Playbook: IT & Operations — Integration, Security, and SRE for AI

Practical guidance and checklists that help IT, platform, security, and SRE teams integrate, run, and support AI services without creating new operational or security debt.

Why this playbook matters

AI projects often start as experiments: a data scientist wires up a model, a product team runs a prototype, or a vendor supplies an API. Those early gains can be lost when services reach real users unless IT and operations teams design secure integrations, clear identity and access controls, cost governance, and SRE patterns from the start. This playbook focuses on the operational work that makes AI services dependable, auditable, and maintainable.

Who benefits

This resource is for platform engineers, IT managers, SREs, security teams, cloud architects, managed‑service providers, and technology leaders in small and mid‑sized companies through large enterprises (including healthcare, education, manufacturing, and nonprofits) who must integrate or operate AI services safely and reliably.

What you'll understand and be able to do

After using this playbook you will be able to:

• Choose safe integration patterns for third‑party and internal AI services (gateway, sidecar, proxy, internal API patterns).
• Design identity, authentication, and least‑privilege authorization for models, service accounts, and human users.
• Apply runtime controls: rate limits, batching, circuit breakers, cost attribution, and quota policies.
• Define observability and telemetry for model performance, inference latency, errors, and data drift.
• Establish SRE artifacts: SLOs, runbooks, incident playbooks, rollback plans, and capacity guidelines.
• Create handoffs and change control between data scientists, ML engineers, IT, and product teams so deployments remain supportable.

Practical examples

Real‑world scenarios you can adapt:

• A regional health clinic exposing a symptom‑triage assistant through an internal API with mTLS, scoped service accounts, and logging that separates PHI from telemetry.
• An e‑commerce site routing customer chat through an LLM via a gateway that enforces rate limits, caches responses, and attaches tenant metadata for billing.
• A manufacturing plant introducing a predictive‑maintenance agent with SLOs, anomaly alerts tied to the on‑call rota, and calculated cost‑per‑inference for budgeting.

How to use this playbook

Start by running the included API Security & Operational Controls Checklist to discover immediate gaps. Use the checklist results to prioritize: secure identity and least privilege, instrument observability, set SLOs and alerts, and build simple runbooks for common failures. Treat the playbook as a living set of controls that must be tailored to your tech stack, regulatory constraints, and organizational roles.

Platform opportunities and next steps

If you maintain an internal Hunger Engine or adopt a domain copy, consider converting key checklists and runbooks into interactive forms and saved runbooks so teams can record audits, track incidents, and version control operational procedures. The platform also supports packaging curated collections for reuse across teams and sites.