Governance
AI governance. Built into the architecture.
Architecture scoped around your regulatory requirements and governance obligations.
AI introduces new ways for information to move, decisions to be made, and work to happen. Those possibilities need clear boundaries.
Askew AI helps your organization translate privacy requirements, security policies, and governance obligations into the systems you build and use. From connected knowledge to agents that take action, we design around what your AI should know, what it can do, and where people remain in control.
Fig. 01 / Governance, by design
- 01 / Allowed
- 02 / Human approved
- 03 / Denied
Policy into practice
Your obligations. Reflected in your systems.
A policy can define an expectation. Architecture determines how that expectation holds up in practice.
Who can access a sensitive document? What information reaches a model provider? Can an agent change a customer record without approval? What happens when someone leaves the company—or a provider changes its terms?
We work with your technical, security, legal, and operational teams to turn those questions into explicit requirements, implementation decisions, and assigned responsibilities.
Not every system needs the same controls. The design should reflect your data, your intended use, and the consequences of getting it wrong.
Scope of design
Considered at every layer.
Data privacy & lifecycle
Understand what information enters the system, where it travels, and how long it remains. We help define boundaries for collection, processing, storage, retention, and deletion—including prompts, retrieved documents, outputs, and logs.
- Data flows
- Purpose limitations
- Retention requirements
Identity & access
Connected knowledge should not become unrestricted knowledge. We design access around authenticated identities, defined roles, and the permissions of your source systems, with appropriate boundaries for users, agents, and administrators.
- Scoped permissions
- Source-system access
- Privileged actions
Model & provider selection
Provider choices carry data-handling implications. We help evaluate deployment options, processing locations, retention settings, training-use terms, and contractual requirements against your intended use. Those arrangements should be verified for the specific service and configuration—not assumed from a product label.
- Provider evaluation
- Processing locations
- Configuration review
Accountability & evidence
Make important decisions easier to explain and review. We help scope activity records, change histories, evaluation results, and control documentation while considering the sensitive information those records may themselves contain.
- Traceable changes
- Reviewable decisions
- Evidence requirements
Evaluation & ongoing governance
Governance does not end at launch. We help define checks for unauthorized disclosure, unreliable outputs, misuse, and failures of access or approval controls, alongside responsibilities for reviewing changes to models, tools, data sources, and permissions.
- Risk-based testing
- Change review
- Assigned ownership
Engagement
From requirements to a working design.
Understand the environment
Review the proposed use cases, connected systems, data sensitivity, existing policies, and applicable obligations with the people responsible for them.
Define the boundaries
Map data flows and trust boundaries. Identify gaps, agree on requirements, and distinguish what must be addressed before launch from what can be improved over time.
Design and implement
Translate agreed requirements into architecture and, where included in the engagement, implemented controls. Document dependencies, responsibilities, and the decisions behind the design.
Validate and hand over
Evaluate against agreed acceptance criteria. Record known limitations, unresolved risks, and the responsibilities your team will carry into operation.
Deliverables
A clear scope. A useful handover.
Depending on your engagement, deliverables may include:
- An AI use-case inventory and risk assessment.
- Data-flow and trust-boundary diagrams.
- A requirements-to-controls mapping with owners and dependencies.
- An access, agent-permissions, and human-approval design.
- A model-provider and deployment-options assessment.
- An evaluation plan, findings, and prioritized remediation roadmap.
- Implementation documentation and an operational handover.
The scope, evidence, and acceptance criteria are agreed before delivery. You know what is being assessed, what is being built, and what remains your responsibility.
Who it’s for
Designed with your teams. Not around them.
This service is for organizations introducing AI into sensitive workflows, connecting internal knowledge, deploying agents across business systems, or bringing existing AI experiments under clearer oversight.
We work alongside your internal specialists and external advisers. Where obligations such as GDPR, HIPAA, or the EU AI Act are relevant, your legal and compliance teams determine applicability and interpretation; we help translate their requirements into technical decisions.
What should your AI be allowed to do?
Start with the use case. We’ll help you identify the information, permissions, and responsibilities the architecture needs to account for.
Please keep initial inquiries high-level. We can arrange an appropriate channel before exchanging sensitive information.