Question 1

What does AI Security and Governance actually cover?

Accepted Answer

Security, control, and accountability for AI systems end to end. That includes bounded use cases instead of open-ended autonomy, human oversight at meaningful decision points, least-privilege access, isolated execution environments, layered defense across identities, apps, infrastructure, networks, data, and logging, and a responsible AI governance process built around mapping, measuring, mitigating, and managing risk.

Question 2

How is this different from a standard security review?

Accepted Answer

Traditional security reviews focus on infrastructure and identity. AI systems add new surfaces: what data the model can reach, which tools it can invoke, how prompts and outputs are governed, and how users interact with the system. We bring Zero Trust principles into all of that — verify explicitly, least-privileged access, assume breach — and design controls specifically for agentic workflows.

Question 3

Do autonomous agents ever get full access to client systems?

Accepted Answer

No. Our default posture is conservative. Agents run in isolated containers with dedicated credentials, connect only to the systems required for the task, and operate under explicit allow-lists. Sensitive or irreversible actions require human sign-off. Every meaningful action is logged and auditable after the fact.

Question 4

What does responsible AI governance look like in practice?

Accepted Answer

A structured process: map potential harms and abuse surfaces, measure risk across model, safety system, grounding, and UX, mitigate at multiple layers, and manage deployment through operational readiness. We think about content risk, prompt abuse, grounding quality, misuse scenarios, feedback loops, rollback plans, and incident response before expanding any deployment.

Question 5

Who should engage Queen City AI for this?

Accepted Answer

Organizations in regulated or high-trust environments — financial services, healthcare, legal, professional services, and anyone with sensitive data or reputational risk — that want to deploy AI in production without betting the business on a single control, and without slowing their teams down.

Secure AI adoption for Microsoft environments.

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