Use AI to strengthen security tools — not replace them
Language models can be genuinely useful in software security. They should not be the thing deciding whether your code has a vulnerability. We build workflows that combine established security tooling with AI-assisted analysis, and keep the two jobs separate.
Let specialized tools do specialized work
Depending on the environment, a security pipeline may include tools for static analysis, dependency vulnerabilities, secret detection, container scanning, configuration analysis, software composition analysis, web application testing and log analysis.
These tools are deterministic, auditable, and have spent years learning what a real finding looks like in their domain. They produce findings. That part is not the problem.
The problem is the pile
Run six scanners across a real codebase and you get an unreadable amount of output: the same issue reported three different ways by three different tools, a hundred findings with no indication which two matter, and severity ratings assigned without any knowledge of how your application actually works.
That is where AI earns its place. It can:
- Correlate reports across tools
- Remove obvious duplication
- Explain a technical finding in plain language
- Identify likely impact in the context of this codebase
- Prioritize what a person should look at first
- Provide the surrounding code context
- Suggest remediation
- Produce a report a human can act on
The scanners decide what was found. AI helps decide what to do about it.
A security worker, end to end
- receive repository or a commit range Runs on your network
- run scanners SAST, deps, secrets Runs on your network
- normalize findings one common format Runs on your network
- correlate collapse duplicates Runs on your network
- AI analysis local model, no upload Runs on your network
- review report evidence attached Runs on your network
Keep source code under your control
For organizations that do not want proprietary source code sent to external AI services, the analysis stage can run on local or controlled infrastructure. For a software company this is often the entire reason the project is possible at all — the code is the intellectual property.
Different stages can also have different rules. Scanning a public dependency graph is not the same as feeding your proprietary business logic to a third party, and the architecture can treat them differently.
Beyond scanning
The same pattern applies to development work more broadly:
- Repository analysis
- Code assistance
- Documentation generation
- Internal knowledge search
- Issue and ticket analysis
- Log analysis
- Release workflow automation
- Configuration review
All of it can be built to run where your policies say it should, which for engineering organizations is usually "here."
Keep the source code, keep the evidence
Established scanners do the scanning. AI correlates, explains and prioritises what they found — and the underlying findings stay reviewable.