What is the difference between AI governance and LLMOps?

Governance defines the policies, risk ratings and accountability; LLMOps provides the tooling — versioning, evaluation, tracing, guardrails and monitoring — that enforces them day to day.

Do internal AI tools need governance?

Yes, but proportionately. Internal tools still handle company data, so at minimum they need an owner, data-access controls, logging and a basic evaluation.

How do we prevent prompt injection?

Combine input filtering, strict separation of instructions from retrieved content, least-privilege tool access, output validation and human approval for sensitive actions. No single control is sufficient.

How often should AI systems be re-evaluated?

On every model, prompt or data-source change, and periodically — for example quarterly — on fresh samples of production traffic.

AI Governance Checklist for Enterprises: Controls to Put in Place Before Go-Live

A practical AI governance checklist: inventory, risk rating, data controls, evaluation, guardrails, human oversight, monitoring and audit trails.

AI governance is the set of policies and technical controls that keep AI systems accurate, fair, secure and accountable. Before go-live, every system should have an owner, a risk rating, controlled data access, a documented evaluation, guardrails, human oversight where decisions matter, monitoring and a complete audit trail.

Why does AI governance matter now?

AI is moving from pilots into processes that affect customers, money and compliance. Regulators in many regions — including the EU AI Act and data-protection laws such as GDPR, India's DPDP Act and the UAE's PDPL — expect organisations to understand and control how automated systems use data and make decisions.

Good governance is also good engineering: the same controls that satisfy auditors make systems easier to debug and improve.

The pre-launch checklist

Use this as a minimum before any AI system reaches real users:

How do you govern LLMs and agents specifically?

Language models add risks that classic ML governance did not cover: prompts that can be manipulated, outputs that sound authoritative when wrong, and agents that can take actions through tools.

Treat prompts as versioned code, restrict agent tools to the minimum required, validate tool inputs against schemas, and log full traces with an observability tool such as Langfuse or OpenTelemetry. An AI gateway such as LiteLLM can centralise model access, keys, rate limits and spend.

What should you monitor after launch?

Governance does not end at go-live. Track continuously:

Who should own AI governance?

A small cross-functional group works best: a business owner per system, a technical owner, plus risk, legal, security and data-protection input. Keep the process proportionate — low-risk internal tools need a light review; systems that affect customers, credit, health or employment need deeper assessment and sign-off.

We implement governance tooling, telemetry and monitoring for AI programmes — see our AI governance and LLMOps pages for how we set it up.

Inventory: the system is registered with a named business owner and technical owner.
Purpose and risk rating: intended use, users and impact are documented and classified.
Data controls: sources are approved, personal data is minimised or masked, and access follows least privilege.
Model record: model versions, providers, prompts and fine-tuning data are documented.
Evaluation: accuracy, robustness and bias are tested on a representative, frozen test set.
Guardrails: input and output filters for PII, harmful content, prompt injection and off-topic use.
Human oversight: people review or approve consequential outputs, with a clear escalation path.
Transparency: users know they are interacting with AI and can reach a human.
Security review: threat modelling for prompt injection, data leakage and tool misuse.
Monitoring and audit: every request, response and tool call is traceable, with alerts on quality and cost.
Quality signals: user feedback, evaluation scores on sampled traffic, and escalation rates.
Drift: changes in input data or user behaviour that the system was not tested on.
Safety events: guardrail triggers, blocked prompts and policy violations.
Cost and latency per use case and per team.
Provider or model changes that require re-evaluation.