What an AI governance platform does

An AI governance platform is the software an enterprise uses to control its AI systems across their full life: deciding what each model and agent is allowed to do, testing it against that envelope, enforcing the rules at runtime, and proving compliance to auditors and regulators.

A real platform covers four operational governance dimensions, not one:

• Inventory. A live register of every model, agent, and AI use case in the estate, with its risk tier, owner, and current control state.

• Policy. The rules each system must follow, defined once and enforced everywhere, not parked in a document nobody reads.

• Monitoring. Continuous observation of live AI behaviour: drift, toxicity, policy breaches, and agent decisions, in real time.

• Audit. Tamper-evident evidence that maps to the standards you answer to, ready before anyone asks.

Point tools cover one or two of these. Disseqt covers all four on a single platform, so the inventory, the policy, the monitoring signal, and the audit trail are the same record rather than four disconnected ones.

The platform, mapped to the three pillars

Disseqt is built as the AI Assurance Lifecycle: three pillars that hand off to each other so AI moves from build to production to proof without leaving the platform.

Test and Detect: find it in private, before someone finds it in public

Before anything ships, you need to know how it breaks. Test and Detect runs 65 ML-based validators across four families (base, RAG, agentic, and MCP), 84 jailbreak techniques covering single and multi-turn attacks, and three guided testing agents.

A Live Vulnerability Database keeps the test set current as new attacks appear, reusable prompt packs make testing repeatable, and cross-LLM benchmarking lets you compare models on the same evidence. It is model-agnostic, so it runs against any LLM, including custom and on-prem.

This is the inventory and pre-deployment side of governance: you cannot govern what you have not characterised.

Protect and Enforce: policy that runs, not policy that sits in a deck

A tested model is not a governed one. Behaviour drifts, agents act on their own, and new vulnerabilities ship daily. Protect and Enforce puts runtime guardrails on every output and policy enforcement on every agent decision.

It adds agentic observability with a configurable rolling 30-day window, per-span input validation on the prompt path, toxicity scoring on live conversations, topic-adherence drift detection, and explainability for why a decision was allowed or blocked.

This is the policy and monitoring side of governance, running continuously, so an agent that looks governed cannot quietly do something else. No Agentic Theatre.

Prove and Comply: evidence regulators accept

Control you cannot prove is control you do not have. Prove and Comply assembles tamper-evident audit trails and compliance dashboards as a by-product of running the platform, not as a separate scramble.

It maps evidence to the EU AI Act (Article 9 risk management, Article 72 post-market monitoring, high-risk focus) and aligns to FCA and SEC expectations and ISO/IEC 42001. Enterprise auditability is built in: SOC 2, SSO and SCIM, and role-based access control.

This is the audit dimension, produced automatically from the same record the rest of the platform writes to. For the regulation detail, see the EU AI Act guide.

The difference: ML validators, not LLM-as-judge

Most governance tooling that scores AI behaviour uses another large language model as the judge. That is slow, expensive, and impossible to run on every output at enterprise scale.

Disseqt uses ML-based validators instead. The cleared numbers: around 99% less water, around 98% less CO2, and sub-50ms inline latency per check. That is what makes continuous, large-scale, real-time validation actually viable rather than a quarterly sampling exercise.

It is the engineering reason Disseqt can govern every output and every agent decision, live, instead of checking a sample after the fact.

Where this sits in the market

Disseqt is the only unified AI assurance platform that covers testing, monitoring, policy, audit, and compliance in one place. Buyers do not have to choose between observability and governance.

AI assurance is a new category. It is not legacy GRC, it is not eval tooling, and it is not monitoring on its own. It sits between the application layer and the enterprise governance function: the place where AI behaviour is tested, enforced, and proven.

Category leaders such as Credo AI, Holistic AI, Fiddler AI, and Monitaur each cover part of this picture well. Disseqt brings the full lifecycle onto one AI governance layer so the test result, the runtime guardrail, and the audit record share a single chain of evidence.

How this page differs from the rest of the cluster

If you are comparing options, start with AI governance tools: an honest run-through of the landscape and the capabilities worth evaluating before you buy.

If you are building the business case around outcomes (deploy AI faster with confidence, pass audits, control agentic risk), read AI governance solutions.

This page is the product itself: what the Disseqt platform is and how it works.

Who this is for

• Enterprise IT and engineering teams at FTSE 1000 and Fortune 500 organisations shipping AI into production and answerable for its behaviour.

• Financial-services risk and compliance leads under FCA or SEC scrutiny who need model risk control and evidence that holds up.

• Heads of AI governance and chief risk officers who need one register, one policy engine, and one audit trail instead of a tool sprawl.

A tier-one banking group governing live AI on Disseqt cut its time to assemble an audit evidence pack by [PROOF PLACEHOLDER].

Frequently asked questions

What is an AI governance platform?

An AI governance platform is the software an enterprise uses to control its AI across its full life: maintaining an inventory of models and agents, defining and enforcing policy, monitoring live behaviour, and producing audit evidence. Disseqt is an AI governance platform that does all four on one assurance layer.

How is an AI governance platform different from AI observability?

Observability tells you what your AI systems did. A governance platform decides what they are allowed to do, enforces it in real time, and proves compliance. Disseqt reads production signals and adds the policy, enforcement, and evidence layer on top. See AI governance vs observability.

Does it work with any LLM?

Yes. Disseqt is model-agnostic and runs against any LLM, including custom and on-prem models, so governance is not tied to a single vendor.

Can it govern AI agents, not just single models?

Yes. The platform enforces policy on every agent decision, validates each step on the prompt path, and detects topic-adherence drift, so autonomous and multi-step agents stay inside their envelope.

What standards does it map to?

The platform maps audit evidence to the EU AI Act (Articles 9 and 72, high-risk focus) and aligns to FCA and SEC expectations and ISO/IEC 42001, with SOC 2, SSO and SCIM, and RBAC for enterprise auditability.