Artificial Intelligence

What it is

Pragmatic AI for higher education — focused on use cases that actually pay off, honest about where AI is the wrong tool, and grounded in the higher-ed-specific risk overlays (FERPA, HIPAA, IRB, NSPM-33, faculty governance, academic-freedom considerations) that most generic AI strategy engagements skip. We help your institution figure out which AI claims are real, which use cases produce frictionless gains, and which ones create new compliance exposure without delivering commensurate benefit.

AI Governance for Higher Education

AI deployment in higher-education environments has to respect FERPA when student records touch hosted LLMs, IRB review when research data is involved, faculty governance when academic freedom intersects, and Title IX considerations when AI-mediated bias affects access. NIST AI RMF gives us the structural framework; higher-education-specific overlays make it operational for your institution.

How prospects describe the pain

• “The board wants us to ‘do something with AI.’”
• “Every vendor we have is now ‘AI-powered’ and we don't know what's real.”
• “Faculty, staff, and students are using ChatGPT everywhere and we have no policy.”
• “Our IRB is asking about LLM use in research and we don't have answers.”
• “We want to use AI in our SOC but worry about leaking data to OpenAI.”
• “Advancement wants AI for donor research and Legal is nervous.”

Where AI actually pays off in higher-ed

• Security operations augmentation — alert triage, detection-rule drafting, Splunk dashboard generation, SOC analyst force-multiplication.
• Governance documentation drafting — policies, standards, runbooks, control narratives (with human review as a hard requirement).
• Risk assessment workflows — HECVAT scoring assistance is exactly what Azimuth does, and a good demonstration of AI calibrated to higher-ed context.
• Operational automation — helpdesk tier-1 triage, knowledge-base curation, ticket classification.
• Analytics and reporting — turning telemetry into Board-readable narratives.
• Documentation review and gap-finding — comparing policy to practice across hundreds of pages.

The pragmatic pieces we deliver

• Use-case prioritization workshop — score candidate AI use cases across ROI, risk, technical feasibility, and governance complexity. Output is a sequenced backlog of what to try first.
• AI governance framework — NIST AI RMF mapped onto your existing governance structure, with FERPA / HIPAA / IRB / NSPM-33 overlays per district of the campus city.
• Data-classification × LLM-provider matrix — for each institutional data class, which LLM providers and deployment modes are usable under their terms.
• Vendor AI claim audit — for the “AI-powered” features in your toolchain, what's real, what's marketing veneer, what's net-new compliance exposure.
• Faculty and researcher engagement framework — how to support legitimate AI use in teaching and research without creating institutional liability.
• Policy and standards — acceptable AI use, AI vendor procurement, AI in research, AI in advising, AI in advancement — templates calibrated to higher-ed.
• Pilot project support — SOC augmentation pilots, governance documentation pilots — designed so the institution can run subsequent pilots itself.

What “frictionless” means here

Your team uses AI where it genuinely helps and ignores it where it doesn't. Faculty, staff, and students have clear guidance — not blanket prohibitions or unwritten rules. Compliance and IRB don't get surprised by new AI use cases because the framework already anticipates them. Vendor AI claims get evaluated on outcomes, not marketing. The institution's capability to evaluate the next wave of AI is the durable output — not a single implementation that depends on us.

What we won't do

Honest boundaries — so an intro call isn't wasted on scope mismatches:

• Build you a custom institutional LLM. Almost always the wrong answer for higher education in 2026. Not our wheelhouse.
• Facilitate your faculty AI ethics committee. That work belongs with academic affairs and faculty governance — not with us.
• Solve academic integrity or plagiarism detection. Different domain, typically owned by the Provost's office. We can advise where AI use intersects with information security and data governance; we don't own the academic-integrity conversation.
• Tell you “buy this AI platform.” We're vendor-agnostic on this offering specifically. Our recommendations are based on fit, not on partnerships.

Frameworks and references we use

NIST AI RMF (the regulatory anchor), EDUCAUSE AI Working Group guidance, HEISC AI risk resources, hosting-provider data-handling terms (Anthropic, OpenAI, Google, Microsoft, AWS Bedrock — we maintain a working crosswalk), state AI legislation where applicable. Higher-ed-specific: IRB AI considerations, faculty governance models for AI ethics, academic-freedom doctrine, Title IX and AI-mediated bias considerations.

Engagement shape

8-week AI readiness and use-case prioritization → optional 12-week AI governance framework build → optional 4-week vendor AI audit → ongoing advisory (monthly cadence) for institutions that want a backstop as new vendor features and regulations land. Pilot projects scoped per use case.