SafeSI

The problem. AI adoption has outrun enterprise control. Models, copilots and agents arrive through a dozen doors, often without a risk assessment, and leadership cannot answer basic questions. What AI are we running. What data can it reach. What can an agent actually do. Which system is the one that causes us a problem. Policies exist, but they sit in documents rather than in the running system.

How it works. SafeSI runs a closed loop across the AI lifecycle: discover, assess, govern, secure, monitor, prove.

Discovery builds a central inventory of every model, dataset, prompt, agent and vendor AI, with its owner and what it can access. Assessment scores each one on criticality, data sensitivity, autonomy, impact, explainability and oversight, producing consistent classification across the enterprise. Governance turns responsible AI principles into operational controls: policies, approval workflows, risk ownership and documentation.

Agentic governance is where SafeSI goes further than model governance can. An agent has an identity, permissions, tools it may invoke, memory it retains, actions it may execute and a threshold at which a human must approve. Each of those is governed, and every autonomous action can be reconstructed afterwards.

Safety testing evaluates behaviour before and after deployment, covering hallucination, bias, toxicity, robustness, explainability and agent conduct. Security addresses the attack surface AI creates, including prompt injection, data leakage, model manipulation, insecure tool invocation and agent privilege escalation. Monitoring watches behaviour, drift, policy violations and human overrides in production, and alerts when a system leaves its defined operating boundaries.

All of it produces evidence. Assessments, test results, approvals, oversight records, exceptions, incidents and agent activity are held as an auditable record and exported as an evidence pack.