Discover
Build a live inventory of applications, code repositories, open-source dependencies, container images, secrets and operating-system software, and confirm the authorised scope before any scan runs.
Context-aware security
QsecurityAI understands your environment — what is exposed, what is reachable and what is critical to your business — then prioritises, fixes and verifies the vulnerabilities that put you at real risk.
New to the idea? What is context-aware security? →
This month's scan · illustrative
Fix first → prepared, approved, tested and verified closed. The rest are scheduled, with the reasoning recorded.
Why it's different
| Dimension | Patch everything | Context-aware with QsecurityAI |
|---|---|---|
| Priority | Generic severity score | Real-world risk in your environment |
| Workload | Thousands of equal-weight tickets | A focused list of fixes that reduce exposure most |
| Change risk | Rushed patches, higher outage risk | Tested, approved and reversible changes |
| Speed on real threats | Critical exposures wait in the queue | Reachable, exploitable issues fixed first |
| Evidence | Patch counts | Documented risk decisions and verified closure |
See it on scenarios like yours. A guided walkthrough of the prioritised view, the prepared fix and the evidence trail.
How QsecurityAI helps
QsecurityAI brings discovery, prioritisation, remediation and verification together. It does not stop at telling you what is wrong — it prepares the fix, routes it for approval, tests it and confirms the issue is closed.
Explore the live demoBuild a live inventory of applications, code repositories, open-source dependencies, container images, secrets and operating-system software, and confirm the authorised scope before any scan runs.
Enrich every finding with asset criticality, environment (production or non-production), exposure and known exploitation, so each issue is assessed in the setting where it actually lives.
Rank findings by real risk rather than raw severity, so teams see a short, defensible list of what to fix first — and what can safely wait or be accepted.
Generate the remediation — an upgraded dependency, a rebuilt image, a code change or an OS-level update — and deliver it as a reviewable change through your existing pipelines.
Apply your own approval policy: production changes wait for a named approver, while low-risk, non-production fixes can proceed automatically. AI-generated tests check for regressions before release.
Re-scan to confirm closure and record every decision, approval and result in an audit-ready trail for risk, compliance and regulators.
From the blog
Go deeper: the six context signals · one flaw, two places · benefits for every team
Frequently asked questions
No. Every finding is recorded and assessed. Context determines the order and urgency of fixes, and any decision to defer or accept a risk is documented and reviewed. How to defer a fix responsibly →
Changes follow your approval policy and are tested before release. Production changes can require a named approver, and every fix is delivered as a reviewable change through your existing pipelines. Setting a risk-calibrated policy →
No. QsecurityAI is designed to work alongside your current security and IT tooling, using existing pipelines and distribution channels to deliver fixes.
QsecurityAI can run within your environment, including with a local AI model, so sensitive code and findings remain under your control. Why it matters →
Guided pilot
Talk to our team about a guided pilot on part of your estate.