AI Control
AI Assurance
Know whether the AI tools and applications your business is using are secure, controlled, supportable and safe to depend on.
Who feels this pain?
AI is becoming business critical faster than most organisations can independently assure it.
Teams are building internal AI tools, introducing Copilot and Claude, and embedding AI into everyday workflows. At first these can feel like useful experiments. Then people start depending on them.
At that point, the question changes from “does it work?” to “how do we know we can rely on it?”
- How do we know the AI we are using is secure and properly controlled?
- Could sensitive information leave the business without us knowing?
- Are there weaknesses in the application, its code or the way it has been set up?
- Are we dependent on one person to keep something business critical running?
- What evidence can we give the board that risk, compliance or audits are under control?
What Is AI Assurance?
An independent review of the AI systems your organisation is already using or building
It gives you a clear, evidence-based view of whether they can be relied on and what needs attention.
What it covers
- Security and data exposure risks
- AI-specific risks, including prompt injection and unintended data exfiltration
- Access, credentials and governance management
- Supportability, resilience and key-person dependency
- Prioritised remediation and practical next steps
What You Get
- An independent, verified view of your AI’s security, control and supportability risks
- One complete, prioritised view of the findings, rather than scattered concerns
- What to fix now, what can safely wait, and how to remediate it
- Evidence that leadership, risk, compliance and IT can use to understand the position
- A route to reduce key-person dependency without losing internal knowledge
- A baseline for ongoing assurance as your AI systems change
More than a list of findings
It’s about knowing which risks matter, fixing them first and keeping AI adoption moving.
Reduced operational, security and data risk
Greater confidence in the AI the business depends on
Clearer ownership and accountability
Stronger resilience and supportability
Faster remediation of the risks that matter
With AI adoption continuing under proportionate controls.
A real example
Independent AI Assurance & Reduced Key-Person Risk
CHALLENGE
An internally built AI tool, used daily for research and document retrieval, had quietly become business critical, and it was built and run by one person. A file deletion incident exposed how much the team relied on it.
WHAT CHANGED
Synetec reviewed the tool, its controls and its supportability in two weeks, working with its builder at code level. Findings were verified, not taken on trust. The firm now has one prioritised view of its risks, including previously unknown credential and prompt-injection issues.
Risks prioritised
To an independent, verified review
Critical issues being fixed
Your AI does not need to have failed before it is worth assuring
If people are already relying on it, the question is whether you know enough about what is underneath it to rely on it with confidence.
