AI Governance Tools
What each category of AI governance tooling actually does, organized by the control domain it serves, so you can evaluate a tool against your actual gaps, not a vendor's feature list.
Start from your gaps, not the tool market
The AI governance tooling market is fragmented across point solutions for monitoring, logging, red-teaming, and compliance mapping, plus a smaller set of platforms that bundle several of these. Before evaluating any tool, run a risk classification of your AI systems and identify which control domains have the largest gap between what's documented and what's actually enforced. That gap, not a vendor comparison, should drive the buying decision.
Inventory and risk classification
What you need it to do: A system of record for every AI deployment in use, with a risk tier assigned to each one.
What to look for: Discovery methods beyond a manual spreadsheet: spotting employees' connections to AI services on the company network, scanning vendor contracts for AI features, and a workflow for assigning and reviewing risk tiers, not just a static list.
Model monitoring and drift detection
What you need it to do: Ongoing visibility into whether a deployed model's behavior or performance has degraded since it was approved.
What to look for: Automatic alerts when a model's accuracy slips over time (drift) or its behavior turns unusual, with thresholds you can set, not just periodic manual review. Confirm the tool can monitor models you don't host yourself, since most organizations run on top of a vendor's model.
Audit logging and documentation
What you need it to do: An immutable, retrievable record of AI decisions, inputs, outputs, and model versions that can be produced on demand for an auditor or regulator.
What to look for: Tamper-evident logging (not just logging), a defined retention policy, and a way to pull records fast enough to meet the deadlines auditors and regulators set. A log you can't search or export quickly is a liability, not evidence.
Model risk management platforms
What you need it to do: A centralized model inventory with lifecycle traceability, most relevant to regulated industries already operating under model risk frameworks like SR 11-7, the US banking regulators' model risk guidance.
What to look for: Whether the platform extends existing model risk infrastructure to generative and agentic AI, or requires a parallel system. A real deployment at a Fortune 500 bank centralized model inventories and automated lifecycle traceability rather than adding a second tracking system.
Agentic AI governance and containment
What you need it to do: Permission boundaries, credential scoping, and kill-switch controls for AI agents that take autonomous action.
What to look for: A hard technical wall between what an agent reads (emails, web pages, documents that anyone could have written) and the actions it is allowed to take. Telling an agent in its instructions to behave is not a containment control.
Regulatory and compliance mapping
What you need it to do: A current mapping of which regulations apply to which AI systems across jurisdictions.
What to look for: Update frequency and sourcing. A compliance-mapping tool is only as good as how quickly it reflects new enforcement actions and legislation, which is why we track this as a daily feed rather than a static database.
Red-teaming and adversarial testing
What you need it to do: Structured, repeatable testing of how an AI system holds up when people try to trick it into ignoring its rules (jailbreaks), smuggle hidden instructions into what it reads (prompt injection), or coax out unsafe output.
What to look for: Whether the tool produces a structured, audit-ready report of which attacks worked and where the system is exposed, not just a pass/fail score. Our own free, open-source developer tool includes a red-teaming test your engineers can run while they build.
Free, open-source tooling for developers
We publish an open-source AI Governance MCP server with three governance controls your engineers can run from AI coding assistants such as Claude Code. MCP (Model Context Protocol) is the standard those assistants use to plug in outside tools. The three checks are output safety screening, AI risk classification, and red-teaming, which means attacking the system's instructions the way a bad actor would. It won't replace an enterprise monitoring platform, but it catches governance gaps at the point of development rather than after deployment, and it's free.