{"resourceId":"aplu-csc-ai-playbook-university-communications-marketing-202608","versions":[{"version":"external-5a07cca5006e7813304f9b1e329022e787612280b448ac2251659659ef919a2e","resource":{"id":"aplu-csc-ai-playbook-university-communications-marketing-202608","title":"APLU communications council playbook sets red lines and tiered review for AI in public university marketing and communications","organization":"Association of Public and Land-grant Universities (APLU) Council on Strategic Communications","sector":"Higher education communications and marketing","geography":"United States","publishedAt":"August 2026 (month stated on the playbook; hosted by the University of Alaska)","publicationDate":"2026-08-01","eventDate":null,"sourceName":"APLU Council on Strategic Communications (via University of Alaska)","sourceLabel":"Association playbook","sourceUrl":"https://www.alaska.edu/opa/aiguidelines/2026-CSC-AI-Playbook-FINAL.pdf","evidenceClass":"public-sector-association","outcomeClass":"emerging","topics":["governance-procurement","knowledge-work","data-security","accessibility-workforce"],"finding":"Newly relevant because this week's campus incidents match its red lines. The playbook tells communications leaders to set guardrails with IT, legal and brand input, and offers an illustrative matrix: AI-drafted press releases allowed with human review, FERPA or HIPAA data in public models prohibited, AI image editing limited with disclosure, AI-altered quotes prohibited. Red lines include never misrepresenting documentary imagery and never imitating a real person's likeness. It notes prompts may be public records, agentic tools are not production-ready, high-stakes external content needs line-by-line verification, generated visuals can stereotype communities, and deepfake response needs a named owner.","sledRelevance":"A sector-wide reference that public universities can cite when writing AI rules for communications, covering both productive internal uses and the imagery and likeness risks now in the news.","evidence":"Practitioner guidance from a public university association; no evaluation, adoption data or measured outcomes.","architectureImplications":"Recommends vetting tools for enterprise availability, SSO, FERPA and HIPAA posture and data-use terms before scale.","governanceImplications":"Treats AI rules as cross-functional (IT, security, legal, brand, faculty senate) and recommends tiered human review by stakes.","securityPrivacyImplications":"FERPA and HIPAA data barred from public models; prompts and drafts may be discoverable under public records law; deepfake impersonation of leaders is a named threat.","caveats":"Guidance only; illustrative matrix; publication day not stated, so publicationDate uses the first of the stated month.","streamIds":["campus-operations"],"roles":{"sales":"Sales framing: APLU's communications council has published a playbook with red lines, including never imitating a real person's likeness, and tiered human review for AI in public university communications. Interpretation: this gives marketing leaders a sector reference to justify rules and budget. A bounded engagement is applying the playbook's decision matrix to a client's actual tools and channels. Claims about productivity gains are unsupported here; the playbook offers guidance, not measured results.","engineering":"Engineering framing: the playbook asks institutions to verify SSO, FERPA and HIPAA posture, data-use terms and pricing before scaling tools, and warns prompts may be public records. Interpretation: pre-sales should pair any communications AI tool with records retention settings, enterprise data protections and audit logs. Note its own limit: it calls agentic tools not yet production-ready for communications, so agent proposals need a narrow, verified scope.","delivery":"Delivery framing: the playbook recommends tiered review by stakes, named owners for corrections and a rehearsed deepfake response. Interpretation: delivery can turn these into a review tier table, a correction and takedown runbook and a tabletop exercise for synthetic media of leaders. Acceptance should track whether tiers are applied and the exercise is run, since the playbook itself provides no evidence of outcomes."},"roleDetails":{"sales":{"customerProblem":"Public university communications teams lack a shared reference for what AI use is allowed and how to review it.","stakeholders":["Vice president for communications","CIO and CISO","General counsel","Records officer"],"discoveryQuestions":["Which communications tasks use AI today and under what review?","Are prompts and drafts covered by records retention?","Who responds to a deepfake of the president?"],"valueHypothesis":"Interpretation: applying a sector playbook may speed policy adoption; no outcomes are measured in the source.","potentialEngagement":"Applying the playbook's decision matrix and red lines to one division's tools and channels.","unsupportedClaims":["Measured productivity gains","Agentic tools are ready for communications production","The matrix fits every institution"]},"engineering":{"fit":"Relevant to enterprise AI tools used by communications and marketing staff.","architectureIntegration":"Use enterprise tiers with SSO and data protections, configure retention for public records, and log usage.","prerequisites":["Tool vetting with IT and legal","Records retention policy","SSO"],"constraints":["Public records exposure varies by state","Agentic tools judged immature"],"security":"Bars FERPA and HIPAA data from public models; plan for deepfake impersonation.","proofOfValue":["Confirm retention and discoverability settings with counsel","Pilot limited to internal drafting use cases"]},"delivery":{"work":"Turn playbook guidance into review tiers, runbooks and training for a communications division.","dependencies":["IT and legal input","Brand leadership","Faculty senate where IP or student data is involved"],"ownership":"Central communications, with named owners for corrections and incident response.","skillsAdoption":"Training on review tiers and verification; open discussion of staff craft concerns.","governanceCheckpoints":["Review tier table approved","Deepfake response tabletop","Accessibility review of AI-assisted visuals"],"acceptanceCriteria":["Proposed: review tiers applied to all external releases","Proposed: deepfake tabletop completed within one term"],"risks":["Policy without enforcement","Records exposure from prompts","Bias in generated visuals"]}},"retrievedAt":"2026-10-25T21:58:54-05:00","enrichedAt":"2026-10-25T22:01:55-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Warns generated visuals can stereotype communities and fail accessibility standards; acknowledges writers' and designers' displacement concerns.","procurementImplications":"Advises checking contractual data-use terms and pricing before adopting tools at scale; enterprise deployment can be costly.","operatingModelImplications":"Positions central communications as owner of the AI narrative and recommends named owners for corrections and incident response.","sourceVerification":{"openedUrl":"https://www.alaska.edu/opa/aiguidelines/2026-CSC-AI-Playbook-FINAL.pdf","referenceExcerpt":"Never use AI to imitate a real person's voice, likeness, or identity.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}