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· 22 min read · By Rajesh Kumar

Enterprise AI security: an implementation guide

How to run AI security as one program instead of a pile of tools: two lanes, one control plane, twelve areas to build, a phased rollout and the metrics that show it is working.

  • AI security
  • Zero Trust
  • Governance

Companies tend to adopt AI one tool at a time, and end up unable to say what is in use, by whom, and with what data. This guide sets out how to fix that: what to build, in what order, and how to tell when each part is done.

It is a practitioner’s working guide. It applies public frameworks (the OWASP Top 10 for LLM Applications, MITRE ATLAS, the NIST AI Risk Management Framework) and the vendors’ own documentation, all linked at the end. It is not a standard, and it is not a substitute for reading those sources.

The approach in one line: give people one approved, easy route to AI that is governed and logged by default, and make unapproved routes harder to use than the approved one.

The guide is written for a company that already runs a security and cloud platform. A smaller team can use the same structure and stop earlier: the principles, the risk tiers, the pitfalls and the first steps apply at any size.

In brief

  • Architecture: AI is used in two ways.
    • Tools people use, like Copilot and ChatGPT Enterprise. Controlled through sign-in rules, discovery of unapproved AI tools, data-loss rules and sensitivity labels.
    • AI the company builds, meaning apps and agents. These go through a central AI gateway, cloud policies and security checks before launch.
    • Both feed one inventory, one SIEM and one governance process.
  • Twelve areas (section 2), each with what to build, which existing products to use first, and when it counts as done: governance and risk tiers; finding all AI in use; identity for people and agents; data protection including Copilot oversharing; the central AI gateway; cloud guardrails; secure AI app development; where models and AI libraries come from; detection and response; third-party AI; training; compliance.
  • Rollout in five phases (section 3), each with criteria for moving on, not dates or headcount: find what’s in use, secure the people tools, centralize the company-built AI, add security checks to development, then cover agents and ongoing assurance.
  • Metrics (section 4): a six-item leadership scorecard plus coverage, risk and adoption metrics. Every control metric is paired with an adoption metric, otherwise “unapproved AI use is down” might only mean people moved to their phones.
  • Also included: checklists for M365 Copilot, ChatGPT Enterprise and GitHub Copilot, a who-owns-what table, common pitfalls, and five first steps.

The two points that matter most:

  • Fix permissions before rolling out Copilot. Copilot can show a user anything they already have access to, so old overshared SharePoint sites become searchable in one prompt.
  • Route all company AI through one gateway. It handles sign-in without shared API keys, logging, token limits, content filters and cost tracking once, instead of every team building its own.

Note: vendor features and some cloud policy names change often. The vendor details here were re-checked against the vendors’ documentation on 2026-10-01; confirm them again before building on them. A good hands-on start is a small Phase 2: one app behind a gateway, no shared keys, logs into a SIEM.

0. The problem this solves

What usually happens:

  • Tools get switched on one at a time: an OpenAI/ChatGPT contract here, Copilot licenses there, a team calling a model API with its own key somewhere else.
  • Each has its own admin console, its own logs, its own data rules. Nobody can answer the basic questions:
    • What AI is in use, by whom, and with what data?
    • Which apps and agents call models, and with what permissions?
    • What did a user or agent send and get back, and would we detect misuse?
    • What does it cost, and who owns each risk?

Buying tools does not answer those questions. A program with one control plane does.

The goal in one line: give people one sanctioned, easy, well-lit path to AI (the paved road), make that path observable and governed by default, and make the unsanctioned paths harder than the paved one.

Guiding principles

  1. Enable, don’t block. If the sanctioned path is slower than the shadow path, people take the shadow path. Security wins only when the paved road is the easiest road.
  2. Central control plane, federated build. Security and platform own identity, gateway, data policy, logging, and guardrails once. Product teams build on top without re-solving them.
  3. Out-of-the-box first. Use what Microsoft 365, the clouds, the SSE/CASB, and the CNAPP already provide before building anything. Build only the glue.
  4. Identity is the perimeter for AI too. Every human, app, and agent that touches a model has a real identity, least privilege, and an audit trail.
  5. Data rules follow the data, not the tool. Sensitivity labels and DLP apply whether the data goes to Copilot, ChatGPT, or a custom app.
  6. Risk-tiered, not one-size. A marketing summarizer and an agent that can change production are not governed the same way.

1. Architecture at a glance

            USERS                         APPS / AGENTS
              |                                |
   [ SSO + Conditional Access ]      [ Workload identity (OIDC / managed identity) ]
              |                                |
   +----------+-----------+          +---------+----------+
   | SaaS AI              |          |  CENTRAL AI GATEWAY |
   | (M365 Copilot,       |          |  - auth, no shared keys
   |  ChatGPT Enterprise, |          |  - model allowlist
   |  GitHub Copilot)     |          |  - token quotas, cost chargeback
   +----------+-----------+          |  - prompt/response guardrails
              |                      |  - full logging
   [ SSE / CASB: discover,           +---------+----------+
     allow sanctioned,                          |
     block/coach unsanctioned ]      +----------+-----------+
              |                      | Model providers       |
   [ DLP + sensitivity labels ]      | (Azure OpenAI/Foundry,|
              |                      |  Bedrock, Vertex)     |
              |                      | private endpoints,    |
              |                      | policy-restricted     |
              |                      +----------+-----------+
              |                                 |
              +---------------+-----------------+
                              |
                   [ SIEM: all AI logs + detections ]
                   [ AI inventory / AI-SPM / risk register ]
                   [ Governance: policy, intake, risk tiers ]

Two lanes, one control plane:

  • Lane A - SaaS AI for people (Copilot, ChatGPT Enterprise, coding assistants). Controlled with identity, SSE/CASB, DLP, labels, and vendor admin settings.
  • Lane B - AI built by the company (apps, RAG, agents). Controlled with the gateway, cloud policy, AppSec gates, and workload identity.

Both lanes feed the same inventory, the same SIEM, and the same governance.

2. The twelve capability pillars

For each pillar: what it is, how to implement it (out-of-the-box first), and what “done” looks like.

Pillar 1 - Governance and operating model

What: who decides, what the rules are, how a new AI use gets approved.

Implement:

  • An AI governance council (security, privacy, legal, data, architecture, a business sponsor). It sets policy and risk appetite; it does not review every prompt.
  • AI Acceptable Use Policy - short, plain language: what data may go into which tools, what is prohibited, how to request something new.
  • AI Security Standard - the technical minimums (the controls in this guide), written as testable requirements.
  • Risk tiering for every AI use case:
Tier Example Controls required
Low Summarize public docs, drafting with no sensitive data Sanctioned tool, standard logging
Medium Internal data, RAG over company docs, customer-facing text reviewed by a human + data classification check, threat model, guardrails, red-team test
High Customer PII or other regulated data, automated decisions affecting people, agents that act on systems + council review, human-in-the-loop, full red team, continuous monitoring, legal review
  • Intake process: one form -> auto-tier -> the tier decides the path. Low tier should be self-service in minutes, or people will bypass it.
  • RACI (see section 6).

Done when: every AI use case in production has an owner, a tier, and a record in the inventory.

Pillar 2 - Inventory and discovery (you can’t secure what you can’t see)

What: a live list of every AI tool, model, app, agent, and dataset in use, sanctioned or not.

Implement:

  • Shadow AI discovery from network/SSE logs: Microsoft Defender for Cloud Apps, Netskope, Zscaler, Palo Alto Prisma Access all have generative-AI app categories and risk scores.
  • OAuth consent audit: find third-party AI apps users granted access to mail/files (Entra enterprise apps, Google Workspace, Slack). This is a common and quiet data leak.
  • Cloud AI posture (AI-SPM): Defender for Cloud (Defender CSPM AI security posture), Wiz AI-SPM, Prisma Cloud and similar list model deployments, endpoints, and misconfigurations across clouds.
  • Code discovery: scan repos for AI SDKs and API keys (openai, anthropic, boto3 bedrock-runtime, langchain, hard-coded keys) with existing SAST/secret scanning.
  • AI-BOM for built apps: model, version, provider, datasets, prompts, tools/plugins, owner. CycloneDX supports ML-BOM.

Done when: discovery runs continuously, and every discovered item is either in the inventory with an owner or has a blocking/remediation ticket.

Pillar 3 - Identity and access

What: only known identities reach AI, with least privilege.

Implement (people):

  • SSO for every sanctioned AI SaaS; SCIM provisioning so leavers lose access automatically.
  • Conditional Access: managed/compliant device, MFA, location rules for AI apps like any other sensitive app.
  • Tenant restrictions so employees can’t sign in to personal ChatGPT/Copilot/other tenants from corporate devices and networks.
  • License/access by group, reviewed in the normal access review cycle.

Implement (apps and agents):

  • No shared API keys. Apps use managed identity / workload identity federation (OIDC) to the gateway and to cloud model services. Disable key-based auth on model resources where the provider allows it (for example Azure OpenAI local auth can be disabled).
  • Each agent gets its own identity with only the permissions its tools need. Treat agents like service accounts that can be tricked: scope narrowly, prefer short-lived tokens, log everything. (Microsoft Entra Agent ID is now generally available and similar agent identity features are emerging elsewhere; check their current status before relying on them. The agent identity lifecycle guide covers that model in detail.)
  • High-impact agent actions (payments, deletions, production changes, external email) require human approval.

Done when: 100% of AI access is SSO or workload identity, zero static model API keys in code or config, and every agent has a named identity and owner.

Pillar 4 - Data protection

What: sensitive data doesn’t leak into prompts, outputs, logs, or vendors.

Implement:

  • Classify and label first. Microsoft Purview sensitivity labels (or equivalent). AI tools respect labels only if data is labeled.
  • DLP for AI:
    • Microsoft Purview Data Security Posture Management (DSPM) for visibility and policies across Copilot and third-party AI. The earlier “DSPM for AI” is now labelled classic.
    • Endpoint/browser DLP to block pasting labeled or pattern-matched data (PII, secrets, source code) into unsanctioned AI sites.
    • SSE inline DLP for web AI traffic.
  • Copilot oversharing - the biggest Copilot lesson: Copilot shows users everything they already have access to. Years of “Everyone” SharePoint sites suddenly become searchable in one prompt. Before broad rollout:
    • Run an oversharing assessment (Purview DSPM data risk assessments, SharePoint Advanced Management reports).
    • Fix broad permissions on sensitive sites; use Restricted Content Discovery as a temporary fence. (Restricted SharePoint Search, the older option, is being retired: Microsoft has blocked new enablement since 31 July 2026.)
    • Apply labels with encryption to the crown jewels.
  • Vendor data terms: enterprise agreements with no training on your data, defined retention, data residency, and access to audit/compliance APIs (ChatGPT Enterprise Compliance API, Microsoft Purview audit for Copilot).
  • RAG permission-aware retrieval: the retrieval layer must enforce the user’s permissions, not the app’s service account. This is the most common RAG design flaw.
  • Prompt and response logs are sensitive data - protect, retain, and restrict them like any other sensitive log.

Done when: sensitive data is labeled, DLP covers sanctioned and unsanctioned AI paths, Copilot oversharing is remediated for sensitive sites, and every AI vendor contract has no-training and retention terms.

Pillar 5 - Central AI gateway (the heart of Lane B)

What: a single front door for every model call from company apps and agents.

Why it matters: it gives you, in one place, the things that are otherwise re-built badly by every team: auth, logging, quotas, cost, model allowlist, guardrails, and provider failover.

Options (out-of-the-box first):

  • Azure API Management - AI gateway capabilities (token limit and token metric policies, load balancing across model deployments, semantic caching, content safety integration). Natural fit in a Microsoft-heavy shop.
  • Cloud-native access paths with policy (Bedrock, Vertex) if a team lives in one cloud, with logging to the SIEM.
  • Open source / commercial gateways (LiteLLM, Kong AI Gateway, Portkey, Cloudflare AI Gateway) if you need provider-neutral routing.

Gateway must do:

  • Authenticate callers by workload identity; map each call to an app, team, and cost center.
  • Enforce a model allowlist by risk tier.
  • Per-app token quotas and rate limits (controls cost and “denial of wallet”).
  • Input/output guardrails: prompt-injection detection, jailbreak detection, PII redaction, content filtering (Azure AI Content Safety / Prompt Shields, Bedrock Guardrails, Google Model Armor, or gateway plugins).
  • Log metadata for every call (who, app, model, tokens, latency, guardrail verdicts) and, per tier and policy, the prompt/response content.
  • Emit usage for chargeback/showback.

Done when: direct calls to model providers from apps are blocked by network or cloud policy, and all traffic flows through the gateway.

Pillar 6 - Cloud platform guardrails for AI services

What: the landing-zone controls you already know, applied to AI services.

Implement:

  • Preventive policy:
    • AWS: SCPs to restrict Bedrock to approved regions and models, and to deny model access outside approved accounts.
    • Azure: Azure Policy to require private endpoints, disable public network access and local (key) auth on Azure OpenAI / AI services, and restrict allowed model deployments.
    • GCP: organization policies for Vertex AI (including allowed-model restrictions; confirm the current constraint names) plus VPC Service Controls around Vertex.
  • Network: private endpoints / PrivateLink for model endpoints; no public model endpoints for internal apps.
  • Encryption: customer-managed keys where the data tier requires it.
  • Logging on by default: Azure diagnostic settings for AI resources, Bedrock model invocation logging, Vertex audit logs, all to the SIEM.
  • AI-SPM continuously checking the above (Pillar 2 tools).

Done when: a new AI resource cannot be created out of policy, and posture checks show zero public or key-auth model endpoints.

Pillar 7 - Secure AI application development (AppSec for AI)

What: every AI app goes through security gates proportional to its tier.

Implement:

  • Threat model each medium/high tier app with OWASP Top 10 for LLM Applications and MITRE ATLAS (prompt injection, sensitive info disclosure, excessive agency, insecure output handling, supply chain, poisoning, unbounded consumption).
  • Design rules:
    • Treat all model output as untrusted input: encode, validate, never execute directly.
    • Keep secrets and authorization logic out of prompts; the system prompt is not a security boundary.
    • Assume indirect prompt injection from any retrieved content (documents, emails, web pages, tool results).
    • Agents: least-privilege tools, allowlisted actions, human approval for high impact, and avoid the “lethal trifecta” (private data + untrusted content + a way to send data out) in a single agent.
  • Pre-launch gates: automated red-team scans (garak, PyRIT, promptfoo) in CI; manual red team for high tier.
  • Evaluation in CI: regression tests for jailbreaks and data leakage run on every prompt or model change, since a model upgrade can silently weaken safety behavior.
  • Reference architectures and templates (secure RAG, secure agent) so teams start compliant instead of being reviewed into compliance.

Done when: no medium/high tier app reaches production without a threat model and a passed red-team gate, and teams mostly start from the reference templates.

Pillar 8 - AI supply chain

What: models, datasets, and AI libraries are trusted.

Implement:

  • Approved model sources (provider APIs, vetted open-weight models); a mirrored/internal model registry for open-weight models.
  • Scan model files before use (pickle-based formats can execute code; prefer safetensors; tools like ModelScan).
  • Pin versions of models and AI libraries; track in the AI-BOM.
  • Vet plugins, MCP servers, and agent tools like any third-party code; allowlist them.
  • Dataset provenance for anything used in fine-tuning or RAG indexing (poisoning risk).

Done when: every model and tool in production traces to an approved source and a scanned artifact.

Pillar 9 - Detection and response

What: you’d notice AI misuse or compromise and know what to do.

Log sources to onboard to the SIEM:

  • M365 Copilot interactions (Purview unified audit), ChatGPT Enterprise Compliance API, GitHub Copilot audit logs.
  • Gateway logs (every call, guardrail verdicts).
  • Azure OpenAI / AI services diagnostics, Bedrock invocation logs + CloudTrail, Vertex audit logs.
  • SSE/CASB logs for AI web traffic and DLP hits.
  • Cloud-native AI threat detection (for example Defender for Cloud threat protection for AI services).

Starter detection use cases:

  • Repeated guardrail blocks (jailbreak/injection attempts) from one user or app.
  • Sensitive-data DLP hits in prompts or responses.
  • Token or cost spikes per app (abuse, runaway agent, stolen credential).
  • Model API calls that bypass the gateway (direct to provider from an unexpected identity/network).
  • New model deployment or AI resource outside policy.
  • Agent performing unusual tool actions, or actions outside business hours / normal volume.
  • Access to AI services from a new country/device for a privileged user.
  • Leaked AI API keys found by secret scanning.

Response:

  • An AI incident playbook added to the existing IR process: kill switch per app (disable at the gateway), revoke agent identity, preserve prompt/response logs, assess data exposure, notify per privacy rules.
  • Include AI scenarios in tabletop exercises.

Done when: all AI log sources are in the SIEM, the starter detections are live and tuned, and the AI playbook has been exercised once.

Pillar 10 - Third-party and embedded AI risk

What: AI arriving inside vendors and existing SaaS (the “AI feature got switched on in a tool we already had” problem).

Implement:

  • Add AI questions to vendor risk assessments: training on customer data, retention, sub-processors, model providers used, opt-out controls, incident notification.
  • Contract clauses: no training on company data, notice before enabling new AI features, audit rights.
  • SaaS posture management or CASB to detect newly enabled AI features in existing SaaS.

Done when: every vendor with AI features has an assessed and contracted position, and new AI features trigger review.

Pillar 11 - People: training and enablement

Implement:

  • Short role-based training: all staff (what data goes where), developers (secure AI app patterns), admins (Copilot/SaaS AI settings).
  • Just-in-time coaching: SSE “coach” pages that redirect users from an unsanctioned AI site to the sanctioned one, instead of a silent block.
  • A champions network in the business units.

Done when: completion tracked, and shadow AI trend is going down while sanctioned usage goes up.

Pillar 12 - Compliance and assurance

Implement:

  • Map controls once to NIST AI RMF (and its Generative AI Profile, NIST AI 600-1), ISO/IEC 42001, and the EU AI Act where applicable; reuse the mapping for every audit.
  • Keep evidence automated where possible (policy compliance, posture scans, gateway logs).
  • Periodic independent red teams for high-tier systems.

Done when: you can produce evidence for any mapped control without a manual scramble.

3. Phased rollout

Sequence by dependency and risk reduction. Each phase has exit criteria; move on when they are met, not by date.

Phase 0 - Discover and baseline

  • Stand up shadow-AI discovery (SSE/CASB), OAuth consent audit, AI-SPM scan, repo scan for AI SDKs and keys.
  • Build the first inventory; identify owners.
  • Measure baseline metrics (section 4).
  • Form the council; publish the Acceptable Use Policy.

Exit: you know what’s in use, by whom, and have baseline numbers. Leadership has seen the gap.

Phase 1 - Secure the paved road for people (Lane A)

  • Pick sanctioned tools; enable SSO, SCIM, Conditional Access, tenant restrictions.
  • Sensitivity labels + DLP for AI; Copilot oversharing remediation before broad rollout.
  • Vendor contracts with no-training and retention terms.
  • SSE: allow sanctioned, coach-then-block unsanctioned.
  • Onboard SaaS AI audit logs to the SIEM.
  • Training for all staff.

Exit: sanctioned tools cover the main use cases, unsanctioned use is coached/blocked, and DLP + audit coverage is in place.

Phase 2 - Central control plane for built AI (Lane B)

  • Deploy the AI gateway; onboard existing apps; remove static keys; switch to workload identity.
  • Cloud policy: private endpoints, no key auth, model allowlists, logging on by default.
  • Block direct provider calls that bypass the gateway.
  • Gateway and cloud AI logs to the SIEM; first detections live.

Exit: all company AI apps call models through the gateway with workload identity, and every call is logged and attributed.

Phase 3 - Secure AI development lifecycle

  • Risk-tiered intake live and self-service for low tier.
  • Threat modeling and red-team gates in the SDLC; CI evaluation tests.
  • Reference templates for secure RAG and secure agents.
  • Supply chain: approved model sources, model scanning, AI-BOM.

Exit: no medium/high tier app ships without passing its gates; most new apps start from templates.

Phase 4 - Agents and continuous assurance

  • Agent identity model, tool allowlists, human-approval patterns, agent-specific detections.
  • MCP/tool server allowlist and review.
  • Tuned detections, AI incident playbook exercised, periodic red teams.
  • Compliance mapping with automated evidence.

Exit: agents operate under the same identity, logging, and approval controls as humans and apps, and the program reports metrics on a regular cadence.

4. Metrics

Report a small set to leadership; keep the rest as operational.

Executive scorecard (the six that tell the story)

Metric Definition Direction
Sanctioned AI adoption Active users of sanctioned AI tools / total workforce Up
Shadow AI usage Users or traffic to unsanctioned AI apps (from SSE) Down
Governed AI apps % of AI apps in production that are inventoried, tiered, owned, and behind the gateway Up to 100%
Sensitive data exposure DLP incidents involving AI (blocked + allowed), trend Down (blocked share up)
High-risk findings open Open critical/high AI findings (posture + red team), and their age Down
AI spend under management % of AI spend attributed to an owner via gateway/licensing Up to 100%

Coverage metrics

  • % AI SaaS with SSO + SCIM.
  • % AI apps/agents using workload identity (target 100%); count of static model API keys found (target 0).
  • % model endpoints private and key-auth disabled.
  • % AI log sources onboarded to SIEM.
  • % sensitive SharePoint/content sites remediated for oversharing.
  • % vendors with AI features assessed and contracted.

Risk and quality metrics

  • Guardrail block rate (jailbreak/injection attempts per 1,000 calls), and false-positive rate from user feedback.
  • Red-team pass rate at launch; findings by OWASP LLM category.
  • Mean time to remediate AI findings by severity.
  • Mean time to detect and contain AI incidents.
  • % medium/high apps with a current threat model.

Enablement and efficiency metrics

  • Intake time: request to approval, by tier (low tier should be near-instant).
  • % new AI apps built from reference templates.
  • Training completion by role.
  • Cost per app/team, token usage trend, cache hit rate at the gateway.

Tip: always pair a control metric with an adoption metric. “Shadow AI down” means little if “sanctioned adoption” isn’t up; it can just mean people moved to phones.

5. Product-specific checklists

Microsoft 365 Copilot

  • Oversharing assessment done; broad-access sensitive sites fixed or fenced (Restricted Content Discovery as a temporary measure).
  • Sensitivity labels on sensitive content; label-based encryption for crown jewels.
  • Purview DSPM set up; DLP policies for Copilot.
  • Audit logging on; Copilot interactions flowing to the SIEM.
  • Retention policy for Copilot interactions set per legal requirements.
  • Licensing by group; staged rollout starting with lower-risk groups.
  • Plugins, connectors, and agents in Copilot governed (who can build/publish; allowed connectors).

ChatGPT Enterprise / OpenAI

  • SSO, SCIM, domain verification; personal accounts blocked via tenant/domain controls.
  • Contract: no training on business data, retention settings, data residency where needed.
  • Compliance API integrated with the SIEM / DLP.
  • GPTs, connectors, and third-party actions restricted to approved ones.
  • API usage by apps goes through the gateway, not direct keys.

GitHub Copilot / coding assistants

  • Business/Enterprise plan with policy controls; public-code-match filter per legal guidance.
  • Content exclusions for sensitive repos/files.
  • Audit logs to the SIEM.
  • Secret scanning and SAST unchanged; AI-generated code gets the same review.
  • Agent modes (autonomous PRs, MCP tools) limited to approved tools and scoped tokens.

6. RACI (who does what)

Activity CISO / Security Arch Platform / Cloud Data / Privacy Legal / Procurement App teams AI Council
Policy and risk tiers R C C C I A
Identity and access for AI A/R R I I C I
AI gateway C A/R C I C I
Cloud AI guardrails A R I I I I
DLP and labeling R C A C I I
Threat model / red team A/R C C I R I
Vendor AI terms C I C A/R I I
Detection and response A/R C C C C I
Metrics reporting R C C I C A

(R = responsible, A = accountable, C = consulted, I = informed)

7. Common pitfalls

  • Rolling out Copilot before fixing permissions. Oversharing turns into an instant, searchable data exposure.
  • Blocking without a sanctioned alternative. Shadow AI moves to personal devices where you have no visibility.
  • Every team building its own gateway, logging, and guardrails. Expensive, inconsistent, and unobservable. Centralize the control plane.
  • Trusting the system prompt as a security control. It isn’t; enforce authorization in code and at the data layer.
  • RAG using a service account with broad access. Users see documents they aren’t entitled to.
  • Agents with standing, broad credentials. One injected instruction becomes a real action.
  • Logging prompts without protecting the logs. The log store becomes the biggest sensitive dataset in the company.
  • Measuring controls without measuring adoption. You’ll report success while risk moves elsewhere.
  • Treating a model upgrade as a config change. Safety behavior can change; re-run evaluations.

8. Where to start on day one

  1. Turn on shadow-AI discovery in the SSE/CASB you already have and pull the OAuth consent report. That gives you the “what’s really in use” slide.
  2. Run the Copilot oversharing assessment (if M365 Copilot is licensed or planned).
  3. Write the one-page Acceptable Use Policy and the three risk tiers.
  4. Pick the gateway and onboard one real app end to end (workload identity, logging, guardrails, SIEM) as the reference.
  5. Publish the executive scorecard with baseline numbers and review it every month.

9. Key references

Product names and features in this space change quickly; confirm the current name and availability of any preview feature before building on it.

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