SECURITY-FIRST DELIVERY
Security
Security-first AI systems: threat modeling, guardrails, and hardening for real-world inputs.
What we deliver
Security for modern AI systems is not a checklist — it’s architecture. We help teams ship systems that are resilient under adversarial and messy real-world inputs.
- Threat modeling and attack-surface mapping for AI + existing systems
- Prompt injection defense and safe tool-use authorization
- Data boundary design (PII/secrets redaction, minimization, retention)
- Logging and audit trails without leaking sensitive data
- Secure integration patterns for legacy systems and internal APIs
Typical engagements
- AI security review of an existing assistant/agent workflow
- Guardrails and policy enforcement for tool-use (allowlists, least privilege)
- Secure RAG and knowledge-system pipelines (document boundaries, retrieval validation, provenance)
- Hardening and monitoring: incidents, drift, abuse patterns, cost anomalies
How we work
- Map systems, data, and trust boundaries
- Threat model critical flows and tools
- Implement safe defaults and controls
- Validate with tests and “abuse cases”
- Monitor, iterate, and document
When this work is useful
Security work is appropriate before a new assistant receives sensitive context, when an agent can call business tools, or when a review cannot reconstruct what happened. Typical triggers are prompt injection findings, unclear trust boundaries, excessive service permissions, or logs that contain more data than the workflow needs. The goal is a system with explicit failure behaviour, not a promise that adversarial input disappears.
Delivery model and deliverables
We map actors, data flows, tools, providers, and egress paths. Threat scenarios are ranked by consequence and controllability. Controls are then implemented at the relevant boundary: input handling, retrieval, tool authorisation, output validation, secrets handling, and monitoring. Validation uses representative abuse cases as well as normal workflows. Deliverables may include an attack-surface map, threat model, allowlist and policy rules, redaction design, test catalogue, incident playbook, and review-ready documentation. See prompt-injection defense and data access governance.
Dependencies, limits, and acceptance
No control can be stronger than the identity, data classification, and ownership decisions beneath it. Provider changes, new tools, and unreviewed prompts remain change-management risks. Acceptance covers blocked high-risk paths, permitted workflows, log redaction, alert routing, and a human escalation path tested with abuse cases. Start a security readiness review.
Integration notes
Security controls should be placed where the decision can still be changed: before retrieval, before a tool call, before a sensitive value enters a log, and before an output triggers a side effect. This makes the control visible to tests and operators. Defence in depth remains useful, but duplicate controls should have different failure assumptions rather than merely repeating the same prompt instruction.
Related reference engagements