Expose weaknesses in AI systems before they can be exploited.
AI is increasingly deployed in high-stakes environments — but most systems are not tested for how they behave under stress, manipulation, or unexpected conditions. REVEAL helps teams understand what an AI model is truly learning, identify hidden vulnerabilities, and validate trust before deployment. It provides AI assurance where reliability and security matter most.
The Problem
AI systems can fail silently under real-world conditions
Adversaries can exploit hidden model weaknesses
Data poisoning and drift can degrade performance over time
Most AI validation stops at accuracy, not robustness
Leaders need trust and transparency before field deployment
What REVEAL Does
REVEAL evaluates AI/ML systems under adversarial stress to uncover vulnerabilities, failure modes, and reliability gaps before deployment.
It ensures AI systems behave as expected — even under attack or uncertainty.
What You Can Do With It
Reveal hidden weaknesses in AI models before field use
Test robustness against adversarial inputs and manipulation
Detect data poisoning and model drift over time
Understand why a model behaves the way it does
Build trust in AI systems supporting operational decisions
How It Works
1) Stress-Test — Apply controlled perturbations and adversarial conditions.
2) Measure — Observe how model outputs shift under pressure.
3) Assure — Generate clear resilience and trust assessments for deployment.
Key Features
AI vulnerability and robustness evaluation
Adversarial stress testing without privileged model access
Drift and poisoning detection over time
Operational trust assessment and reporting
Integrates directly with Digital Twin environments
Typical Use Cases
Defense AI systems: validate reliability in contested environments
Industrial automation: ensure models remain safe over time
Mission-critical decision support: prevent silent failure
AI transition: move models from lab to operational deployment
Secure AI programs: certify trust before fielding
What Success Looks Like
AI models tested beyond simple accuracy
Early discovery of vulnerabilities before exploitation
Increased confidence in deployment decisions
Stronger resilience against drift, poisoning, and attack
Trusted AI integration into real operation
REVEAL
Empowering Cyber Operators: Exploit AI's Hidden Weaknesses through Algorithm Fingerprinting and Targeted Data Poisoning
REVERSE ENGINEERING AND VULNERABILITY ELUCIDATION OF ALGORITHMS (REVEAL) system focuses on reverse engineering and characterizing AI/ML algorithms' learning mechanisms, particularly addressing security concerns such as adversarial attacks, model drift, and data poisoning. This system provides a systematic, generalizable approach to assessing AI/ML algorithms' robustness and identifying vulnerabilities.