AI CYBER VALIDATION
Validation, not assumption
Veync tests whether your AI governance, your guardrails and your deployed security controls hold under sustained autonomous adversarial pressure — and produces the evidence either way.
Autonomous adversary agents. Written authorization. Every finding linked to the action that proved it.
Frameworks and standards
Evidence mapped to the frameworks your board, your auditor and your regulator already use.
EU AI Act Regulation (EU) 2024/1689
European Union
NIS2 Directive (EU) 2022/2555
European Union
DORA Regulation (EU) 2022/2554
European Union
ISO/IEC 42001 2023
ISO/IEC
NIST AI RMF 1.0
NIST
MITRE ATT&CK
MITRE
MITRE ATLAS
MITRE
OWASP Top 10 for LLM Applications
OWASP
OWASP Top 10 for Agentic Applications
OWASP
The problem
Your security posture is an assumption until something tests it
Controls are bought, configured and attested. Very few are ever proved against a real adversary after the day they went live — and the AI layer has added an entire surface with no baseline at all.
01
Controls drift silently
A control that worked at deployment degrades through policy changes, upgrades, tuning and exclusions. Nothing announces the moment it stops detecting, so the gap surfaces during an incident instead of before one.
02
Attestation is not evidence
Questionnaires, architecture diagrams and coverage matrices describe intent. None of them demonstrate that a specific attacker action would be prevented, detected or missed in the environment you actually run.
03
AI outpaced assurance
Models, agents and tool chains now hold real permissions and act without a human in the path. Their failure modes are authorization and scope failures, which conventional testing was never built to exercise.
Veync closes that distance: an autonomous adversary running continuously, confined to an authorized scope, producing evidence that survives an auditor's reading.
Validation modules
Seven validation modules
Each module answers one question a board can ask and an auditor can follow.
01
AI Governance Assurance
Tests whether declared AI governance is enforced in the running system, not only documented.
02
Guardrail Integrity Testing
Measures whether model and agent guardrails hold under sustained adversarial pressure.
03
Agentic Risk and Authorization
Probes agent permissions, authorization boundaries and scope confinement for excessive agency.
04
Security Control Validation
Shows whether deployed control categories prevent, detect, alert on or miss real attacker actions.
05
Framework Alignment and Gap Analysis
Maps validation evidence to the frameworks you are accountable to, and names the gaps.
06
AI-Generated Code Security
Finds exploitable defects in AI-written code and its dependencies, with proof of exploitability.
07
Autonomous Offensive Operations
Assume-breach adversary emulation across the kill chain, under written rules of engagement.
The Veync Engine
An autonomous adversary, under a control plane you own
The engine plans, executes and verifies adversary actions, confines every one of them to an authorized scope, and records exactly what proved each result.
Autonomous adversary fabric
Planning, reconnaissance, exploitation and AI-layer agents that chain actions the way an operator would, rather than replaying a fixed scenario library.
Authorization control plane
Rules of engagement, scope enforcement, blast-radius limits and a kill switch sit above the agents. Nothing executes outside the boundary you signed.
Evidence graph
Every finding links to the exact executed action that proved it, so a verdict can be re-read, re-run and handed to an auditor without translation.
Continuous, not annual
Validation re-runs on every material change — a new agent, a new tool permission, a tuning adjustment — so the answer describes the system you are running today.
Veync operates only under written authorization and defined rules of engagement. Scope, boundaries and escalation paths are agreed before anything executes.
See how the engine worksWhere this differs
Four practices, and what each one actually proves
Continuous validation is not a faster penetration test, and it is not a scenario library. The distinction matters the moment a regulator asks what your evidence covers.
| Veync | Annual penetration testing | Breach-and-attack simulation | AI red-teaming tools | In-house red team | |
|---|---|---|---|---|---|
| Cadence | Continuous, and re-run on every material change | Once or twice a year, scoped in advance | Scheduled, from a fixed scenario library | Ad hoc, per prompt campaign | Bounded by team capacity |
| Adversary model | Autonomous agents that plan, execute and chain actions | Human testers inside a fixed window | Replay of already-known techniques | Prompt-level probing of a single model | Human operators against competing priorities |
| AI and agent coverage | Governance, guardrails, agent authorization and AI-written code | Rarely in scope | Not modelled | Model output only | Depends on in-house AI expertise |
| Evidence produced | Every finding linked to the executed action that proved it | A point-in-time report | Coverage percentages | Prompt transcripts | Internal write-ups |
| Cadence | Continuous, and re-run on every material change |
|---|---|
| Adversary model | Autonomous agents that plan, execute and chain actions |
| AI and agent coverage | Governance, guardrails, agent authorization and AI-written code |
| Evidence produced | Every finding linked to the executed action that proved it |
| Cadence | Once or twice a year, scoped in advance |
|---|---|
| Adversary model | Human testers inside a fixed window |
| AI and agent coverage | Rarely in scope |
| Evidence produced | A point-in-time report |
| Cadence | Scheduled, from a fixed scenario library |
|---|---|
| Adversary model | Replay of already-known techniques |
| AI and agent coverage | Not modelled |
| Evidence produced | Coverage percentages |
| Cadence | Ad hoc, per prompt campaign |
|---|---|
| Adversary model | Prompt-level probing of a single model |
| AI and agent coverage | Model output only |
| Evidence produced | Prompt transcripts |
| Cadence | Bounded by team capacity |
|---|---|
| Adversary model | Human operators against competing priorities |
| AI and agent coverage | Depends on in-house AI expertise |
| Evidence produced | Internal write-ups |
Platform scope
The platform at a glance
- Validation modules
- 7
- Core frameworks mapped
- 9
- Control categories exercised
- 12
- Findings linked to the action that proved them
- 100%
These figures describe the platform itself: the modules it runs, the frameworks its evidence maps to and the control categories it exercises. Customer results are shared under NDA, with their methodology.
Veync Labs
Original research, disclosed responsibly
Labs studies how AI guardrails, agent authorization models and detection pipelines fail, then publishes what defenders can act on.
Advisory identifiers
- VYN-2026-0001Under coordinated disclosure
- VYN-2026-0002Under coordinated disclosure
- VYN-2026-0003Under coordinated disclosure
Veync Labs researchers participate in coordinated vulnerability disclosure and bug-bounty programmes across leading AI platforms and model providers. Findings reach the affected party first, and are published only after a fix or an agreed disclosure date.
Visit Veync LabsQuestions
What teams ask first
Is this a penetration test?
No. A penetration test is a scoped human engagement inside a fixed window. Veync runs autonomous adversary agents against the live environment continuously, and re-runs them whenever the system changes.
Do you need access to production?
Veync operates under written authorization with an agreed scope. What that scope covers — production, pre-production, specific tenants, specific agents — is decided with you before anything executes, and it is enforced by the control plane rather than by convention.
Which security controls can you validate?
Control categories rather than named products: endpoint detection and response, network detection, email security, data loss prevention, web application firewalls, identity and multi-factor enforcement, AI guardrail and moderation layers, and operational or physical telemetry where it is in scope.
How is this different from a scenario library?
A library replays techniques someone has already written down. Veync's agents plan against your environment, chain actions and adapt when a step fails — which is where undetected paths are usually found.
What do we receive at the end?
An evidence set rather than a slide deck: a verdict per control, the executed action that produced it, the detection-engineering changes that would close each gap, and a mapping to the frameworks you report against.
Find out what your controls actually do
A validation briefing walks through your environment, the scope we would authorize, and the evidence you would receive.
Named-vendor results and control-evasion evidence are shared under NDA, through the evidence annex.