Complex context, made readable
AegisVMP uses AI to explain findings, exposure and possible impact — producing narratives and recommendations that support human decision-making rather than replacing it.
A conversation with your own context
Because assessments are grounded in the platform's unified model, the answers reference your assets, your exposure and your prioritization — not general advice.
Why is this finding ranked above the other criticals?
The affected asset is internet reachable and carries high business criticality. The vulnerability also has associated threat intelligence context, and related activity was observed in security telemetry. Together these factors raise contextual risk above findings with the same technical severity.
What would reduce the risk fastest?
Reducing exposure on the affected service lowers reachability, which is the largest contributing factor here. Applying the vendor remediation removes the underlying vulnerability. Both actions are suggestions for review, not automatic changes.
Illustrative example. AI output supports analyst judgement; it does not replace it.
Four things AI assessment is good at
Narrow, well-defined jobs where turning layered context into plain language saves a security team real time.
Explain a finding
Describe what the vulnerability is, what it affects and why it appears where it does in the queue.
Summarize exposure
Express how reachable an affected system may be, in language that does not require a scanner report to interpret.
Describe possible impact
Outline what the finding could mean for the organization given the asset and its business context.
Suggest next steps
Offer candidate remediation directions for a security team to evaluate, prioritize and decide upon.
Designed for review
AI in a security platform has to be checkable. These principles shape how assessment output is produced and presented.
Explanatory, not authoritative
AI describes why a finding looks the way it does. Decisions remain with the security team.
Grounded in platform data
Assessments draw on the assets, findings, signals and context already unified in the platform.
Reviewable output
Narratives and recommendations are written to be checked, questioned and overridden.
Consistent language
Complex technical detail is expressed in terms that both engineers and leadership can read.
Assessment lives where the work happens
AI output appears alongside the findings and dashboards teams already use, so the explanation and the evidence stay together.
See AI assessment on real context
Request a walkthrough of AegisVMP and see how unified intelligence changes what your team works on first.