AI Risk Management on Microsoft 365 and Azure

AI can help your teams ship faster, answer customers, and modernize old processes. It can also leak data, make flawed decisions, and create costs you cannot explain. This page gives you a clear plan for AI risk management that a small or mid-size business can run without a big program. You will get a plain framework, quick checklists, and a 30-60-90 day rollout. Examples stay inside the Microsoft stack Yocum Technology Group actually builds on, including Azure, Microsoft 365, and the Power Platform.

What AI Risk Management Means

AI risk management is the set of guardrails and habits that keep AI systems reliable, safe, and auditable while you use them to do real work. It covers the full pipeline, from data and prompts to models, apps, and operations. The goal is simple. Use AI where it helps, control what it touches, and prove how decisions were made.

Top takeaways up front

• Start with identity, data access, and basic sharing controls. If inputs are clean and permissions are tight, most AI risks shrink fast.
• Treat AI like any other system. Define owners, logs, testing, and rollback.
• Write a short decision policy for where human approval is required. Then use it.
• Track a few metrics that leadership understands. Coverage, time to detect, and time to correct beat vanity dashboards.

The YTG Lens

Yocum Technology Group designs and ships solutions on Azure, Microsoft 365, and the Power Platform. We modernize applications, build custom software, and connect AI into real workflows with cloud foundations, DevOps discipline, and practical governance. That scope shapes this guide. You will see patterns that map cleanly to Microsoft 365, Azure landing zones, Power Apps, and Power Automate.

The Seven Risks That Actually Show Up

Most organizations face a repeatable set of risks when they move from AI trials to daily use.

1. Data exposure. Sensitive content in prompts or training data leaks through sharing, connectors, or logs.
2. Bad answers at scale. Hallucinations and weak retrieval create wrong outputs that look confident.
3. Model bias and unfair outcomes. Skewed data or prompts create uneven results that harm users or decisions.
4. Prompt and injection attacks. Inputs try to override system instructions or pull data from places they should not.
5. Integrity and availability. Changes break prompts, connectors, or policies. Outages leave critical processes stuck.
6. Shadow AI. Teams use unsanctioned tools and store results outside your policies.
7. Cost creep. Unmonitored usage, chatty prompts, and overprovisioned services drive spend.

You do not need a giant program to control these. A few strong defaults and small, steady reviews carry most of the weight.

A Simple, Reusable Framework

Use this three-part loop to govern, build, and operate AI in a way your team can maintain.

1) Govern: Decide What Is Allowed

Purpose. Set boundaries, owners, and records before you start building. Keep the rules short and tied to your Microsoft environment.

• Approved use cases. List where AI is allowed. For each, define the owner and the business outcome.
• Data boundaries. Name the approved sources. Keep customer and financial data in governed storage with versioning and access reviews.
• Human approval points. For customer-facing content, legal documents, pricing, or high-risk changes, require review before publishing.
• Model and tool catalog. Track which models and copilots are in use, who owns them, and where logs live.
• Offboarding. Remove access, revoke tokens, and transfer ownership when people leave.

These decisions align well with Microsoft 365 access controls, Azure landing zone guardrails, and Power Platform environments that separate development and production.

2) Build: Ship Safe By Default

Purpose. Use patterns that make safe choices the easy choices.

• Identity first. Require multi-factor authentication for all users, with separate admin accounts.
• Least privilege. Scope connectors and storage access to only what the app needs.
• Data preparation. Clean, mask, and label sensitive fields. Store datasets in approved locations tied to your landing zone.
• Retrieval with guardrails. Use grounded retrieval so answers reference approved content. Apply content filters and block lists.
• Human in the loop. Add an approval step where errors matter. Keep the approver and timestamp in the record.
• Change control. Track prompt and configuration changes the same way you track code changes. Use pull requests and environment promotion.

This mirrors the way YTG modernizes apps with Azure and the Power Platform, using clean environments, DevOps, and standard identity.

3) Operate: Watch, Test, And Adjust

Purpose. Measure what you run, fix drift, and prove reliability.

• Logging. Keep prompt inputs, retrieval sources, and outputs with hashed IDs and timestamps.
• Monitoring. Alert on unusual volumes, spikes in blocked content, external sharing changes, and new admin assignments.
• Quality reviews. Sample outputs weekly and label them pass or fail with a short reason.
• Cost visibility. Track spend by environment and use case. Alerts when monthly run-rate crosses thresholds.
• Drills and rollback. Run small restore or rollback tests on a calendar. Keep one runbook per use case.
• Quarterly access review. Audit connectors, secrets, and admin roles.

Where To Start: 30-60-90 Day Plan

Use calendar time. Treat each step like a sprint goal.

Days 1–30: Tighten The Front Door And Inventory

• Turn on multi-factor authentication for every user. Create separate admin accounts.
• Inventory AI tools in use. Add owners for each.
• List approved data sources for AI. Move shadow datasets into governed storage.
• Enable versioning and recycle bins for shared document libraries.
• Publish a one-page policy that bans pasting sensitive data into unapproved tools.

Days 31–60: Add Guardrails That Stick

• Create Power Platform environments for dev, test, and prod.
• In Azure, confirm budgets, tags, and policy in your landing zone so encryption and logging are on by default.
• Set up a prompt and config change process in your repo with approvals.
• Add grounded retrieval for your top use case. Log source snippets with outputs.
• Define human approval steps for high-risk outputs and add them to the workflow.

Days 61–90: Measure, Drill, And Improve

• Pick three quality metrics that matter for your use case. For example, source coverage, human corrections per 100 results, and time to approval.
• Run a weekly sample review and record pass or fail.
• Add cost alerts per environment.
• Run a short incident drill for a prompt injection scenario and a data exposure scenario.
• Close the top three gaps found during the drill and publish the fixes.

A Plain Policy Set You Can Adopt

Keep policies concise and tied to the tools your team already uses.

Access Policy

• Multi-factor authentication for all users. Admin accounts kept separate.
• Named external sharing only, with expiration dates for links.
• Quarterly review of admin roles and service accounts.

AI Use Policy

• AI may only read from approved sources.
• High-risk outputs require human approval.
• All AI changes are tracked in the repo with pull requests.

Data Handling Policy

• Sensitive customer and finance content lives in governed storage with versioning.
• Logs must not contain raw secrets or full personal identifiers.
• Retention rules are set per workspace.

Offboarding Policy

• Disable accounts the day employment ends.
• Transfer ownership of apps, flows, and data connections.
• Revoke tokens and rotate shared secrets.

Microsoft-Anchored Controls That Map Cleanly

This section translates the framework into familiar Microsoft building blocks YTG uses in real projects.

• Identity and access. Use Microsoft 365 for MFA, conditional access, and separate admin roles.
• Landing zone guardrails. In Azure, set budgets, tags, encryption defaults, and centralized logging.
• Data and storage. Keep datasets in governed storage tied to your subscription and environment strategy.
• Power Platform environments. Separate dev, test, and prod with solution export and import, and role-based access.
• Automation. Power Automate flows that include approval steps for high-risk outputs.
• Web experiences. Power Pages integrate with Azure AD and other identity providers for managed access.

The Minimal Logs You Need To Stay Sane

Keep just enough history to investigate issues and learn from them.

• Input log. A hashed user ID, timestamp, use case, and prompt template name.
• Retrieval log. The list of sources used, with document IDs and versions.
• Output log. The result, confidence signals if available, and a link to any approval record.
• Change log. Prompt and config changes with reviewer names.
• Cost log. Daily cost by environment and top use cases.

These logs make audits simple and help teams spot steady drift.

Evaluation That Fits Small Teams

Fancy evaluation is not required to make progress. Use three light methods.

1. Source coverage. What percent of answers cite at least one approved source.
2. Human correction rate. How many outputs needed edits or rejection.
3. Time to approval. How long it takes for a human to greenlight a high-risk output.

Set targets per use case and review trends monthly.

Common Failure Modes And How To Avoid Them

• Unbounded connectors. A connector granted broad access pulls in off-limits content. Scope to the minimum needed.
• No owner. AI apps without named owners gather errors nobody fixes. Assign owners before go-live.
• Shadow AI. Teams experiment in unapproved tools and paste results into production. Provide a sanctioned path and training.
• Drift. A helpful change to a prompt template creates new errors. Use pull requests and environment promotion.
• No rollback. When outputs look wrong, nobody can restore an older version. Keep versions for prompts and retrieval rules.

Questions To Use In Your Intake Form

Ask these before you build.

• What decision or task will this AI support.
• What sources of truth are allowed.
• Who approves high-risk outputs and how fast must they respond.
• What is the rollback for a bad change.
• What does failure look like and who gets paged.
• How will we measure quality.

Short Templates You Can Copy

AI Use Case Card

• Name and owner:
• Outcome:
• Approved sources:
• Human approval: yes or no. If yes, who approves.
• Key metrics:
• Rollback procedure:

Prompt And Config Change Record

• Change summary:
• Reason:
• Linked tickets:
• Reviewer:
• Environments promoted:
• Date and time:

Weekly Quality Review

• Sample size:
• Pass rate:
• Common errors:
• Fixes planned:
• Owner and date:

Cost Controls That Do Not Get In The Way

AI spend can drift when prompts are long, calls are frequent, or environments are left running. Keep cost simple.

• Set budgets and alerts per environment. Tag resources with owner and use case.
• Cache retrieval where possible.
• Trim prompt templates and avoid redundant calls in loops.
• Turn off unused labs at the end of the day.
• Report a short spend table weekly by use case.

These moves fit cleanly with the landing zone and cost practices YTG uses for cloud projects.

Security And Privacy Quick Wins

• MFA everywhere. Include service accounts.
• Named sharing only. Expire external links on a schedule.
• Masking and minimization. Strip secrets and identifiers before prompts are sent.
• Content filters. Block known risky patterns in inputs and outputs.
• Device basics. Encrypt laptops and set screen locks and remote wipe.
• Incident drills. Practice a prompt injection and a data exposure case twice a year.

Human In The Loop, Where It Matters

Not every output needs review. These usually do.

• Public content on your website or social channels.
• Legal, HR, or finance writing.
• Pricing or contract language.
• Product and policy changes.
• Any decision that affects customers or employees in a material way.

Record who approved and keep the version that was approved. This creates trust over time.

Change Management Without The Drama

Explain to staff what is allowed, where to try ideas, and how you will help. Keep training short and repeat it.

• A one hour intro to the approved tools and the data rules.
• A twenty minute walkthrough for prompts that call out risky patterns.
• A simple request form for new use cases.
• A shared chat channel for questions and pattern sharing.

Metrics That Leaders Can Read In Five Minutes

Pick numbers that show coverage, quality, and time to fix.

• Percent of AI users with MFA.
• Number of approved use cases and owners.
• Weekly pass rate from sampled outputs.
• Time to human approval for high-risk outputs.
• Number of prompt changes promoted with review.
• Spend by environment per week with owner.

How YTG Helps You Move Fast And Safe

YTG focuses on practical steps that respect your size and timeline.

• Cloud foundations. Set an Azure landing zone with budgets, tags, logging, and default encryption so safe choices are the defaults.
• Power Platform governance. Create environments, roles, and solution paths so citizen development does not leak data.
• App and workflow delivery. Build or modernize apps with DevOps discipline and approval steps where they matter.
• Operate and improve. Add the light logs, reviews, and drills that keep outputs steady and costs predictable.

This work sits inside YTG’s core services in cloud migration, modernization, and custom development with Microsoft technologies.

Action Plan For This Week

• Confirm MFA for everyone. Create separate admin accounts.
• Publish a one-page AI policy with approved use cases and data sources.
• Inventory current prompts and connectors. Add owners.
• Enable versioning and change reviews for prompts and configs.
• Schedule a thirty minute drill for a bad output scenario.
• Pick three metrics and start the weekly quality sample.

Keep the motion small and steady. You will reduce risk while keeping the benefits that make AI worth the effort.