Security review
How to review data access, model routes, prompts, retrieval, tools, permissions, logs, and recovery before launch.
Security and compliance
AI integrations need review before they touch sensitive systems.
Security and compliance for AI integration means reviewing what the AI can access, what it can reveal, which tools it can call, what vendors are involved, what evidence is logged, and where human approval is required before AI affects important systems or records.
What this section explains
These guides cover practical security, privacy, vendor, and evidence questions that appear when AI is connected to real data, software, accounts, tools, and workflows.
Agent security
How AI agents and tool-using systems need limits, approval gates, least privilege, logs, and safe failure paths.
Data privacy
How prompts, source material, outputs, logs, vendors, and retained records can affect privacy risk.
Vendor risk
How outside AI providers, tools, plugins, APIs, and platforms create review questions before use.
Compliance evidence
How records, approvals, logs, source references, and change history support later review.
Security and compliance article list
This section contains five launch articles. Build these before treating the section as complete.
Start here
Reading order
AI Integration Security Review
Review the security questions to ask before connecting AI to applications, data, tools, workflows, users, or production systems.
Agent controlsSecure AI Agent Integrations
Understand how to keep tool-using AI agents bounded with permissions, approval gates, action limits, logs, and rollback paths.
PrivacyData Privacy in AI Integrations
Learn how prompts, retrieved context, source systems, outputs, logs, vendors, and retention rules affect privacy risk.
Third partiesVendor Risk for AI Integrations
Review how outside AI providers, plugins, model platforms, SaaS tools, and APIs should be assessed before integration.
RecordsCompliance Evidence for AI-Integrated Systems
See how source references, approvals, logs, version history, access records, and change records can support later review.
Recommended path
Start with security review, then secure agent integrations, data privacy, vendor risk, and compliance evidence.
Security review should follow the full AI path
AI integration security is not one checkbox. Review the request path from user input to source retrieval, model call, tool action, output display, logging, and recovery.
1
Identity
Who or what is calling the AI system: user, role, service account, workflow, app, or agent?
2
Data access
Which records, documents, folders, APIs, indexes, and source systems can the AI reach?
3
Model route
Which provider, model, endpoint, route, gateway, or fallback path receives the request?
4
Output
What does the AI produce: answer, draft, summary, classification, recommendation, or action request?
5
Tool use
Can the AI call tools, update records, send messages, trigger workflows, or affect systems?
6
Review gate
Which outputs or actions need approval, escalation, validation, or human review?
7
Logging
What is recorded for audit, privacy, troubleshooting, compliance, and incident response?
8
Recovery
Can the system be paused, disabled, rolled back, narrowed, or returned to manual operation?
Integration reminder: Security review should cover the system around the model, not only
the model itself.
Security and compliance questions before launch
A practical AI integration review should ask what the system can see, what it can do, what it can reveal, who owns it, and how problems will be detected and contained. These questions matter whether the system uses a public AI API, a private model route, a SaaS assistant, a document Q&A tool, a workflow agent, or a custom model platform.
| Review area | Question | Why it matters |
|---|---|---|
| Access | Does the AI have only the data and tools needed for the approved use case? | Reduces unnecessary exposure and action risk. |
| Privacy | Could prompts, outputs, retrieved sources, logs, or vendors expose personal or sensitive data? | Supports data minimization and privacy review. |
| Vendor | Which outside providers, APIs, plugins, platforms, or subprocessors are involved? | Supports third-party and contractual review. |
| Tool action | Can AI trigger real changes in systems or records? | Determines whether approval gates, rollback, and stronger logs are needed. |
| Evidence | Can the organization explain what happened later? | Supports audit trails, incident response, and compliance review. |
| Recovery | Can the AI feature be paused, disabled, narrowed, or rolled back? | Supports safe containment when behaviour becomes unreliable. |
AI risk changes with connection depth
A standalone draft helper has a different risk profile than an AI agent with write access to business systems. As the connection depth increases, security and compliance review should become stronger.
Lower connection depth
The AI receives limited user input, produces drafts or explanations, and does not directly retrieve sensitive sources or change records.
Higher connection depth
The AI retrieves private sources, calls tools, uses service accounts, affects workflows, updates records, or supports high-impact decisions.
Practical warning: Do not treat a write-capable AI agent the same way as a simple
internal drafting assistant.
How this section connects to the rest of the site
Security and compliance depend on the rest of the integration design. Identity controls decide who can use the system. RAG controls decide what sources can be retrieved. Model platforms decide which routes and versions are active. Observability decides whether the system can be investigated later.
Identity and Access
Roles, permissions, service accounts, approval gates, and audit trails are core security controls.
RAG and Knowledge
Source access, metadata, ingestion, grounding, and knowledge permissions shape privacy and disclosure risk.
Model Platforms
Gateways, routing, model registries, release controls, and rollback paths affect operational risk.
Monitoring and Observability
Logs, traces, drift signals, latency records, and incident response support safe operation.
Educational limitation
This section provides general educational information about security and compliance considerations for AI integrations. It is not legal, financial, medical, engineering, safety, cybersecurity, procurement, compliance, privacy, tax, accounting, or professional advice. It does not provide instructions for bypassing controls, exploiting systems, unauthorized access, or unsafe automation. Use qualified review before connecting AI systems to sensitive data, regulated systems, production infrastructure, customer records, financial processes, safety systems, connected devices, or other high-consequence environments.