Cybersecurity
AI security architecture
Before each team builds its own assistant with its own keys, decide how AI features connect to models, data and tools. A shared architecture gives you one place to enforce policy, log activity and control cost.
The problem this service solves
Without a shared pattern, every team picks a provider, stores an API key in a repository and sends whatever data is convenient. Security finds out after launch. Costs and incidents have no single owner.
We design the paved road: a model gateway with authentication and logging, rules on data that may be sent to which provider, retrieval patterns that respect permissions, tool-calling patterns with approval, and a lightweight review process that product teams will use.
What is in scope
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Model access
Gateway or proxy pattern, key management, provider selection criteria and data retention terms review.
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Data boundaries
Which data classes may be sent where, redaction, and handling of customer and personal data.
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Identity and authorization
User propagation, service identities and per-user retrieval.
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Tool and agent patterns
Approved patterns for tool calling, human approval and sandboxed execution.
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Evaluation and testing
Pre-release security testing, regression tests and red team cadence.
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Monitoring
Logs, alerts for abuse and cost anomalies, and incident procedures for AI misuse.
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Governance
Intake process, risk tiers, review checklists and records, mapped to NIST AI RMF.
How the work runs
Discover
See current use.
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1
AI inventory
List AI features, models, providers, data and owners, including shadow use.
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2
Requirements
Gather legal, customer and business constraints.
Design
Define the pattern.
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3
Reference architecture
Draw the gateway, data and tool patterns.
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4
Policy and process
Write short policies and a review workflow.
Enable
Help teams adopt it.
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5
Pilot
Apply the pattern to one or two projects.
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6
Guidance
Publish checklists and examples.
What you receive
- AI use inventory
- Reference architecture
- Data handling rules
- Policy set and review workflow
- Testing and monitoring plan
- Pilot results
Tooling and technique
Examples of patterns and components. We design around your platforms.
Standards we map to
Mapping findings to a framework is not the same as certification against it.
Before we start
What we need from you
- Interviews with AI project owners
- Current provider contracts or terms
- Legal and privacy input on data use
What this service is not
- Legal opinion on AI regulation
- Certification to an AI management standard
What changes afterwards
- Teams build on an approved pattern
- Security sees AI use and cost in one place
- Policy matches what is actually enforced
Questions people ask
Do you advise on AI regulation?
We map technical controls to frameworks such as NIST AI RMF. Legal obligations under specific laws are for your counsel.
Should we block public AI tools?
That is a policy decision. We help you offer a sanctioned alternative and monitor, since blocking alone tends to push use out of sight.
Related
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AI agent security
Review agents that plan and call tools: identity, tool permissions, approvals, memory, isolation and audit.
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RAG security
Review retrieval-augmented generation: document-level authorization, poisoning, embedding exposure and leakage through answers.
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Generative AI
Enterprise copilots, retrieval-augmented generation, document processing and agent workflows, designed with permissions, evaluation and security from the start.
Need to understand your security exposure?
Speak with a security expert
Talk through your environment and goals. We reply to scope the work before anything is tested.
Speak with a security expertPlan an assessment
Answer a few questions and get a structured preliminary scope you can send us.
Use the plannerUnder attack right now?
Call the US office and tell us what you are seeing. Do not wait for a form reply.
Incident response