See Where AI is Already Running. Then Govern it
With Confidence.

Before investing further in AI, you need to know whether your security, governance, data, and operating models are prepared to support it. We assess current state across strategy, oversight, policy, data protection, identity, application and model security, third-party risk, legal and privacy, use-case approval, education, monitoring, and lifecycle management.

Deliverables:
Current-state maturity assessment, key findings and risk observations, capability and control maturity scorecard, prioritized gap analysis, executive-level risk summary, recommended future-state operating model, and a practical 30/60/90/180-day roadmap.

AI governance should enable innovation, not prevent it. We help establish the policies, decision rights, processes, and accountability needed to evaluate and approve AI use responsibly.

• Enterprise AI governance charter
• Generative AI usage standards
• AI risk-classification methodology
• AI governance committee design
• Human oversight and accountability standards
• Data handling and retention requirements
• AI acceptable-use policy
• Use-case intake and approval process
• Roles and responsibilities matrix
• Model and application inventory requirements
• Procurement and vendor-review requirements
• Exception and risk-acceptance processes

The Result:
A framework that defines who can use AI, what AI can access, how AI projects are approved, and how the organization verifies that AI stays secure, compliant, and aligned to business objectives.

You should not have to choose between AI innovation and security. We implement layered guardrails around AI users, applications, models, agents, data sources, and integrations.

• Identity — strong authentication, least privilege, privileged access, non-human and agent identity governance
• Applications & models — approved AI catalogs, secure configuration, prompt-injection protection, output validation and filtering
• Agents, APIs & connectors — API security, connector approval, MCP governance, tool authorization, human approval for high-risk actions
• Data — classification, DLP, sensitive-data detection, prompt and response inspection, encryption, residency and retention
• Devices & browsers — managed device requirements, enterprise browser controls, copy/paste and upload restrictions, context-aware access
• Monitoring & response — AI activity logging, behavioral monitoring, anomaly detection, incident response, evidence and audit trails

AI is only as trustworthy as the data it can access. We help you understand where data resides, how it is classified, which AI systems can reach it, and whether that access is appropriate — across structured and unstructured sources, cloud, SaaS, and on-premises.

• Data discovery and classification
• Data ownership and stewardship
• Sensitive and regulated information
• Vector databases and AI knowledge stores
• AI data-access mapping
• Data quality and lineage
• Retrieval-augmented generation environments
• Retention, deletion, and training-data governance

AI governance is not a one-time assessment. Models change, vendors add capabilities, employees adopt new tools, agents gain access to new data and business processes, and regulations keep moving. We help you build a repeatable lifecycle for managing AI from initial idea through retirement.

• Use-case submission
• Business value
• Risk classification
• Security & privacy review
• Data-access approval
• Architecture review
• Vendor due diligence
• Testing & validation
• Deployment approval
• Continuous monitoring
• Control reviews
• Retirement