OverlayIQFind the Policy Flaws
Users Will Exploit
Before They Do
Continuously test your promotions, incentives, and policy enforcement logic to uncover real-world abuse risks.
Your Systems Are Secure. Your Rules Are Not.
Most high-impact incidents don't come from technical vulnerabilities. They come from how systems are designed to work:
Incentives that can be repeatedly claimed
Single-use offers that fail under edge cases or multi-account scenarios.
Access rules that fail under edge cases
Policy enforcement that breaks across regions or integrations.
Policy changes that introduce silent regressions
Updates that inadvertently create new exploitation paths.
Enforcement logic that breaks across regions
Rules that work in one context but fail in another.
Traditional security and fraud tools don't test these scenarios.
OverlayIQ does.
Continuous Adversarial Testing for Business Logic
Human-Led Abuse Discovery
Researchers identify emerging ways users manipulate incentives and policies across industries.
Controlled Policy Simulation
The platform safely tests real user flows (promotions, onboarding, access controls, payments) against adversarial scenarios without impacting real users.
Actionable Risk Reports
Each issue includes location, exploit path, estimated impact, and remediation guidance.
Protecting Critical User Journeys
Promotion & Incentive Abuse
Scenario: Detecting logic flaws that allow repeated claims on single-use offers or farming loyalty points across multiple accounts.
Outcome prevented: Systemic financial losses and incentive program manipulation.
Age, Identity, and Access Policies
Scenario: Closing configuration flaws that allow deepfakes or virtual cameras to bypass required age and liveness checks.
Outcome prevented: COPPA/GDPR violations and safeguarding minors.
Content & Safety Enforcement
Scenario: Uncovering logic errors that allow users to bypass safety filters, access restricted content, or manipulate parental controls.
Outcome prevented: User trust erosion and reputational damage.
Policy Change Regression Testing
Scenario: Validating that policy updates don't inadvertently create new exploitation paths or break existing enforcement.
Outcome prevented: Silent regressions that introduce new vulnerabilities.
From Reactive Enforcement to Predictive Control
OverlayIQ identified a BNPL cashback loophole that allowed users to repeatedly claim promotional incentives across multiple accounts—the exact type of policy exploitation that cost major platforms millions in recent incidents.
— VP of Risk, FinTech Platform