Use case
Continuous pentesting
Keep adversarial testing current between manual engagements, so meaningful changes can be tested instead of waiting for the next scheduled assessment.
The challenge
Where assumptions break down
A manual pentest is valuable because a skilled tester can explore context, intent and unusual behavior. The problem is that the environment keeps changing after the engagement ends. Applications deploy, permissions shift, secrets appear, SaaS access changes and AI models are updated. The assessment remains useful, but it no longer represents everything that is live.
How RedMaw approaches it
- 01RedMaw defines authorized scope and verifies ownership before active testing begins.
- 02It continuously tests supported applications, APIs, SaaS identities and AI systems within that scope.
- 03When exploitation is validated, RedMaw preserves reproducible evidence instead of treating possibility as proof.
- 04Findings keep a persistent security state as they move into remediation.
- 05When a fix is ready, RedMaw re-runs the relevant validation and closes the finding only when the exposure is no longer reproducible.
- 06Manual testing remains valuable for especially novel work, unusual business logic and assessments that specifically require human expertise.
Outcome
What changes
Adversarial testing continues between manual assessments.
Engineering receives validated findings with evidence and remediation context.
Security state stays connected to the current environment instead of a past report.
Fixes are verified by re-attack rather than ticket status.
Human pentesting can focus on the work where human creativity adds the most value.
Keep going
Related
Capabilities
- Application SecurityAdversarial testing of the web applications and APIs you own and authorize, aimed at proving exploitability rather than reporting resemblance to a known pattern.
- SaaS & IdentityTesting the customer-controlled identity and permission graph to establish what one compromised account, key or grant can actually expose.
- AI SecurityAdversarial testing of deployed AI systems and release pipelines, aimed at how the system behaves when the input is hostile rather than expected.
Stop assuming you are secure. Prove it.
Continuously test what an attacker can actually reach across your applications, SaaS identities, internal infrastructure and AI systems.