An attacker who lands in your app does not stay there, and neither does Aegis. Autonomous agents test the application; the operating system, infrastructure and network are tested in their own modes. Every finding graded by its evidence. An attacker's view in hours, not weeks.
is how much of your attack surface a quarterly engagement actually covers.
Production code changes hourly. Dependencies update daily. A single pull request rewrites your topology. Your last pentest (four to eight weeks, $15k-50k) describes a system that no longer exists.
Coverage has become a percentage. It should be a date.
“Can I get a credible attacker's view on the release I'm about to ship?”
Findings lists still require expert triage before a team knows what is actually real. The human triage cost dwarfs the scan cost.
An engagement produces a snapshot, then the system changes before remediation completes. The PDF is out of date on delivery.
Senior testers are scarce; coverage expands only by adding people and calendar time. Doubling coverage means doubling the bench.
The gap isn't detection. It is validated, reproducible adversarial evidence at the speed of delivery.
Models now construct full exploitation paths end-to-end, without supervision.
Agents chain proxies, shells and analysers reliably; the scaffolding succeeds more than it fails.
A full assessment moved from experimental to routine. Over 10x cheaper in eighteen months.
None alone made this category. Their arrival together did, and the window is measured in quarters, not years.
Hundreds of autonomous agents reason, plan and execute tools against your stack. Every call, token and dollar is audited with provenance.
Application, operating system, infrastructure, network. The same run crosses the boundaries an attacker crosses, and one CLI flag puts it on every pull request.
Every finding tied to OWASP, MITRE ATT&CK / ATLAS / D3FEND, NIST CSF and AI RMF, plus your sector rules: PCI DSS 4, RBI, UAE Central Bank.
One run, one chain of reasoning, one evidence package your team can defend.
Payments API response shape repeats across user contexts.
Sibling endpoints inherit the same authorization pattern.
A PoC moves a small balance between test accounts.
Impact is translated into transaction and regulatory exposure.
The exploit path ran through a credential in app config: the host config audit follows it down.
Every finding labelled with how it was tested and how strongly it is proven.
The interesting word isn't autonomous. It is reproducible.
The code you ship and the endpoints it exposes.
A credential in application config is an operating-system door.
How the host and its containers are configured.
A privileged container is a route to everything the host runs.
The software the host is running, and its version.
A known-vulnerable service is a foothold that needs no zero-day.
What answers on the wire, and how it negotiates.
Every finding at every layer carries a CWE. So all four speak one language: the same OWASP categories, the same ATT&CK techniques, the same clauses. Not four tools that each describe the same host differently.
Response headers, cookie names, body markers, and well-known paths identify what the target actually runs, then the matching specialist agent is recommended for it.
A pinned probe corpus driven at any chat endpoint with deterministic detectors by default. The same scope guard and finding lifecycle also drive live cross-tenant RAG isolation and tool/agent authorization probes, and an opt-in model-assisted judge catches compliance no pattern can key on, clearly labelled apart from every deterministic hit.
Static audit of manifests, ATS exceptions, exported components, URL schemes, and hardcoded secrets.
Static configuration audit, passive CVE correlation from versions a target already disclosed, and live TCP/TLS probing that stays off until you arm it.
Agents do not carry one generic prompt. They load the skill the target calls for, from OAuth flaws and request smuggling to cloud misconfiguration and prompt injection.
An HTTP proxy, a headless browser, a terminal, a Python runtime, web search, and file analysis, wired into the agent loop inside an isolated sandbox.
Scans of the same target line up against each other: what is fixed, what is new, what came back. Posture is classified per run and charted as a trend, not asserted.
Findings inherit the business context that decides how much they matter. Criticality on a product escalates its findings; every UI-driven change lands in an activity trail.
Aegis is an offensive system, so governance is a product requirement, not an appendix. Every prompt, tool call, reasoning step and dollar is a typed telemetry record, and each finding carries the chain of inference that produced it.
A senior consultant remembers a fraction of what they did. Aegis records all of it.
Every tier enforces the same per-scan budget cap, real-time spend visibility, and dollar-per-finding telemetry. The tier governs how often you run, not how the platform behaves on each run.
One founder builds the agent platform. Two have spent their careers running the security programmes, and clearing the certification regimes, that it reports into.
Chief AI Officer. Non-Executive Director. Author.
Builds the agent platform: the autonomous testing loop, the evidence pipeline that replays every proof of concept, and the per-call cost ledger underneath it.
Cybersecurity transformation leader.
Leads global AI-powered cybersecurity programmes, advising brands and decision makers on strategic, cost-efficient defence. Brings the 360 degree operator view.
Cybersecurity transformation and strategy leader.
Eighteen years of enterprise security transformation, from on-prem through cloud, SASE and ZTNA. Carries the certification side: FIPS, Common Criteria, NIST, USGv6 and VPAT.
The people building it are the people who have had to answer for it: to a board, to an auditor, and to a certification lab.
Bring one app or API. We run a bounded, instrumented engagement against your real surface, and you leave with validated evidence to compare against your last assessment.
The cleanest buying motion is to run Aegis against a real surface and compare the output to your last assessment.
One app, API, or agentic workflow with test credentials.
Bounded budget, bounded scope, fully auditable activity.
Validated findings, PoC replay, impact framing, remediation path.
Time, cost, coverage, false positives and usefulness vs. your last pentest.
Pilot success criteria: reproduced findings, lower triage burden, faster evidence, and a credible path to deployment gating.
The category is arriving. The only variable is who runs it first.