Most AI advisory either ignores governance entirely or pretends to be a compliance audit firm. IT Empowered does neither. Here's the honest scope of what governance work looks like in our engagements.
Draft and tailor an internal policy that defines what staff can and cannot do with public LLMs, what data may be pasted into third-party tools, and how to label AI-assisted output. Includes leadership review cycle and a rollout plan tied to AI literacy training.
Map which data classes (public, internal, confidential, regulated) are allowed in which AI systems. Architect automations to default-keep sensitive data out of third-party LLM contexts — using prompt redaction, server-side scrubbing, or routing to private model endpoints where required.
Evaluate AI tools and platforms against a consistent rubric: data-retention posture, training-on-customer-data terms, sub-processor list, regional hosting, SSO/RBAC support, audit logging, and exit clauses. Output is a one-page recommendation per tool, not a 40-page report nobody reads.
Bake review checkpoints into AI-driven workflows: who confirms a generated draft before it goes out, what gets logged, when escalation is mandatory, and how exceptions are tracked. This is the difference between an AI experiment and a production system you can defend.
Direct experience includes role-based access control and audit trails on CivicRelay (a government-facing platform), least-privilege design for webhook automations, and PII-aware prompt construction. For deeper controls (penetration testing, formal SOC 2 readiness), IT Empowered scopes the right partner and stays in the room as the AI subject-matter expert.
As organizations scale AI adoption from a few enthusiastic users to org-wide workflows, the absence of policy and boundaries becomes the single biggest source of risk — far more than model choice. A short, well-rolled-out governance baseline lets a team move faster, not slower, because staff stop self-policing every prompt.