LLM pentest, adversarial Red Team, ISO 42001 governance and continuous monitoring — designed for the real AI stack, not adapted from web testing.
Schedule an AI diagnosticEvery security testing provider promises to find vulnerabilities. Few can guarantee that each finding in the report is real, exploitable and correctly prioritized — that is the difference between a report that drives action and a report that drives rework.
A single false positive reaching the client is not operational noise: it is erosion of trust. The client loses hours investigating something that does not exist, questions every other finding by association, and the reputational cost outweighs the value of the entire engagement. Our methodology starts from a non-negotiable premise — no finding reaches the client without mechanical evidence that makes it defensible.
Most tools treat "finding" as a single category. We treat it as four distinct problems — because each one destroys value in a different way.
A finding that does not exist in the environment. It erodes trust directly.
A real vulnerability that goes unreported. The worst case: the client is exposed without knowing.
The vulnerability exists, but is not exploitable in that context. Operationally, it costs the same as an FP.
500 real findings without prioritization are worth, in practice, the same as 500 false positives.
Generic assessments only fight the FP. We address all four.
The differentiator is not a marketing promise — it is a pipeline with five components that work together.
Before any test, we map what is installed and authorized by policy, which controls are active and with what coverage, how the network is segmented and how identity/MFA are configured. That is what lets us distinguish "RustDesk installed" from "RustDesk authorized by policy, with verified hash". Without that context, the two look identical.
Each finding gets a score computed from six independent blocks — authorized inventory, behavior of active controls, formal allowlist, real exploitability in the segment, identity/MFA configuration and historical sector pattern. The score does not decide, it informs.
The analyst sees the score, the reasons behind it and four options mapped directly to the taxonomy: confirm, reject as FP (with mandatory justification), reclassify as irrelevant or re-prioritize. A high score requires a justification recorded for audit.
Every decision feeds a database of patterns by sector and size. The system learns, for example, that a given behavior is legitimate in 80% of mid-sized banks but only 17% of hospitals — and applies that knowledge from day one on a new client. Confidence in the pattern grows with volume and decays over time (18-month half-life), tracking the changing landscape.
Decisions enter an asynchronous queue, are aggregated every 6 hours in staging and only reach production after six quality gates: minimum volume, analyst diversity, engagement diversity, individual bias detection, temporal consistency and abrupt-change check. A daily monitor automatically downgrades rules that start to diverge from recent behavior.
Each finding ships with the mechanical evidence that justifies its presence.
VP-I and VP-MP are filtered out before they become tickets.
The more engagements in your sector, the more accurate the result — without rebuilding from scratch.
Every decision is recorded, ready for compliance.
Adapted from the research of Schneier et al. (2025), our Promptware Kill Chain maps how real adversaries compromise AI systems — from initial prompt injection to total exfiltration.
From proactive offensive testing to continuous governance, each pillar protects a different dimension of your AI program.
OWASP LLM Top 10, adversarial prompt engineering and infrastructure analysis — we find the flaws before the attacker does.
Full Promptware Kill Chain simulation — human operators exploit your system the way a real attacker would.
AI-VRM diagnostic of security posture, architecture and exposure across own and third-party systems.
Regulatory frameworks (EU AI Act, PL 2338), responsible-use policies and risk management for AI adoption.
AI security roadmap, board-level reporting and maturity assessment — C-suite-grade guidance.
21 incidents cataloged. 15 with 4+ kill chain stages. 57% maintained active persistence after initial detection.
Malicious code suggestions leading to arbitrary command execution on developer machines.
Lateral movement across Google Workspace without user interaction via prompt injection.
Using conversation memory as a persistent command-and-control channel for adversary operations.
Exploiting the AI code assistant to execute arbitrary commands on developer machines.
Whether you're deploying your first LLM or managing enterprise AI at scale, our team is ready to help you secure it.
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