AI Assurance
Make AI easier to trust, review, and defend.
Catalyst AI helps organisations assess AI use cases, define controls, evaluate behaviour, and assemble the evidence needed for internal governance, procurement, risk review, and audit readiness.
Discuss AI Assurance
What AI Assurance means
AI Assurance is the discipline of showing that an AI system is being used responsibly and predictably enough for its context.
It asks practical questions:
- Q1
What is the AI allowed to do?
- Q2
What data can it access?
- Q3
Who is accountable for the outcome?
- Q4
How is behaviour evaluated?
- Q5
When must a human review or approve?
- Q6
What gets logged?
- Q7
What evidence exists if a decision is challenged?
- Q8
How are changes tested before release?
Services
AI use-case risk review
Assess the intended use, impact, data, users, decisions, and failure modes.
Governance and control design
Define ownership, permissions, approval gates, change control, escalation, and documentation requirements.
Evaluation and evidence
Design practical evaluation sets and evidence that show how the system behaves on representative and edge cases.
Human oversight design
Decide where human review is required, who can approve, and what information the reviewer needs.
Traceability and audit design
Define what should be logged across prompts, sources, model outputs, approvals, actions, and system changes.
Readiness support
Help teams prepare for internal assurance, customer procurement, and recognised AI-management expectations.
For AI we build, and AI others build
AI Assurance is cross-cutting. We build appropriate controls into Catalyst AI delivery, and we can also review or support governance for AI systems built by internal teams or other vendors.
Where independent certification, legal opinion, or specialist regulated assurance is required, the engagement clearly defines the role of qualified external partners. Catalyst AI is not an independent certifier.
What we do not claim
- We do not claim that AI risk can be eliminated.
- We do not claim that a model is "safe" without defining the use case and controls.
- We do not claim certification status that has not been formally achieved.
- We do not treat a policy document as proof that controls are operating.
Need stronger governance around an AI system?
Whether we built it, your team built it, or a vendor did.
Discuss AI Assurance