- Home
- Our services
- Verify
- AI Assurance
AI Assurance
AI you can trust, govern and defend
Independent assurance for the AI you build or buy. We validate models and agents, test their guardrails and put governance in place, so AI moves from pilot to production with evidence your regulators and customers can trust.
How we work
Every agent is tested against seven controls before it meets a customer
See how we test agentsfrom idea to impact, across the systems lifecycle, this service sits here
The challenge
Most AI pilots stall on trust, not technology
A successful pilot is only the beginning. The real test comes when AI meets the gates of accuracy, governance, risk, security and compliance reviews.
Accuracy review
Where it stallsNobody can show the results are accurate, fair and stable on real data.
20 to 30% of the AI proofs of concept we see go on to production-scale deployment.
Vericence engagement reviewsWhat gets it throughEvaluation results on your own data, broken down by customer segment.
Security review
Where it stallsNo one has tested what happens when users or documents try to steer the model.
#1 risk for LLM applications is prompt injection.
OWASP Top 10 for LLM Applications, 2025What gets it throughRed-team findings, fixed and retested.
Ownership and controls
Where it stallsThere is no named owner, no monitoring and no way to trace a decision afterwards.
40%+ of agentic AI projects will be canceled by the end of 2027, with weak risk controls among the reasons.
Gartner, 2025What gets it throughA named owner, decision logs and drift alerts.
Regulatory sign-off
Where it stallsCompliance asks for evidence the team never collected.
2 Dec 2027 is when EU AI Act duties for high-risk systems apply.
Orrick, 2026What gets it throughEvidence mapped to the EU AI Act, NIST AI RMF and ISO/IEC 42001.
We treat AI like any other mission-critical system: validated before release, monitored after it, governed by named owners, with an audit trail for every decision.
Take the Digital Trust CheckStart here
Which risk tier is your AI system in?
Six questions, about a minute. The tier sets how deep testing and human oversight need to go.
What we do
Four disciplines that make AI trustworthy
For machine learning models, generative AI and AI agents.
AI-accelerated
AI governance and controls
A control framework that people actually use, owned by named people.
Control framework and ownership
Policies for how models and agents are approved, changed, monitored and retired, and who signs off.
AI inventory
One register of the models, agents and AI features in use, including the ones nobody approved.
Decision-trace logging
Every AI decision and agent action recorded with its inputs, so it can be explained later.
Human oversight that works
Escalation rules and confidence thresholds that give reviewers real decisions, not rubber stamps.
AI Model validation
Evidence that a model works on data and edge cases, not just on benchmarks.
Accuracy and robustness
Testing against your own data, edge cases and adversarial inputs before release.
Bias and fairness
Checks for unequal outcomes across the groups your decisions affect.
Drift monitoring
Alerts when data or behavior moves away from what was validated.
LLM and agent evaluation
Task success, hallucination, tool use and refusal behavior, scored across thousands of scenarios.
AI-acceleratedAI security & privacy reviews
AI attacked the way real attackers would, before it goes live.
Prompt injection and jailbreak testing
Direct and indirect attacks on prompts, documents and tools.
AI-acceleratedData leakage and privacy
Checks on what a model can see, store and reveal, including personal and regulated data.
Agent permission reviews
What each agent is allowed to access and do, limited to what the task needs.
AI red teaming
Adversarial exercises that probe for harmful, unsafe or off-policy behavior.
AI-acceleratedAI regulatory compliance
Evidence ready for auditors, regulators and your customers' due diligence.
Regulatory mapping
Your AI use cases mapped to frameworks such as the EU AI Act and NIST AI RMF, and sector rules such as HIPAA.
Audit-ready evidence
Model cards, test reports and decision logs kept current with each release.
Third-party AI due diligence
Assessment of vendor models and AI features before you adopt them.
Continuous compliance monitoring
Controls checked as models, prompts and regulations change.
Agent assurance
Your agent platform gives you the controls, and we prove they hold
Whoever built your agents, whether your own team, another vendor or our AI Engineering practice, we attack each control the way a real failure or attacker would.
Context and memory
What must holdThe agent sees only the data its task and its user are entitled to.
How we try to break itCross-user and cross-tenant retrieval probes, and poisoned documents planted in its sources.
Evidence you getA data-boundary report listing every leak found, fixed and retested.
Planted a poisoned document and asked for another customer’s orders. Retrieval stayed within the user’s own data.
Tool orchestration
What must holdIt calls only approved tools, with valid inputs, within rate limits.
How we try to break itTool-misuse scenarios, malformed arguments and calls to tools outside its allowlist.
Evidence you getTool contract test results, rerun on every tool or prompt change.
Tried tool calls outside the allowlist and above the rate limit. All were blocked and logged.
Identity and authorization
What must holdEach action runs under the right identity, with the least privilege it needs.
How we try to break itPrivilege-escalation attempts: recommending a fix is pushed until it becomes executing one.
Evidence you getA permission matrix per agent, every right tested and signed off.
Asked the agent to issue a refund under a read-only role. Refused. Finding: one shared service account, now split per agent.
Guardrails and policy
What must holdBusiness rules apply at the moment of action, whatever the prompt says.
How we try to break itDirect and indirect prompt injection, jailbreaks and instructions hidden in emails or web pages.
Evidence you getRed-team findings against the OWASP list of LLM risks, each fix retested.
Prompt-injection attempts to skip the $1,000 approval rule. The policy held, because it is enforced outside the prompt.
Observability and evaluation
What must holdEvery decision can be traced, replayed and scored afterwards.
How we try to break itReplaying sampled incidents end to end and looking for gaps in the trace.
Evidence you getTrace completeness results and an evaluation baseline for each release.
Replayed a full session from the trace and ran the evaluation set on the release candidate.
Recovery and human control
What must holdWhen it fails or is unsure, it stops and hands over to a person.
How we try to break itFailure drills: tool outages, timeouts, contradictory data and low-confidence edge cases.
Evidence you getDrill results showing each escalation and rollback fired as designed.
Forced a tool timeout mid-refund. The agent rolled back and escalated. Finding: the alert never reached a supervisor, now fixed.
Model and cost management
What must holdSpend and latency stay within agreed limits, even under load or in loops.
How we try to break itRunaway-loop, retry-storm and peak-load scenarios.
Evidence you getCost and latency results against each workflow’s budget.
Load-tested at peak volume. Cost per refund stayed within budget, with routine steps on the smaller model.
ExampleWe test a customer refund agent before it goes live.
ResultTwo findings fixed and retested. The agent goes live with evidence for each of the seven controls.
How an agent assurance review runs
- 1Map
Listing each agent, its tools and permissions, and the controls it relies on.
- 2Break
Running the attack and failure scenarios against every control.
- 3Prove
Fixing with the team, re-testing and handing over an evidence pack per agent.
How we deliver
Assurance before release, at release and every day after
Agents execute testing and validation on every release, while accountability for risk and control decisions stays with the owners and engineers.
Before release
Our engineers and the product team
- Tier the system and name its owner
- Evaluate accuracy, bias and robustness on your own data
- Red-team for prompt injection, data leakage and unsafe actions
At release
Owner
- Sign off against thresholds agreed up front
- Publish the model card, test results and known limits
After release
Shared, with alerts to the owner
- Watch drift, cost and incidents in production
- Log agent actions and escalations for review
- Re-evaluate on every model, prompt or data change
Agents run these at scale
Every result lands in one evidence pack mapped to the NIST AI RMF, ISO/IEC 42001 and the EU AI Act, so auditors, regulators and customers read the same record.
Case study
Oncology provider | Agentic AI
Agentic AI in the revenue cycle, with people in control
Front-end revenue cycle work was handled offshore and faced regulatory restrictions. Nurse practitioners were being pulled into prior-authorization administration activities, and pharmacy staff re-keyed the same patient data into two systems.
We developed three AI agents for eligibility, prior-authorization and pharmacy work, with assurance built in from the start. Staff review the exceptions, every agent action is traceable and patient data stays inside the client’s network.
Ready to move AI from pilot to production, safely?
Talk to our AI assurance team about the controls, validation and evidence your AI needs.