Services / AI & Digital Assurance
AI features open attack surface your last assessment never saw, and enterprise buyers now ask who tested them. We assess, test, and govern AI systems for clients, and we run AI in production across our own platforms under rules we publish. Both sides of that work are on this page.
We run AI in our own delivery and we secure the AI our clients ship. The same engineering discipline runs through both.
Our three platforms put AI to work on the parts that scale. VirtueThreatX validates which exposures are genuinely exploitable before they reach you. VirtueShieldX runs AI-driven detection and autonomous triage under analyst supervision. VirtueATLAS authors tests and self-heals automation as your application changes. Each one earns its keep on live client work every day, on real releases rather than in a demo.
AI features open an attack surface your last assessment never saw: prompt injection, insecure model integration, and shadow-AI endpoints. Our offensive team tests them as first-class targets. In one engagement we demonstrated a working prompt-injection bypass in an AI rewrite endpoint, plus two file-upload bypasses, all remediated and verified clean on retest. VirtueThreatX treats AI and LLM systems as one of its nine attack surfaces.
Eight engagements, one discipline: findings demonstrated rather than asserted, fixes verified on retest, and a deliverable your engineers can act on the week it arrives.
Security assessment of the AI in your product. We map where models touch user input, data stores, and downstream tools, then test those paths as first-class attack surface. Findings are demonstrated, ranked by exploitability, and verified fixed on retest.
Read the engagement→Prompt injection, instruction override, system-prompt exposure, and unsafe output handling, tested against your live endpoints. In a real engagement we demonstrated a working prompt-injection bypass in an AI rewrite endpoint; the fix was verified clean on retest. You can read that finding in full before you talk to us.
Read the finding→Assessment of AI agents that hold tools and act: tool-use boundaries, privilege containment, instruction-hierarchy integrity, and memory poisoning surfaces. The deliverable maps each capability an agent holds to the damage it could cause with it, with demonstrated findings where exploitation is possible and containment recommendations where it is not.
An objective-driven adversarial exercise against your AI system under agreed rules of engagement: jailbreak resistance, data extraction through model channels, and abuse of connected tools. The deliverable is a set of attack narratives with reproduction steps your engineers can rerun.
We publish our own AI governance: a three-state disclosure taxonomy and a named human owner for every consequential action. The assurance engagement builds the same discipline for your organization: an AI-use inventory, decision-rights policy, and the evidence trail your auditors and enterprise customers ask for.
Our own governance→Functional and regression testing for AI-backed features: handling nondeterminism, validating outputs against product intent, and catching regressions when models or prompts change. The discipline comes from the QE practice that tests the rest of your product.
The QE practice→Focused review of AI-generated and AI-assisted code paths: injection risks, secrets handling, dependency provenance, and the insecure patterns assistants repeat at scale. Findings arrive as demonstrated issues with recommended fixes, sized for a sprint.
A structured map of where AI touches your business, including the shadow AI nobody procured. The deliverable is an inventory, an exposure ranking, and a remediation sequence written for executives and engineers alike.
The delivery side, made concrete. Each platform states the AI capability plainly and shows it running.
Exposure validation
Findings are corroborated across engines and proven exploitable before your team sees them, across web, API, network, code, mobile, cloud, container, identity, and AI or LLM surfaces. What reaches your engineers' pager is proven risk.
Explore VirtueThreatX→Security operations
AI-driven detection on 2,250+ MITRE ATT&CK-mapped rules, behavioral analytics, and autonomous triage. The loop has run in production since June 2026 under analyst supervision, with containment always a human decision.
Explore VirtueShieldX→Quality engineering
AI-assisted test authoring and self-healing automation that validates, executes, and learns as the application changes, integrated with Jenkins, GitHub, GitLab, Azure DevOps, and Jira so release evidence stays current.
Explore VirtueATLAS→We publish where AI acts alone and where it stops. When your auditor or your customers ask how AI acts on your environment, that line is the answer you need.
No consequential action touches a live system without a named approver. Response playbooks can dry-run in simulated mode first, so you see exactly what an automated action would do before it happens.
In any of our platforms. Client data stays in per-tenant isolation, and the commitment is fixed in your service agreement rather than left to a policy page that can change after signature.
The five commitments and the exact answers your AI-governance questionnaire will ask for.
VirtueThreatX, VirtueShieldX, and VirtueATLAS: what each does, and how they close one assurance loop together.
Prompt injection demonstrated in an AI rewrite endpoint, then fixed and verified clean on retest.
A practical checklist for taking an AI feature to production, from our engineering practice.
A technical conversation about your AI features, the attack surface they add, and how to test them before someone else does.
The people who scope your assessment run it: the same security practice behind our published AI-endpoint case study.