Internal tool takeover & security review
The tools your teams built work.
That's not the same as safe.
Employees across your organisation are building software with AI. It saves them real time, and it carries unreviewed code, embedded credentials and undocumented data flows into production. We bring it under control without taking it away from the teams that rely on it.
Internal tool takeover
From shadow IT to a maintained, audited system.
Discover
Inventory internally built tools and scripts: owners, users, data touched, systems connected.
Review
Secrets, dependencies, authentication, authorisation, data handling, injection paths and a threat model per tool.
Harden
Fix findings, add tests and CI, move secrets to a vault, put it behind SSO, log every change, deploy properly.
Own
Document it in a private DeepWiki, then maintain it through our harness or hand it back to your engineers.
Why not ban them
Banning AI-built tools doesn't remove them. It hides them.
These tools exist because they save real time. Block them and the work moves to personal accounts and unapproved services, where nobody can see it. Bringing them under control keeps the productivity and gives security what it actually needs: an inventory, an owner and a record of every change.
- Teams keep working while we harden a copy of each tool.
- Every tool gets a named owner and a risk rating.
- Critical findings, like exposed credentials, are reported immediately, not in the final report.
- Tools that can't be made safe economically are flagged for replacement, with the reason.
Security review
For internal tools, vendor code, or any codebase you need to trust.
Available on its own, or as the first step of a takeover.
Secrets & credentials
API keys, tokens and passwords in code, configs and history, with a rotation plan for anything exposed.
Dependencies
Known-vulnerable and abandoned libraries, licence issues, and how fresh the lockfile really is.
Access control
Who can reach the tool, what it can reach, and whether either is more than it needs.
Data handling
Where sensitive data is read, stored, logged and sent, including to AI model providers, mapped against your policies.
Code-level flaws
Injection (including prompt injection into AI features), unsafe deserialisation, broken auth flows and the patterns AI-generated code gets wrong most often.
Findings you can act on
Ranked by severity, each with evidence and a fix, verified by a second independent reviewer to cut false positives.
Why it matters
AI-built code fails in predictable ways.
Independent testing found security flaws introduced in 45% of AI code-generation tasks. Research from Anthropic shows models under pressure to pass tests can learn to game them. Neither is a reason to stop your teams building. Both are reasons to put a system around what they build.
What you keep
- The cleaned code, in your own private repository
- A private DeepWiki of each tool
- The full findings report and remediation record
- A runbook your engineers can operate from
Find out what's running in your organisation.
Every engagement is scoped to your environment. Tell us where to start.