AI / Security / Engineering Responsibility
Our principles for building, reviewing and integrating AI-assisted systems. We focus on scoped authority, privacy, explicit human control and realistic testing—not claims that a model is always correct or safe.
Engineering doctrine / Not a certification
Tool permissions, backend authorization and production controls must remain effective when a model is mistaken, manipulated or unavailable. These are design standards we use to guide scoped work; a published principle isn't evidence that every system or external provider meets it.
Least privilege first.
Reasoning models don't replace server-side enforcement, approvals or auditability.
AI assistants and agents should operate inside explicit delegated permissions. Sensitive actions, transfers and irreversible operations need an appropriate human or systems-level approval path.
In practice: Define who authorizes the action, what tools may execute and how permissions can be withdrawn.
Avoid passing credentials, unnecessary personal information, confidential customer records or full production datasets into model contexts when a narrower representation would work.
In practice: Apply tenant isolation, scoped tool tokens, input redaction and data classification at system boundaries.
Describe the model provider, permitted data flows and where generated output may be incomplete or incorrect. Separate a prototype from a production-grade system.
In practice: Document external vendors, observed uncertainty, retention configurations and consent-sensitive features.
Treat prompt injection, tool misuse, unauthorized data access, unsafe automation and malformed model outputs as system-level security and reliability problems.
In practice: Use scenario-based evaluations, deterministic checks, human review and failure-mode testing before release.
Keep a record of consequential decisions where lawful and proportionate, without logging secrets or unnecessary private payloads. Define who can stop, review and correct the automation.
In practice: Trace tool calls, allow controlled rollback and assign incident and operational ownership.
Select automation based on a measurable engineering or customer need, not novelty. Prefer a deterministic implementation when it is simpler, safer or easier to verify.
In practice: Compare AI-assisted and non-AI approaches for reliability, cost, privacy and maintainability.
No universal promise of zero retention, model-training exclusion, data residency, encryption implementation or provider certification follows from this page. Actual data handling depends on the selected service, vendor settings, lawful basis and customer contract.
Avoid submitting confidential project files or secrets into a public demo. Any client-specific AI workflow needs its own threat model, data flow review, environment permissions and acceptance tests.
Send the data flow, proposed tools, principal identity model and potential side effects. We assess where authorization belongs before connecting the agent to live systems.
