AI Governance Consulting Services

Custom AI governance starts directly from your own AI inventory - who uses which models for what purposes and which data. It then places additional control when the risk is higher and lesser controls when used internally for experiments.

Responsible AI is the principles (fairness, transparency, accountability, safety, privacy). AI governance is the implementation model, roles, policies and technical controls to enforce these principles and auditability thereof. Our services provide all of this in addition to security and compliance.

The EU AI Act (mandatory, €35m fines possible), ISO/IEC 42001 (certifiable AI Management System), NIST AI RMF 1.0 and GenAI profile, OECD principles, compliant with GDPR and, when necessary, HIPAA and SOC 2. Enterprises need at least 2 of them. This is implementation support, not legal advice.

Copilot shows anything the user has access to, therefore over-permissioned or unlabeled SharePoint documents are rapidly surfaced. We mitigate through Purview DSPM to assess AI, SharePoint item-level remediation (remediation, labelling, notification to document owner, de-linking from Copilot), DLP to prevent summary generation of labelled documents, restricted content discovery and permissions hygiene.

Data Security Posture Management for AI consistently highlights the locations where sensitive data exists within SharePoint, OneDrive, Teams, and Exchange; the people that have access to it; how it is shared and used by AI; and its current risk level. It's the foundation for Copilot and agent data governance.

Yes, output policies and access control for generative AI, Microsoft Entra Agent ID for every Foundry agent (which is governed like a user with Conditional Access, Identity Protection, and network policy); least privilege tools, human in the loop approvals, lifecycle/registry and tracing via the Foundry Control Plane.

For guarding against prompt injection, data poisoning, leakage, drift, model stealing and unauthorized retraining we use guardrails on input/output, content safety, grounding/retrieval, least privilege access, monitoring, red teaming and testing, and this in combination with Microsoft Defender & Purview.

Yes, readiness for EU AI Act (risk classification, transparency, documentation, human oversight, technical file) and ISO/IEC 42001 management system controls in combination with your legal and risk management partners. Not legal advice.

Foundry Control Plane with observability, runtime controls, evaluation and fleet operations, together with Defender, Purview, and Entra; along with content safety, private networking, Entra ID, Key Vault and EU Data Boundary / Sovereign Cloud when necessary.

Both, but putting the controls in place is what sets us apart. As a Microsoft Solutions Partner, we design the governance for your Azure, Microsoft 365 and AI applications, and we can deliver the operating model and managed governance.

LLMOps/MLOps: groundedness and answer-quality evaluation, safety/red-team testing, latency and cost monitoring, data and model drift detection, and auditing/logging, with alerting/remediation on quality degradation.

An initial assessment and roadmap typically runs 3-6 weeks; standing up the operating model, policies and first controls usually takes 2-4 months, followed by ongoing managed governance. Cost scales with your AI inventory, the frameworks in scope, and whether implementation is included. Tell us your footprint and we'll scope a fixed estimate.

Absolutely, our strength lies in the Microsoft estate and our ability to govern multi-cloud, hybrid AI with controls being mapped into one model.

Data governance manages how data is collected, stored, accessed and protected. AI governance goes further: it manages how that data is used inside AI models, how AI decisions get made, and how the resulting risk is monitored. Most enterprises need both, delivered as one connected program rather than two separate initiatives.

Yes, governance can be tailored to application-specific AI like chatbots and enterprise copilots, ensuring they follow approved data-usage rules, stay transparent about what they can access, and meet compliance standards, which matters most for customer-facing or regulated workflows.

The right solution is one that involves a combination of policy management, risk controls, auditability, and compliance with regulations relevant to the industry, such as HIPAA for health care and IRDAI for insurance; and DORA and model risk management for financial institutions. The key is having an AI governance consultant tailor it based on your risk exposure.