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We help enterprises govern AI models and AI agents from policy to production: an inventory of every system, risk tiers, decision boundaries agents can't cross, and evidence regulators accept. Then AgentGRC keeps it running.
From XenonStack, trusted on five-year contracts by a leading Indian bank and a Dubai government entity.
Illustrative values, not customer data.
AI governance consulting helps an organisation decide how its AI systems may be built, used and monitored. Consultants inventory AI use, classify risk, write policies and an operating model, map them to regulations such as the EU AI Act and ISO 42001, and set up the controls and evidence that prove AI is used responsibly.
Most governance programmes stop at a policy document. We go further and enforce it: each policy becomes a decision boundary that runs before an AI agent acts, and each action leaves a trace auditors can read.
Model governance asks whether an output is accurate and fair. Agentic AI governance also has to ask what the system is allowed to do, on whose authority, and how you would prove it afterwards.
| Dimension | Governing AI models | Governing AI agents |
|---|---|---|
| Main risk | A wrong or biased answer | A wrong action in a system of record |
| Control point | Training data and output review | Every tool call, before it runs |
| Permissions | Who can use the model | What the agent can read, change and approve |
| Evidence | Model cards and test reports | Decision traces: inputs, policy, approver |
| Rollout | Deploy or don't | Autonomy earned in stages |
Find every model, copilot and agent in use, including shadow AI, and assign each a risk tier.
An AI governance framework with clear owners, approval paths and the lines of defence that fit your organisation.
Turn policy into machine-checkable rules: limits, approvals and escalations each agent must respect.
Controls mapped to the EU AI Act, ISO 42001, NIST AI RMF, India's DPDP Act and UAE AI and data rules.
Validation, evaluation and incident processes, extended from model risk to agent behaviour.
Charter, membership, decision rights and the reporting pack your board and regulators will see.
Gap assessment and an AI management system ready for certification audit. ISO 42001 guide (coming soon).
Controls, traces and reports live on the AgentGRC platform, so governance keeps working after we leave.
Which data, documents and systems each agent can see, and whether that access is justified.
Policies written as decision boundaries, checked before every action, with human approval where the risk calls for it.
Tool permissions, rate limits and rollback plans for every system an agent can change.
Each AI decision is stored with its inputs, the policy applied and the approver. That record is your audit evidence.
Agents move from Shadow to Supervised to Bounded to Full autonomy only when their traces show they have earned it.
AI inventory, risk tiers, regulatory scope and a gap analysis against your chosen frameworks.
Policies, operating model, governance board and decision boundaries for the highest-risk systems.
Controls live on AgentGRC, traces switched on, first board report delivered and a roadmap for the rest.
| Dimension | Big-4 advisory | Tool-only vendors | AgentGRC consulting + platform |
|---|---|---|---|
| Output | Policies and a report | Software you configure yourself | Policies enforced as running controls |
| Agent depth | Mostly model-level | Monitoring after the fact | Boundaries checked before each action |
| Evidence | Point-in-time assessment | Dashboards and logs | Continuous decision traces |
| Regulations | Broad coverage | Varies by product | EU AI Act, ISO 42001, NIST AI RMF, DPDP, UAE |
| After go-live | New engagement | Your team runs it | Platform plus optional managed oversight |
Building agents too? See AI agent development services. Need to keep models and data in-country? See sovereign AI options.
They inventory your AI use, classify risk, write policies and an operating model, map controls to regulations, and set up the monitoring and evidence that show AI is used responsibly.
A set of principles, policies, roles and controls that decides how AI is approved, built, used and monitored. Ours adds decision boundaries and decision traces for AI agents.
Agents act in your systems, so governance must control what they can do before they do it, not just review outputs afterwards.
The EU AI Act, ISO/IEC 42001, the NIST AI Risk Management Framework, India's DPDP Act and UAE AI and data requirements, plus sector rules your regulator sets.
Yes. We run the gap assessment and build the AI management system. Certification itself is issued by an accredited certification body.
A typical programme runs 90 days from assessment to live controls. Scope depends on the size of your AI estate; commercial terms are agreed per engagement.
No, the consulting stands on its own. The platform keeps controls and evidence running continuously, which is hard to do in spreadsheets.
We'll inventory your AI systems, rate their risk, and show you the gaps against the regulations that apply to you.
AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability
Proactive detection of performance and availability issues
Root-cause analysis across microservices and environments
Automated remediation playbooks to reduce MTTR
AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly
Real-time detection of suspicious motion or intrusion
Natural language video search and instant playback
Smart summaries for audits, investigations, and compliance
Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics
Connects to warehouses, lakes, and streaming sources
Question-answering in natural language
Continuous monitoring for anomalies and KPI deviations
Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment
Automated diagnostics for recurring errors
Playbook execution: restart services, scale pods, clear queues
Feedback loop for improving remediation strategies
AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready
Continuous control checks across infrastructure and SaaS
Automated evidence collection for audits
Risk scoring and prioritized remediation recommendations
Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter
Real-time visibility into spend and commitments
Anomaly detection on invoices and vendor performance
Intelligent workflows for approvals and sourcing decisions
