# AI Governance And Risk Control Blueprint ## For A Small Marketing Agency Using AI In Daily Operations ## 1. AI Readiness Diagnosis The agency is ready for AI-assisted work, but not ready for fully automated AI decision-making. The current AI use cases are useful and realistic for a small marketing team, especially ad copy drafting, campaign brainstorming, call summaries, email drafting, and performance reporting. However, the business needs clearer rules before expanding AI use. The main readiness strengths are that the team already has defined review roles, client-facing work is reviewed by account managers, and the owner already approves sensitive strategy decisions. This gives the agency a good foundation for responsible AI use. The main gaps are ownership, data handling, approval consistency, and output verification. The copywriter and VA use AI daily, but there is no clear rulebook for what client information can be entered into ChatGPT or other AI tools. Client briefs, ad results, customer comments, sales notes, and internal strategies may contain confidential or commercially sensitive information. Without rules, the team may accidentally expose private client data or reuse information across accounts. The agency also has reliability risks. AI-generated ad copy may sound convincing but include unsupported claims, exaggerated guarantees, inaccurate offer details, or tone that does not match the client’s brand. AI-generated summaries may miss key client concerns, misinterpret numbers, or create false conclusions from campaign data. AI-generated monthly reports may overstate performance or recommend actions without enough evidence. The agency should not allow AI to independently send client emails, publish ads, change budgets, approve campaign strategies, make final performance conclusions, or communicate sensitive issues to clients. AI should support the team, not replace human accountability. The agency’s current best position is medium automation with strong human approval gates. AI can speed up drafting, summarizing, organizing, and first-pass analysis, but all client-facing, strategic, financial, reputational, or sensitive outputs must be reviewed before use. --- ## 2. AI Use Case Classification ### Low-Risk AI Use Cases Low-risk tasks are internal, non-sensitive, easy to verify, and do not directly affect client money, client reputation, customer communication, or campaign decisions. Examples: | Use Case | Automation Level | Rules | | -------------------------------------- | ----------------------------: | ------------------------------------------------ | | Brainstorming campaign ideas | AI-assisted or semi-automated | Human selects final ideas | | Rewriting internal notes | AI-assisted | No confidential client data unless anonymized | | Creating internal Trello task drafts | AI-assisted | Account manager reviews before assigning | | Formatting Google Sheets notes | AI-assisted | No confidential data pasted into unsecured tools | | Creating first-draft content calendars | AI-assisted | Account manager approves before client use | Low-risk tasks can be automated more freely, but the output should still be checked for relevance and accuracy before use. ### Medium-Risk AI Use Cases Medium-risk tasks may affect client communication, client perception, campaign direction, or internal decision-making. AI can help draft or analyze, but a human must review before use. Examples: | Use Case | Automation Level | Rules | | -------------------------------------- | ---------------: | ------------------------------------------------------------------ | | Drafting ad copy | AI-assisted only | Copywriter reviews, account manager approves before publishing | | Drafting client emails | AI-assisted only | Account manager reviews before sending | | Summarizing client calls | AI-assisted only | Account manager verifies against notes or recording | | Creating monthly performance summaries | AI-assisted only | Numbers must be checked against Meta Ads Manager and Google Sheets | | Creating campaign recommendations | AI-assisted only | Owner approves before client presentation | Medium-risk tasks should never move directly from AI output to client delivery without review. ### High-Risk AI Use Cases High-risk tasks can expose confidential data, affect client budgets, create legal or reputational risk, or make final business decisions. Examples: | Use Case | Automation Level | Rules | | ----------------------------------------------- | ---------------------------------------: | ----------------------- | | Sending client messages automatically | Not allowed | Human must send | | Changing Meta Ads budgets | Not allowed without owner approval | AI may suggest only | | Making final campaign strategy decisions | Human decision only | Owner approves | | Handling legal, financial, or compliance claims | Human plus professional review if needed | AI may draft only | | Using customer personal data in AI tools | Restricted | Must anonymize or avoid | | Comparing confidential client strategies | Restricted | Never mix client data | High-risk tasks must remain under human control. AI can prepare drafts, summaries, or options, but cannot execute final actions. --- ## 3. Data Safety Rules ### Information That Can Be Entered Into AI Tools The team may enter: | Allowed Data | Example | | -------------------------------- | ------------------------------------------------ | | Generic campaign goals | “Increase leads for a local dental clinic” | | Public business information | Website copy, public offers, public social posts | | Non-sensitive creative direction | “Friendly, local, trustworthy tone” | | Anonymized campaign results | “Campaign A had 2.4% CTR and $18 cost per lead” | | General audience descriptions | “Homeowners aged 35–55 in the local area” | | Internal task instructions | “Turn this outline into a campaign checklist” | ### Information That Should Be Anonymized Before entering information into AI tools, the team should remove or generalize: | Sensitive Detail | Safer Version | | ---------------------------------- | ---------------------------------------------------------- | | Client name | “Client A” or “local HVAC client” | | Customer names | “Customer 1” | | Phone numbers and emails | Remove completely | | Exact addresses | “Local service area” | | Sales notes with customer identity | “Lead asked about pricing and availability” | | Private campaign strategy | Summarize without revealing proprietary details | | Revenue numbers | Use ranges unless exact numbers are necessary and approved | ### Information That Should Never Be Shared In AI Tools Without Owner Approval The team must not paste: | Never Share Without Approval | | ----------------------------------------------------- | | Client login details | | API keys, passwords, or access tokens | | Full customer contact lists | | Private customer messages containing identity details | | Payment information | | Contracts or legal documents | | Confidential client strategy documents | | Internal pricing models | | Sensitive sales notes | | Unpublished client offers or launches | | Competitor attack strategies | | Employee performance concerns | | Hiring or firing discussions | ### Client Data Handling Rules Client data should be separated by account. The team must not paste one client’s data into prompts for another client. AI outputs should never reuse examples, hooks, insights, or customer comments from one client in work for another client unless all identifying information is removed and the concept is generic. ### Financial Data Rules AI may help explain performance metrics, summarize ad spend, or create plain-English reporting language. However, all numbers must be checked against Meta Ads Manager, Google Sheets, or the original data source before sharing with a client. AI must not make final budget recommendations without owner approval. ### Business Secrets And Internal Strategy Rules Internal agency systems, pricing strategy, client acquisition strategy, SOPs, and proprietary campaign methods should not be entered into public or unmanaged AI tools unless approved by the owner. --- ## 4. Human Approval Gates The agency should use simple approval gates based on risk level. ### Approval Gate 1: Internal Draft Review Required for: | Task | Reviewer | | --------------------------- | --------------------- | | AI-generated task notes | VA or account manager | | AI-generated call summaries | Account manager | | AI-generated campaign ideas | Account manager | | Internal Trello cards | Account manager | ### Approval Gate 2: Client-Facing Review Required before anything is sent to a client or published. | Task | Reviewer | | ------------------------- | -------------------------------------- | | Client emails | Account manager | | Ad copy | Copywriter first, then account manager | | Monthly reports | Account manager | | Campaign summaries | Account manager | | Client presentation notes | Account manager | ### Approval Gate 3: Owner Approval Required for high-risk decisions. | Task | Final Approver | | ------------------------------- | -------------- | | Strategy changes | Owner | | Campaign budget recommendations | Owner | | Sensitive client communication | Owner | | Performance issue explanations | Owner | | Major campaign pivots | Owner | | AI automation changes | Owner | | Use of new AI tools | Owner | ### Specific Approval Rules Customer messages: AI may draft, but humans must review and send. Legal or financial content: AI may help simplify language, but the agency should get professional review if legal, regulatory, tax, financial, or compliance risk is important. Hiring decisions: AI should not make hiring decisions. AI may help draft interview questions or summarize role requirements, but the owner makes decisions. Strategy decisions: AI can create options, pros and cons, and scenario summaries. The owner approves final strategy. Public content: AI-generated ads, posts, landing page copy, and client-facing reports require human review before publishing. Data analysis: AI may interpret campaign results, but account managers must verify all numbers against source data. Automation triggers: No automation should send emails, change Trello status to final approved, publish content, or change ads without human confirmation. AI agent actions: Any action that changes a client account, sends communication, edits a live asset, or updates budget requires explicit human approval. --- ## 5. AI Output Quality Control Before using AI output, the reviewer should check the following: ### Accuracy Check * Are all facts correct? * Are campaign numbers copied correctly? * Are dates, names, offers, prices, and locations correct? * Does the output match the client brief? * Does it avoid unsupported claims? ### Hallucination Check * Did AI invent campaign results? * Did AI create fake customer insights? * Did AI assume information not provided? * Did AI mention features, guarantees, awards, or prices that were not in the brief? * Did AI create fake sources or benchmarks? ### Source Verification * For performance summaries, check Meta Ads Manager and Google Sheets. * For client claims, check the client brief or website. * For customer comments, check the original comments or notes. * For strategy recommendations, check actual campaign data before accepting. ### Bias And Fairness Review * Does the copy stereotype a group of people? * Does it make unfair assumptions about age, gender, income, location, or background? * Does targeting language feel discriminatory or manipulative? * Could the message harm the client’s reputation? ### Tone Review * Does the output match the client’s brand voice? * Is it too aggressive, too casual, too formal, or too generic? * Does it sound like the agency or the client would actually say it? * Is it clear, professional, and appropriate for a local business audience? ### Consistency Review * Does the message match previous client communications? * Does the offer match current campaigns? * Does the recommendation align with the agreed strategy? * Does the report use the same metric definitions as previous reports? ### Privacy Review * Does the output include private customer data? * Does it reveal internal client strategy? * Does it include another client’s information? * Does it expose confidential numbers or documents? * Does it include names, emails, phone numbers, addresses, or private notes unnecessarily? ### Final Approval Criteria AI output can be used only when: * The reviewer understands it. * The source data has been checked. * The tone fits the client. * No private data is exposed. * No unsupported claims remain. * The correct approval gate has been completed. * The final human reviewer accepts responsibility for using it. --- ## 6. Role And Responsibility Map ### Agency Owner The owner is accountable for the AI governance system. Responsibilities: * Approves AI policy. * Approves high-risk AI use cases. * Approves campaign strategy changes. * Approves sensitive client communication. * Reviews AI incidents and mistakes. * Decides which AI tools the team may use. * Updates the rules monthly or when risks appear. ### Account Managers Account managers are the main review layer for client-facing AI output. Responsibilities: * Review AI-generated client emails. * Verify client call summaries. * Approve campaign summaries before sending to clients. * Check performance report accuracy. * Escalate sensitive issues to the owner. * Make sure client-specific context is correct. ### Copywriter The copywriter is responsible for creative quality. Responsibilities: * Use AI for first drafts, variations, and idea generation. * Review copy for brand fit, clarity, claims, and tone. * Remove unsupported claims. * Make sure ad copy follows client instructions. * Flag risky or sensitive messaging. ### Virtual Assistant The VA may use AI for structured support tasks, but not final approvals. Responsibilities: * Draft internal summaries. * Organize notes. * Create first-draft Trello cards. * Format report drafts. * Anonymize information before using AI. * Escalate unclear or sensitive tasks to an account manager. ### AI Process Owner For a small agency, this can be the owner or a designated account manager. Responsibilities: * Maintains the AI use case list. * Keeps the AI policy updated. * Reviews logs and mistakes. * Trains new team members. * Checks that approval gates are followed. * Updates approved prompt templates. ### Mistake Handler The owner handles serious AI-related mistakes. Account managers handle minor corrections. Examples: * Incorrect report number: account manager corrects and notifies client if needed. * Wrong client data used: owner reviews and decides response. * Sensitive client email drafted incorrectly: owner approves final response. * AI tool exposes private information: owner documents incident and changes rules. --- ## 7. AI Agent Operating Rules The agency may use AI agents or automation tools only with narrow permissions. ### Agents Can Do Independently AI agents may: * Draft Trello task cards. * Summarize internal Slack discussions. * Create first-draft meeting notes. * Generate campaign idea lists. * Format Google Sheets data into plain-English summaries. * Create internal checklists. * Draft email replies without sending them. * Draft monthly report sections without publishing them. ### Agents Require Human Confirmation Before AI agents must ask for confirmation before they: * Send any email. * Send Slack messages to clients or external partners. * Publish ad copy. * Edit live ad campaigns. * Change budgets. * Change campaign status. * Create client-facing reports. * Move Trello cards to “Approved” or “Sent.” * Access sensitive folders. * Use client data in a new prompt. * Recommend strategy changes. ### Agents Must Never Do AI agents must never: * Log into client accounts using shared passwords. * Change Meta Ads budgets automatically. * Send client communication automatically. * Make final campaign decisions. * Mix client data across accounts. * Use private client documents for another client. * Delete files, emails, campaign data, or Trello cards without approval. * Make legal, financial, or compliance decisions. ### Access Limits Each AI tool or agent should have the minimum access needed. | Area | Access Rule | | ---------------- | ----------------------------------------------------------- | | Google Drive | Client folders separated; no full-drive access if avoidable | | Gmail | Draft-only access preferred; no auto-send | | Slack | Read or summarize selected channels only | | Trello | Create draft cards; no final approval status | | Meta Ads Manager | Read-only unless owner approves otherwise | | Google Sheets | Use copies or controlled sheets for AI analysis | ### Logging Rules The agency should keep a simple AI Activity Log in Google Sheets. Track: * Date * Team member * Client * AI tool used * Use case * Data type used * Output type * Reviewer * Approval status * Issues found * Final action ### Log Review The owner or AI process owner should review the log weekly for the first 30 days, then monthly after the system is stable. Look for: * Repeated copy errors. * Private data being entered. * AI recommendations being accepted too quickly. * Missing approvals. * Client-facing outputs sent without review. * Tools being used outside approved use cases. --- ## 8. Risk Register And Safeguards | Risk | Potential Impact | Likelihood | Warning Signs | Prevention | Response Plan | | ---------------------------------------------------- | ----------------------------------------------------------- | --------------: | -------------------------------------------------------------------- | ------------------------------------------------------------------------ | ---------------------------------------------------------------------------- | | Client data exposure | Loss of trust, contract damage, confidentiality issue | Medium | Team pastes full briefs, names, notes, or customer data into AI | Anonymization rules, approved prompt templates, client folder separation | Owner reviews incident, notify affected client if appropriate, update policy | | AI-generated false claims in ads | Client reputation damage, rejected ads, customer complaints | Medium | Copy includes guarantees, fake awards, exaggerated results | Copywriter claim checklist, account manager approval | Remove claim, correct live copy, inform client if needed | | Incorrect performance summaries | Client loses trust, wrong decisions | Medium | AI report numbers do not match Meta or Sheets | Source verification before reports | Correct report, explain correction, review process failure | | AI sends or prepares sensitive client message poorly | Relationship damage | Medium | Draft sounds defensive, vague, or too casual | Owner approval for sensitive communication | Owner rewrites message and follows up with client | | AI recommends bad campaign changes | Poor results, wasted budget | Medium | AI suggests budget increase without data | Owner approval for budget or strategy changes | Pause recommendation, review data, document decision | | Client data mixed across accounts | Confidentiality breach | Low to Medium | Output includes another client’s offer, name, or strategy | Separate prompt sessions by client, anonymize examples | Stop use, identify source, correct output, notify owner | | Team over-trusts AI | Lower quality work, weak strategy | High | AI output accepted without edits | Mandatory review checklist, training | Retrain team, add stronger approval gates | | AI output sounds generic | Lower client value, weak campaign performance | High | Same style across clients | Brand voice checklist, client-specific context | Copywriter revises, improve prompt templates | | Unauthorized tool use | Security or confidentiality issue | Medium | Team tries new AI tools without approval | Approved tools list | Owner reviews tool, remove access if risky | | Agent takes unintended action | Client account damage | Low but serious | Automation changes status, sends message, or edits data unexpectedly | Draft-only permissions, confirmation gates, logs | Disable agent, audit logs, restore data if needed | | Poor handling of customer comments | Privacy or reputational issue | Medium | Customer names and comments pasted into AI | Remove identifiers, summarize comments safely | Delete unsafe prompt history if possible, update rules | | Unclear accountability | Mistakes not owned or fixed | Medium | “AI wrote it” becomes excuse | Human owner for every output | Assign final approver and document incident | --- ## 9. Internal AI Policy Draft # Internal AI Usage Policy ## Purpose We use AI to help the agency work faster, organize information, create better drafts, and improve internal productivity. AI is a support tool, not a final decision-maker. Every team member is responsible for reviewing AI work before using it. ## Approved AI Uses Team members may use AI to: * Draft ad copy ideas. * Rewrite copy for tone and clarity. * Summarize client calls. * Draft client email responses. * Create campaign idea lists. * Organize meeting notes. * Draft monthly performance summaries. * Format internal checklists. * Create Trello task drafts. * Turn campaign data into first-draft insights. ## Restricted AI Uses AI may assist, but a human must review and approve before use: * Client emails. * Ad copy. * Monthly reports. * Campaign recommendations. * Strategy documents. * Public content. * Client-facing summaries. * Data analysis. * Sensitive communication. ## Prohibited AI Uses Team members must not use AI to: * Send client emails automatically. * Change ad budgets automatically. * Publish ads without approval. * Make final campaign decisions. * Share client passwords, logins, or private files. * Upload full customer contact lists. * Use one client’s private data for another client. * Make legal, financial, or compliance decisions. * Make hiring or firing decisions. ## Data Safety Rules Before using AI, remove: * Customer names. * Emails. * Phone numbers. * Addresses. * Payment information. * Private sales notes. * Passwords or access details. * Client confidential strategy. * Any information that is not needed for the task. Use client labels like “Client A” or “local plumbing client” instead of real names when possible. ## Review Requirements Before AI output is used, the reviewer must check: * Is it accurate? * Does it match the client brief? * Are all numbers correct? * Are there unsupported claims? * Is the tone appropriate? * Is private data removed? * Is the correct person approving it? * Could this harm the client or agency if wrong? ## Approval Rules * Copywriter reviews AI-generated copy. * Account managers approve client-facing work. * Owner approves sensitive communication, strategy changes, budget recommendations, and new AI automation. * VA may draft and organize, but cannot approve final client-facing work. ## AI Agent Rules AI agents may draft, summarize, organize, and prepare internal materials. They must not send messages, publish content, change budgets, delete files, or make final decisions without human approval. ## Mistakes If AI creates an error, the team member who used the output must report it to the account manager or owner. Serious mistakes involving client data, incorrect reports, sensitive messages, or live campaigns must be escalated to the owner immediately. ## Tool Approval Only approved AI tools may be used for client work. New tools must be approved by the owner before the team enters client information. ## Final Rule AI can help create work, but humans are responsible for the final output. --- ## 10. 30-Day Implementation Plan ### Week 1: Set Rules And Map Current AI Use Actions: * List all current AI use cases. * Identify which team members use AI and for what tasks. * Separate use cases into low-risk, medium-risk, and high-risk. * Create the approved AI tool list. * Set the first version of the internal AI policy. * Create a simple AI Activity Log in Google Sheets. * Create client data anonymization examples. * Confirm that AI cannot send emails, change budgets, or publish ads automatically. Deliverables: * Approved AI Use Case List. * Internal AI Policy v1. * AI Activity Log. * Data Safety Rules. * Approval Gate Map. ### Week 2: Train Team And Create Templates Actions: * Train the copywriter and VA on what data can and cannot be entered into AI. * Train account managers on reviewing AI outputs. * Create standard prompt templates for: * Ad copy drafting. * Client call summaries. * Client email drafts. * Monthly report summaries. * Campaign idea generation. * Add a privacy reminder at the top of every prompt template. * Create review checklists for client emails, ad copy, and reports. Deliverables: * Approved Prompt Library. * AI Output Review Checklist. * Client Email Review Checklist. * Ad Copy Review Checklist. * Monthly Report Review Checklist. ### Week 3: Pilot The Governance System Actions: * Test the system on 2–3 active client accounts. * Require team members to log AI use in the AI Activity Log. * Review all client-facing AI output through the correct approval gate. * Compare AI-generated summaries against real notes and campaign data. * Track common errors such as wrong tone, fake claims, incorrect numbers, and missing context. * Adjust prompt templates based on mistakes. Deliverables: * Pilot Results Summary. * List of common AI errors. * Updated prompt templates. * Updated approval rules if needed. ### Week 4: Review, Improve, And Standardize Actions: * Owner reviews the AI Activity Log. * Identify risky behavior or missing approvals. * Update the internal AI policy. * Decide which AI use cases are safe for regular use. * Decide which use cases need stronger approval. * Create a monthly AI governance review habit. * Confirm whether any legal, regulatory, or contractual issues require professional review. Deliverables: * Internal AI Policy v2. * Final AI Use Case Classification. * Monthly Review Process. * Updated Risk Register. * Team Training Notes. ### After 30 Days The agency should continue with a monthly AI review meeting. Monthly review questions: * Did AI save time? * Were any mistakes caught? * Did any private data get exposed? * Were approval gates followed? * Which prompts worked best? * Which use cases should be stopped, improved, or expanded? * Are any client contracts or legal obligations affected? --- ## 11. Executive Summary The safest and most valuable way for this marketing agency to use AI is as a drafting, summarizing, organizing, and analysis support system with clear human review. AI should help the team move faster, but it should not send client messages, change ad budgets, publish campaigns, expose client data, or make final strategy decisions. The agency should automate low-risk internal work such as note formatting, campaign idea generation, task drafting, and internal checklists. It should use AI-assisted workflows for ad copy, client emails, call summaries, monthly reports, and campaign recommendations. These tasks can save time, but they must be reviewed by the right person before use. The agency should keep high-risk actions under human control. Strategy changes, budget recommendations, sensitive client communication, final performance conclusions, and live campaign changes require owner or account manager approval. The most urgent risks to control are client data exposure, inaccurate performance summaries, unsupported ad claims, over-trusting AI output, and unclear approval ownership. The agency does not need expensive governance software at the beginning. It needs a simple internal policy, approved use case list, AI Activity Log, review checklist, prompt templates, and clear approval gates. The immediate next actions are: 1. Create the approved AI use case list. 2. Set data safety rules. 3. Create the AI Activity Log. 4. Require human review before client-facing use. 5. Give the owner final approval over strategy, budgets, and sensitive communication. 6. Train the copywriter, VA, and account managers on safe AI use. 7. Review the system after 30 days and improve the rules. AI should make the agency faster and more organized, but humans must remain responsible for judgment, client trust, strategy, privacy, and final decisions.