How does AI governance differ from LLMOps?

Governance sets the policies, risk ratings and accountability; LLMOps supplies the tooling — versioning, evaluation, tracing, guardrails and monitoring — that enforces them every day.

Do AI tools used only internally need governance?

Yes, in proportion. Internal tools still process company data, so as a minimum they need an owner, data-access controls, logging and a basic evaluation.

What is the best way to prevent prompt injection?

Layer input filtering, strict separation between instructions and retrieved content, least-privilege tool access, output validation and human approval for sensitive actions. No one control is enough by itself.

How frequently should AI systems be re-evaluated?

Whenever a model, prompt or data source changes, and also on a regular cycle — quarterly, for instance — using fresh samples of production traffic.

Enterprise AI Governance: Pre-Launch Controls Checklist

An AI governance checklist for enterprises: inventory, risk rating, data controls, evaluation, guardrails, human oversight, monitoring and audit trails.

AI governance is the combination of policies and technical controls that keeps AI systems accurate, fair, secure and accountable. Ahead of go-live, each system needs an owner, a risk rating, controlled data access, a documented evaluation, guardrails, human oversight where decisions carry weight, monitoring and a full audit trail.

Why AI governance is urgent today

AI is shifting from pilots into processes that touch customers, money and compliance. Regulators across many regions — through the EU AI Act and data-protection laws like GDPR, India's DPDP Act and the UAE's PDPL — expect organisations to understand and control how automated systems handle data and reach decisions.

Sound governance is sound engineering too: the controls that satisfy auditors also make systems simpler to debug and improve.

AI go-live checklist

Treat this as the baseline before any AI system reaches real users:

Governing LLMs and AI agents

Language models bring risks that traditional ML governance never addressed: prompts open to manipulation, wrong outputs that still sound authoritative, and agents able to take actions through tools.

Handle prompts as versioned code, limit agent tools to the bare minimum, validate tool inputs against schemas, and record full traces using an observability tool like Langfuse or OpenTelemetry. An AI gateway like LiteLLM can centralise model access, keys, rate limits and spend.

Post-launch monitoring for AI systems

Governance carries on after go-live. Keep tracking:

Who owns AI governance?

A compact cross-functional group works best: a business owner for each system, a technical owner, and input from risk, legal, security and data protection. Keep the effort proportionate — low-risk internal tools need only a light review, while systems affecting customers, credit, health or employment need deeper assessment and formal sign-off.

We put governance tooling, telemetry and monitoring in place for AI programmes — our AI services page explains how we set this up.

Inventory: the system is registered, with a named business owner and a named technical owner.
Purpose and risk rating: the intended use, users and impact are written down 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 all documented.
Evaluation: accuracy, robustness and bias are tested against a representative, frozen test set.
Guardrails: input and output filters cover PII, harmful content, prompt injection and off-topic use.
Human oversight: people review or approve consequential outputs, and there is a clear escalation path.
Transparency: users are aware they are dealing with AI and can reach a human.
Security review: threat modelling addresses prompt injection, data leakage and tool misuse.
Monitoring and audit: every request, response and tool call can be traced, with alerts on quality and cost.
Quality signals: user feedback, evaluation scores on sampled traffic and escalation rates.
Drift: shifts in input data or user behaviour that the system was never tested against.
Safety events: guardrail triggers, blocked prompts and policy violations.
Cost and latency broken down by use case and by team.
Provider or model changes that call for re-evaluation.