Security & data exposure
Prompt injection, sensitive-data leakage, instruction bypass, retrieval boundaries, and unauthorized access paths.
AI assurance • red teaming • governance
Don't deploy AI on reputation alone.
YW2 evaluates AI applications, models, agents, and AI-enabled workflows for security, reliability, data exposure, adversarial behavior, bias, operational risk, and governance readiness.
The real question
What happens when the system fails?
A respected model provider does not eliminate application risk. AI can still leak information, follow malicious instructions, hallucinate, misuse connected tools, or behave differently across languages and user groups.
YW2 tests the deployed system—not just the logo on the model.
Reliability & accuracy
Whether outputs are consistent, supportable, and fit for the actual business task rather than a generic benchmark.
Agents, tools & permissions
Whether AI-enabled actions, APIs, internal tools, and automation privileges are appropriately constrained.
Bias & multilingual behavior
Material changes across language, phrasing, geography, demographic context, or other inputs relevant to the use case.
Governance & accountability
Ownership, human oversight, logging, escalation, acceptable use, evidence, and clearly defined deployment boundaries.
Adversarial behavior
What happens under conflicting, malicious, encoded, indirect, or unexpected instructions and inputs.
01
Start with one system and one real use case.
The first engagement is intentionally concrete: define the AI system, who uses it, what it is expected to do, and what failure would cost.
- Map models, prompts, RAG sources, integrations, users, permissions, and decision points.
- Identify the highest-consequence failure modes before selecting tests.
- Keep the scope tied to the deployed workflow rather than abstract AI risk.
02
Challenge the system with repeatable tests.
YW2 combines functional evaluation with adversarial testing to determine where expected behavior breaks down.
- Document test conditions, inputs, outputs, and reproducible failures.
- Evaluate model behavior together with application controls and authorization boundaries.
- Compare multiple models against the same methodology when model selection is part of the decision.
03
Turn findings into decisions.
The deliverable is evidence a technical team, security team, executive, or risk owner can actually use.
- Executive risk summary and prioritized technical findings.
- Reproduction steps, impact assessment, and remediation recommendations where appropriate.
- Retesting of critical findings after changes are made.
Evidence over origin
Nationality, brand, and popularity are not substitutes for measurement.
Model provenance, jurisdiction, hosting location, supply chain, and data-handling practices can be legitimate risk factors. YW2 separates those considerations from measured technical behavior so organizations can make decisions from evidence instead of assumptions.
Layer 04GovernanceOwnership, oversight, policy, evidence
Layer 03ApplicationPrompts, RAG, agents, permissions
Layer 02Model behaviorReliability, bias, adversarial response
Layer 01Data & infrastructureAccess, privacy, logging, boundaries
AI Assurance Sprint
A practical first engagement.
Start small enough to finish, document the evidence, and use what is learned to decide whether broader governance or continuous evaluation is justified.
Map
Document the system, intended outcome, users, data, integrations, permissions, and operating environment.
Threat model
Define realistic misuse, failure, security, reliability, and governance scenarios for the use case.
Evaluate
Run repeatable functional and adversarial tests against the highest-priority risks.
Report & retest
Prioritize findings, recommend next actions, and verify critical fixes when requested.
Strong-fit engagements
AI systems where failure has a real consequence.
Challenge AI before customers do.
Internal assistants, customer-facing AI, RAG systems, agents, automation, and AI-enabled products that need structured evaluation before wider deployment.
- Pre-production assessment
- Model comparison
- Post-remediation retesting
Produce evidence for accountable deployment.
Organizations and contracting teams in the U.S., Taiwan, and other international markets that need stronger documentation around AI security, reliability, governance, or technical evaluation.
- SBA-certified SDVOSB partner
- Framework-aware evaluation
- U.S. and international collaboration
Boundaries
Assurance is evidence—not a rubber stamp.
YW2 structures this work around measurable technical evidence and clear limits on what an assessment means.
What is AI assurance?+
AI assurance is evidence-driven evaluation of an AI-enabled system against defined security, reliability, data, operational, and governance risks before or during deployment.
Does YW2 certify AI systems?+
No. YW2 provides technical evaluation, red teaming, governance-readiness analysis, and documented evidence. Framework mapping does not constitute legal advice, regulatory approval, or accredited certification.
Can YW2 compare multiple AI models?+
Yes. Where practical, competing models can be evaluated against the same use-case-specific methodology so the decision is based on measured behavior rather than provider reputation alone.
TEST BEFORE TRUST
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