Know Your AI
Is Safe, With Proof.
Most organizations cannot prove their AI is secure, compliant, or ready for production-leaving them exposed to unexpected business, security, and compliance risks.
We continuously test your AI, measure real-world risk, and provide clear evidence of what is safe, what needs attention, and what to fix first.
attack impact
Data integrity
Pass
Fairness / bias
Review
Security / robustness
Fail
GovernanceReviewAI Is Moving Faster Than AI Security
Companies are adopting AI quickly, but many still cannot prove that it is safe, compliant, or under control.
Most AI audits are still completed only once and rely heavily on manual work. As soon as the model, data, or system changes, the results may no longer be reliable.
Continuous testing helps businesses identify security, compliance, and operational risks before they become costly incidents.
Why AI Still Creates Business Risk
Even organizations with AI policies often lack the tools to know if their AI is actually safe.
Your AI has never been tested the way a real attacker would. Hidden vulnerabilities remain until they're exploited.
When customers, leadership, or regulators ask for evidence, you have reports and opinions-but not continuous proof.
A one-time assessment becomes outdated as soon as your AI, prompts, or data change.
Without a consistent score, it's difficult to prioritize issues, track progress, or demonstrate improvement over time.
EnGenious closes all four – automated, continuous, evidence-based AI cybersecurity, not a one-off manual review.
The Full Agentic AI Stack
We test every layer, then turn each weakness into a concrete, prioritized fix. A consistent score – not subjective review – just tells you which fixes matter most, organized into five executive-readable domains.
Lineage, quality, drift, and privacy across the full data lifecycle – the foundation most failures actually trace back to.
Quality, completeness & representativenessData-leakage detectionTraining-vs-production parityLabeling accuracy and consistencyDistribution mismatch & driftPrivacy exposure riskAI That Continuously Learns to Find New Risks
Our autonomous security agent thinks and acts like a real attacker. It explores your AI, tries different attack strategies, verifies what it finds, and learns from every assessment.
Instead of running the same predefined tests, it continuously adapts to changes in your AI-finding new security, business, and compliance risks as they emerge.
This means every assessment becomes smarter than the last, helping you stay protected as your AI evolves.
Attack
Judge
ReinforceTests Built Around Your AI’s Weak Spots
Static evaluation turns risks discovered during testing into dedicated coverage for your AI system’s specific security, compliance, and business requirements.
These prepared tests run whenever your model, prompts, or data change-helping you catch issues early and confirm that previously identified weaknesses do not return.
Promptfoo
DeepEval
CI regression gate
Your Audit, On One Live Dashboard
Both engines feed one continuously re-scored view – with its own posture score, pillar breakdown, and generated insights. This is what your team logs into.
Assurance· Acme AI PlatformLive · re-scored 2 min ago
Generated insights
Security posture dropped 6 pts this week – driven by a new multi-turn jailbreak on the checkout agent.
1 static requirement regressed after the last model swap: data-exfiltration guard now bypassable.
Highest-yield attack surface: tool-call manipulation on agents with write access to production APIs.Your AI System, Scored On AIVSS – With Evidence Of Complexity And Attack Directions
Scoring isn't the point – safe AI is. AIVSS just measures how far different attacks could carry through your system so we can rank the fixes: CVSS says how bad the bug is, AARS says how far an agent can take it. Every score maps to a mitigation.
Audits Go Stale. Automated Ones Don't.
Because evaluation is automated, findings are continuously re-scored as models, data, and agents evolve – catching drift before it reaches production.
Real Engagements. Root Cause Below The Model.
Four engagements from production systems – each shows the failure sat below the model layer, and the numbers that moved once it was fixed.
Broken data pipeline → low fact accuracy
Inconsistent, incorrect answers despite a strong LLM.
No clear data lineage across ingestion pipelines
Inconsistent preprocessing across sources
Training data didn't match production distributionBiased, incomplete evaluation dataset
Reconstructed the full pipeline & mapped transformation mismatches
Validated training-vs-production alignment
Redesigned the evaluation dataset with realistic scenariosIntroduced data-quality gates & validation checks
94%4%Real Agentic Attacks, In The Attacker's Own Words
Five failure modes from production-style agents – a chatbot, a recruiter, a finance agent, a support bot, a sales agent – each scored on the same AIVSS scale.
Retail chatbot
The attacker sends
Four Artifacts, One Source Of Truth
All generated from the same scored evaluation data – so leadership and engineering work from one consistent picture.
Business-risk translation of every finding, organized by the Five Pillars. Built for board-level decisions.
Sequenced actions – quick wins, structural fixes, long-term investments – ranked by effort and impact.
Layer-by-layer analysis with evidence: failing pipelines, misrouted agents, retrieval misses, adversarial exposures.
Vendor-neutral guidance across data, model, retrieval, agents, evaluation, and governance.
We audit the system. We don't sell the fix.
No stake in which model, framework, or cloud you chose.
Like a cybersecurity firm retained to test, not build.
The full agentic stack – not only the model layer.
From First Conversation To Validated Pilot
Four clear stages take you from first call to a proven engagement – with zero ambiguity about scope, environment, or confidentiality at each step.
Understand your AI footprint and where assurance fits your priorities. Execute a mutual NDA so methodology, pricing, and structure can be shared openly.
45–60 minutesDefine the audit scope, map infrastructure across AWS, Azure, or GCP, and identify every RAG entry point where untrusted input can reach the model.
1–2 weeksProvision a dedicated, case-by-case environment that mirrors your target architecture and security controls – so testing is realistic without touching production.
3–7 business daysRun an initial audit together on one real system. This validates the methodology and establishes a repeatable partnership for future engagements.
3–6 weeks · scope-dependentKNOW HOW SAFE YOUR AI REALLY IS
Tell us what your AI does and where it runs. We'll show you exactly where it's exposed – and hand you a prioritized plan to make it safe. 90% of businesses have already had an AI incident; find out where you stand before an attacker does.
