We assess your AI use case, identify security risks, and define the evidence and controls you need before deployment.
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Know which AI use cases merit investment and what to validate first.
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Get a scoped roadmap with priorities, costs, and clear next steps.
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Define AI controls, review owners, and evidence for accountable use.
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We help organizations evaluate AI use cases, uncover security gaps, and turn findings into practical decisions, scoped implementation, and an AI plan.
Organizations can use AI with clear ownership, reviewed risks, and defined controls. People remain accountable for the decisions.
We assess your AI use case, data flows and risks, then deliver findings and priorities for your next decision.
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We design AI access to your knowledge with defined permissions, source retrieval, and controls for your data.
We turn AI risk findings into clear controls, assigned owners, and review evidence, with implementation scoped to your systems and AI requirements.
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We help your team apply the agreed AI workflow, understand its limits, and follow review steps, with training tied to operational tasks.
Know which AI risks need action, who owns them and what evidence guides your decision.
Finance teams need to review how AI uses sensitive data, supports decisions and fits your existing controls.
We map the proposed AI workflow, examine data access and vendor boundaries, and document risks and control priorities so your team can decide what to adopt, restrict, or review before deployment.
Healthcare teams need to understand where AI processes patient information and what safeguards each workflow needs.
We review the AI use case, data handling, and access boundaries, then outline the security gaps and controls your team should evaluate before using the system.
Retail teams need AI workflows with precise data access rules.
We assess proposed AI workflows for customer support and operations, map data exposure, and define practical controls before your team expands the deployment.
Education teams need clear rules for AI use, sensitive data, and reviews of AI-generated work.
We assess the intended workflow, data access and review requirements, then help your institution define controls and training for accountable use by staff and students.
Manufacturers need to review how AI connects to operating systems, uses sensitive data and affects current controls.
We review the planned integration, identify data and access risks and define controls before implementation in the operating environment.
Public agencies need AI decisions guided by mission needs, data controls and documented evidence.
We review the use case, data sensitivity, and system boundaries and document risk findings, control gaps, and implementation priorities for review against your team requirements and mission.
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We assess a defined AI use case and its data, systems, and risks.
You receive documented findings, prioritized control gaps, and a practical roadmap for the next decision.
We agree the scope and deliverables before work begins. Implementation can follow under a separate, defined engagement.
Start with us when an AI decision involves sensitive information, operational workflows, or accountability.
The assessment is useful for teams considering new AI tools or reviewing an existing deployment.
We first consider your use case, decision owner, and evidence you need.
We connect your goal to the AI workflow, security risks, and governance requirements.
Your assessment provides a documented basis for decisions:
-A clear use case and agreed review scope
-Data flows, access boundaries, and vendor dependencies
-Risk findings, evidence gaps, and control priorities
-Implementation plan with owners and acceptance criteria
These outputs help your team decide where to invest and what to review before deployment.
Implementation can follow under a separate agreed scope.
Custom AI is a system configured around a defined business workflow and approved knowledge sources.
Depending on the use case, it may use retrieval, integrations, or model tuning.
We agree access controls, evaluation criteria, and deployment boundaries before implementation. Rights to data, code, configurations, and model components are specified in the agreed scope.
We review the agreed AI use case, system integrations, and data flows. We examine sensitivity, access permissions, vendor handling, and the controls currently in place.
The assessment records findings and unknowns, then prioritizes security gaps and recommended actions. If implementation is included in a separate scope, we define acceptance criteria and test the agreed controls. We document what was tested and the remaining limitations.
Each finding has an owner and a next action. Security also depends on ongoing operations and third-party changes, so the evidence should be reviewed when the system, data, or use case changes.
Book a scoping call and describe the AI decision you need to make. We discuss the business objective, proposed workflow, affected systems, data sensitivity, and your decision owner. You can begin with a plain-language description.
Share confidential records only after the engagement terms and an appropriate transfer method are agreed. We then define the assessment boundary, required inputs, deliverables, schedule, and fee in a written scope.
The review may cover data flows, access permissions, vendor handling, existing controls, and selected tests, according to the agreed use case.
Findings distinguish observed evidence, assumptions, unknowns, and recommended actions.
You receive a prioritized report and a roadmap your team can review before deciding whether to proceed with implementation.
The scope also identifies exclusions, third-party dependencies, and the limits of the assessment.
We discuss which findings need your internal security, business, or legal review and who owns the next decision.
Implementation, ongoing monitoring, and team training can be scoped separately where they are needed. The first step is a focused conversation about your AI problem and the evidence required to make a useful decision.
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