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From AI risk to
a defensible
deployment decision

  1. Use case
  2. Risk
  3. Controls & guardrails
  4. Test & evaluation
  5. Evidence
  6. Assurance
  7. Deployment decision

AI Assurance

Prove your AI isready for real business.

Don’t just test your AI. Build the evidence needed to determine whether a specific AI use case is ready to operate in its intended business context.

Validate an AI Use Case See How It Works

Reduce risk

Lower AI risk exposure

Accelerate time to value

Deploy with confidence

Make defensible decisions

Evidence-backed

Build stakeholder trust

Customers, regulators, partners

Trusted by forward-thinking organizations

The Big Idea

A high-performing AI system can still be unsafe to deploy.

Accuracy and benchmark scores are important, but they don’t tell the whole story. Before deployment, organizations need to understand the use case, identify material risks, establish safeguards and determine whether the available evidence supports the decision.

AI Performance

“Does the model work?”

  • Accuracy
  • Benchmark scores
  • Evaluation metrics
  • Model results

AI Assurance

“Can we rely on this AI use case?”

  • Risk & impact
  • Controls & guardrails
  • Human oversight
  • Evidence
  • Control effectiveness
  • Residual risk
  • Deployment conditions

What CognitiveView validates

Validate the AI use caseNot just the model.

A comprehensive, evidence-driven validation of your AI use case in its real business context.

  1. Use Case & Context

    What does it do? Who is affected? What decisions can it influence?

  2. AI Risk & Impact

    What can go wrong? What is the impact? Which risks are material?

  3. Controls & Guardrails

    What safeguards are required? Are they defined, owned and implemented?

  4. Test & Evaluation

    Are the right behaviours, failure modes and metrics being tested?

  5. Evidence

    Can the required controls be demonstrated to operate?

  6. Assurance

    Is the evidence sufficient to establish confidence in the AI use case?

The CognitiveView assurance process

From initial assessment to ongoing assurance.

A structured, end-to-end approach that turns AI risk into a defensible deployment decision.

Explore the Assurance Process

  1. Register

    Know the AI and use case

  2. Assess

    AI readiness and risks

  3. Govern

    Policies and controls

  4. Prove

    Test and collect evidence

  5. Authorize

    Ready / Conditional / Not Ready

  6. Assure

    Control, evidence, effectiveness

  7. Monitor

    Keep assurance current

  8. Refresh

    Revalidate after material changes

CognitiveView Mortgage Underwriting Assistant dashboard showing assurance score 85, Medium risk, 18 of 18 controls, 96% evidence coverage, and Ready with Conditions.

The Decision

What should happen next?

CognitiveView provides the evidence. The organization makes the authorization decision.

Ready

Material risks are adequately addressed and evidence supports deployment.

Ready with Conditions

Deployment may proceed subject to specified safeguards, monitoring or time-bound remediation.

Not Ready

Material risks, control gaps or evidence deficiencies prevent a supportable deployment.

When should you validate?

The earlier you validate, the faster and safer you can scale.

Validate at the moments that matter, from your first pilot to changes in a live AI system.

  • Before pilot

    Validate an AI use case before moving to production.

  • Before customer deployment

    Validate AI entering a customer-facing process.

  • Before agent action

    Validate an agent before it can access tools or take actions.

  • Before material decisions

    When AI influences material business or customer decisions.

  • When evidence is required

    By risk teams, regulators, procurement or customers.

  • After a material change

    Model, agent, data, prompt, architecture or use-case change.

Real AI use cases & industries

Assurance depends on context.

The same AI technology can present very different risks depending on how it is used.

Explore AI Use Cases
A business professional overlooking a city skyline at sunset, representing innovation becoming trusted business impact

From innovation
to impact.
With confidence.

Framework & regulatory alignment

One assurance model. Multiple requirements.

  1. Requirements
  2. Controls
  3. Evaluations
  4. Evidence
  5. Assurance
Explore Frameworks

Continuous assurance

Assurance doesn’t end at deployment.

AI systems change. CognitiveView continuously monitors, detects changes and keeps assurance current.

  1. Deploy

    Move the validated AI use case into production.

  2. Monitor

    Track behavior, performance and control signals.

  3. Change detected

    Identify changes that could affect the deployment decision.

  4. Reassess

    Review the impact on risks and existing safeguards.

  5. Re-test

    Run targeted evaluations against the updated use case.

  6. Update evidence

    Collect current results and maintain a traceable record.

  7. Refresh assurance

    Confirm whether deployment conditions remain supported.

What triggers reassessment

  • Model change
  • Agent change
  • Data change
  • Prompt change
  • Architecture change
  • Use case change
  • Regulatory change

Business value

More than a checklist.A foundation for responsible AI.

  • Deploy with confidence: AI assurance business benefit

    Deploy with confidence

    Make informed, defensible decisions.

  • Reduce risk: AI assurance business benefit

    Reduce risk

    Identify and mitigate issues early.

  • Accelerate time to value: AI assurance business benefit

    Accelerate time to value

    Move from pilot to production faster.

  • Build stakeholder trust: AI assurance business benefit

    Build stakeholder trust

    Demonstrate responsible AI to customers, regulators and partners.