Adversarial Decision Engineering

Move beyond static testing frameworks. Adversaia™ executes reproducible, version-controlled adversarial evaluations against your decisioning stack.

Map Control Gaps to remediation patterns.
Quantify Coverage across regulatory decision points.
Generate Audit-Grade Evidence without production access.

What Adversaia™ is

Adversaia™ (Ad-verz-A-I) is a sandbox-only adversarial evaluation platform purpose-built for institutions managing high-consequence decision systems under heightened regulatory scrutiny. It stress-tests decisioning architectures (rule engines, ML models, graph detectors, agent policy guardrails) under realistic, evolving threat vectors—isolation layers fully intact. No production dependencies. No live PII. No secrets in flight.

The platform operationalizes adversarial testing: scenarios become versioned artifacts, findings map to root causes and patch patterns, coverage becomes measurable, and resilience becomes defensible.

What you get

Scenario Packs

Version-controlled, auditable attack taxonomies targeting known and emerging attack patterns (synthetic identity, application fraud, mule networks, account takeover, collusion, RAG-steering in agentic systems).

Reproducible Runs

Deterministic execution with immutable identifiers; supports manual triggers, scheduled cadences (nightly/weekly for drift detection), and API-driven deployment integration.

Decision Lineage

Full traceability—signal provenance, rule/model evaluation paths, threshold application, approval gate routing—in both human-readable and JSON audit formats.

Control Contracts

Explicit specification of required signals, mandatory checks, approval gates, and latency SLAs so coverage becomes provable rather than assumed.

Coverage Mapping

Matrix views connecting scenarios to contracts; identifies silent failure modes (missing signal sources, absent detectors, bypassed gates) before attackers exploit them.

Regression Monitoring

Alerts and dashboards tracking resilience drift; configurable thresholds for score degradation, contract violations, and decision-point flips.

How it works

1

Define Decision Contracts

Specify which signals must be present, which detectors (rules/ML/graph/policy) must execute, what actions are permitted, required approvals, and latency bounds for each critical decision point (onboarding, KYC, payout, dispute, agent tool execution, etc.).

2

Construct Scenario Packs

Assemble attack sequences targeting contract requirements: synthetic identities failing consistency checks, velocity-based mule rings, account takeover chains, agent jailbreaks, RAG prompt injections. Mix complexity levels to validate both foundational and sophisticated controls.

3

Execute Runs

Run packs against your decisioning stack snapshot in sandbox. Capture signal availability, rule outputs, model scores, graph signals, agent policy evaluations, and approval outcomes. Generate immutable run records with configurable resilience scoring bands.

4

Analyze Findings

Map each failure to root cause (missing signal, weak threshold, rule gap, model blind spot, policy constraint violation, approval bypass). Link to patch patterns (signal enrichment, detector hardening, rule addition, approval gate tightening, agent constraint enforcement).

5

Track Coverage & Regression

Monitor coverage heatmaps quarterly. Trigger alerts on score degradation, contract violations, or high-severity scenario flips. Validate fixes with retests pinned to specific pack versions.

Trust & Boundaries

Sandbox-only execution. No production connectors. No live customer PII. No institutional secrets required. Tenant isolation with encrypted evidence artifacts—fully customer-owned and exportable. Compliant with data governance frameworks (zero exfiltration risk, audit logs, access controls).

Ready to validate resilience on your workflows?