance.

Download →
Open Source
aisecops-interceptor

Reference implementation with Runtime Governance APIs, Replay Diff Engine, Agent Identity Layer, Replay Audit UI, and execution graph investigation.

View on GitHub →