Always-on AI pentesting, across all exposed assets.

It is the contextual orchestration engine that decides where AI pentesting runs, how deep, and with which techniques. Its scoring draws on three inputs: exposure assessment (fresh exposure data plus business and stack context), exposure validation (what continuous deterministic testing has already confirmed or ruled out), and threat intelligence (known vulnerabilities, emerging threats, attacker TTPs, and statistical models).

That scoring refreshes each cycle, so testing follows the current state of your surface and the threat landscape rather than a static list. It is also what makes continuous AI pentesting practical across the whole surface: by pointing each run only where it adds something new, it avoids spending compute on what a deterministic test already covered.

A deterministic test checks for a specific, known condition and returns the same verdict every time, with no reasoning required. These make up the Adversarial Exposure Validation layer: 100,000+ tests run continuously against every asset, spanning 30+ categories such as OWASP application weaknesses, authentication bypass, exposed data, injection, misconfigured security controls, orphaned assets, and cloud-specific exposures across web, API, and network surfaces.

They work two ways. Passive tests read surface signals, a software version with known CVEs, default credentials, a missing WAF, no HTTPS, and flag the exposure from what is observable, without sending an attack.

Active tests interact with the target directly, sending payloads measured against defined success criteria to confirm an exposure is genuinely exploitable. They also confirm passive findings, turning an inferred exposure into verified evidence.

Because they are fast and repeatable, deterministic tests cover the known and clear-cut cases at scale, which frees AI pentesting for complex attack simulations, which chain exploits and require reasoning.

Yes to both. Continuous testing runs unauthenticated by default, the way an external attacker starts.

On top of that, you can supply credentials to test authenticated areas and the functionality behind a login, and you can commission a dedicated test to go deep on a specific asset or scenario. Authenticated and dedicated tests run only on the assets and with the accounts you designate, and you can scope them per application or turn them off.

Yes. You have full control over the scope of what gets tested and the plan, which you approve before each run. Our platform then uses a combination of soft and hard guardrails to provide an additional layer of governance, keeping the agents focused and contained. At the end of each run, the full sequence of actions is transparently logged for audit.

We can test numerous types of externally reachable AI infrastructure: MCP servers, LLM and inference endpoints, chatbots and application front-ends, agent frameworks, and the vector databases behind retrieval-augmented generation.

Anything reachable from the outside is in scope and is tested for the failure modes specific to AI: prompt injection, agent hijacking through connected tools, unauthenticated access, system-prompt and credential leakage, and data exposure.

No. It makes your security team and red team more effective; it is not a replacement for them.

The platform handles the broad, repetitive work, proving exploitable findings across the full surface continuously, so your people are freed for what needs human judgment: complex business-logic abuse, social engineering, and sensitive internal targets. Teams often fold their recurring external testing into it, but the skilled work, and the people who do it, stay.

A scanner infers what might be vulnerable from version banners and configuration signatures and leaves confirmation to you, which is where false positives accumulate.

Breach-and-attack simulation replays known techniques inside your environment to test whether your controls detect and block them.

This does both, and goes further: it confirms what a scanner only flags, runs real attacker techniques the way breach-and-attack simulation does, and chains multiple findings into a proven path to impact, delivered with the request and response to reproduce it.

It is case by case. It addresses a lot of what SOC 2, PCI DSS, ISO 27001, and NYDFS ask for, with reproducible evidence packages and findings mapped to recognized frameworks, and many teams run it as their continuous external testing.

Requirements and auditors vary, though, so for anything compliance-specific we always suggest confirming with your compliance experts or auditor.