Industries
Built for the sectors where AI risk has consequences
The organizations adopting AI fastest are often the ones carrying the heaviest regulatory, operational, and security constraints. That tension is our specialty — and it looks different in every sector.
Government and Public Sector
AI adoption under procurement, transparency, and public-trust constraints
Public-sector organizations are deploying AI into services where errors become press coverage and records requests. The playbooks written for startups do not survive contact with procurement, transparency law, or constituent trust.
AI adoption patterns
- Constituent service assistants and case-processing support
- Document summarization across records-heavy workflows
- Fraud, waste, and abuse detection
- AI capabilities arriving embedded in procured platforms
Primary risks
- Sensitive citizen data reaching unapproved AI services
- Decisions that must be explainable made by systems that were not designed to explain
- Vendor AI features expanding the data footprint without review
- Public records and retention obligations colliding with AI data flows
Regulatory and operational pressures
- State AI policies and executive directives moving faster than budgets
- Transparency and public-records law applied to AI-assisted decisions
- FedRAMP and state authorization requirements for cloud AI, where relevant
- Constituent trust as a non-renewable resource
Recommended starting engagements
- AI asset and shadow AI discovery across departments
- AI governance operating model aligned to NIST AI RMF
- AI red team of the highest-exposure constituent-facing system
- Executive briefing for agency leadership
Healthcare
AI near clinical workflows, PHI, and patient safety
Health systems are adopting AI for documentation, triage support, and operations while carrying HIPAA obligations and patient-safety expectations. The gap between pilot governance and production reality is where exposure concentrates.
AI adoption patterns
- Ambient clinical documentation and scribing
- Prior authorization and revenue-cycle automation
- Patient-facing assistants and triage support
- Imaging and diagnostic support tools from vendors
Primary risks
- PHI flowing into AI systems without BAAs or minimum-necessary analysis
- Retrieval systems surfacing records across care-team boundaries
- Clinical staff adopting unsanctioned AI tools under documentation pressure
- Vendor AI features changing data handling mid-contract
Regulatory and operational pressures
- HIPAA Security Rule risk analysis obligations covering AI data flows
- FDA expectations for AI-enabled clinical software, where applicable
- Board and medical-staff scrutiny of AI in care pathways
- Cyber insurance questionnaires that now ask about AI
Recommended starting engagements
- HIPAA-aligned AI risk assessment across current deployments
- AI red team of patient-facing or PHI-adjacent assistants
- AI governance operating model for clinical and administrative AI
- AI security training for clinical informatics and security teams
Financial Services
AI-assisted decisioning under regulatory examination
Banks, insurers, and fintechs are extending AI from analytics into decisioning and customer interaction — in a sector where model risk management is already a regulatory discipline and examiners ask for evidence, not enthusiasm.
AI adoption patterns
- Customer service assistants and agent-assist tooling
- Fraud detection and transaction monitoring augmentation
- Underwriting and credit-decision support
- Developer copilots inside regulated codebases
Primary risks
- AI-assisted decisions that cannot be reconstructed for examiners
- Customer data disclosure through assistants and retrieval layers
- Model risk frameworks that predate generative and agentic systems
- Third-party AI concentrated in a handful of critical vendors
Regulatory and operational pressures
- Model risk management expectations extended to generative AI
- GLBA, PCI DSS, and state privacy obligations across AI data flows
- Fair-lending and explainability scrutiny on AI-assisted decisions
- Examination findings as a forcing function for AI governance
Recommended starting engagements
- AI risk framework alignment with existing model risk management
- AI red team of customer-facing and decision-support systems
- AI vendor and platform risk review for critical dependencies
- Board briefing on AI risk posture and regulatory direction
Critical Infrastructure
AI security as an operational resilience issue
AI will increasingly touch forecasting, maintenance, dispatch, monitoring, safety, fraud detection, customer operations, and cyber defense. That makes AI security an operational resilience issue, not an innovation side quest.
AI adoption patterns
- Predictive maintenance and asset-health analytics
- Load, demand, and capacity forecasting
- Operational monitoring and anomaly triage support
- AI-assisted engineering and field documentation
Primary risks
- AI recommendations influencing operational decisions without validation gates
- IT/OT boundary erosion as AI platforms bridge both sides
- Manipulated inputs propagating into safety-adjacent workflows
- Vendor AI features reaching into control-adjacent networks
Regulatory and operational pressures
- Sector security directives and reporting obligations (e.g., CISA-aligned regimes)
- Reliability standards that assume deterministic systems
- Nation-state interest in exactly these environments
- Public consequence for what would elsewhere be a private outage
Recommended starting engagements
- AI asset inventory across IT, OT-adjacent, and vendor systems
- AI threat modeling for operationally connected AI workflows
- Custom critical infrastructure AI security exercise
- Critical infrastructure cyber advisory and resilience planning
Energy and Utilities
Grid-adjacent AI with reliability obligations
Utilities are applying AI to forecasting, outage prediction, vegetation management, and customer operations — while operating under reliability standards and a threat environment that treats the sector as a strategic target.
AI adoption patterns
- Demand forecasting and market-operations analytics
- Outage prediction and storm-response optimization
- Customer service automation for high-volume operations
- Field-work documentation and knowledge assistants
Primary risks
- Forecast and optimization inputs as a manipulation surface
- AI tools bridging enterprise IT and operations-adjacent data
- Customer PII concentrated in AI-assisted service platforms
- Long-lived OT environments meeting rapidly changing AI tooling
Regulatory and operational pressures
- NERC CIP and reliability obligations shaping what can connect to what
- State commission scrutiny of customer-data handling
- Federal attention to grid security and foreign threat activity
- Rate-case visibility into technology spending decisions
Recommended starting engagements
- AI governance and inventory for enterprise and operations-adjacent AI
- AI red team of customer-facing and forecasting-adjacent systems
- Tabletop exercise for an AI-involved operational scenario
- Cyber program modernization with AI-era threat assumptions
Manufacturing and OT-Adjacent Environments
AI on the plant floor's doorstep
Manufacturers are deploying AI for quality, scheduling, and maintenance in environments where the crown jewels are uptime and intellectual property — and where the network diagram still includes machines older than the security team.
AI adoption patterns
- Quality inspection and defect-detection systems
- Production scheduling and supply chain optimization
- Maintenance prediction and technician assistants
- Engineering copilots touching proprietary designs
Primary risks
- Proprietary process and design data flowing into AI tools
- AI platforms bridging enterprise and plant networks
- Supplier and partner AI integrations widening the attack surface
- Downtime consequences from AI-adjacent system compromise
Regulatory and operational pressures
- Customer and defense-supply-chain requirements (including CMMC where relevant)
- IP theft as a persistent, well-resourced threat
- Thin security staffing relative to environment complexity
- Just-in-time operations with little tolerance for disruption
Recommended starting engagements
- AI and data-flow inventory across enterprise and plant-adjacent systems
- Vendor security review for AI-enabled platforms
- AI security training for engineering and OT-adjacent teams
- Incident response planning that includes AI-involved scenarios
Technology and SaaS
Shipping AI features your customers must be able to trust
Software companies face AI risk twice: in the AI features they ship to customers and in the AI tools their own teams adopt. Customer trust, security questionnaires, and enterprise deals now hinge on credible answers to both.
AI adoption patterns
- AI features embedded across the product surface
- Developer copilots and AI-assisted engineering at scale
- Customer support automation with product-data access
- Internal agents wired into the SaaS toolchain
Primary risks
- Cross-tenant exposure through AI features and retrieval layers
- Prompt injection against customer-facing product features
- AI supply chain dependencies shipped to every customer
- Enterprise deals stalling on unanswered AI security questions
Regulatory and operational pressures
- SOC 2 and ISO 27001 expectations extending to AI features
- Enterprise customer security review of AI functionality
- EU AI Act obligations for products sold into Europe
- Competitive pressure to ship AI faster than review cycles run
Recommended starting engagements
- AI red team of customer-facing AI features before (or after) launch
- Secure AI SDLC integration for product engineering
- AI security questionnaire and trust-page substance
- ISO 42001 readiness as a differentiator in enterprise sales
Education and Research
Open environments, sensitive data, and AI everywhere
Universities and research institutions run some of the most open networks in existence while holding student records, health data, and federally funded research. AI adoption arrived campus-wide without waiting for a committee.
AI adoption patterns
- AI in teaching, advising, and student services
- Research computing with large-scale model training and use
- Administrative automation across decentralized units
- Broad, largely unmanaged personal AI tool adoption
Primary risks
- FERPA-protected and health data reaching consumer AI tools
- Export-controlled and sponsored research data in AI workflows
- Decentralized IT making inventory and governance genuinely hard
- Research credibility exposure from AI-related data incidents
Regulatory and operational pressures
- FERPA, HIPAA (for academic medical centers), and grant obligations
- Federal research security requirements and CMMC where applicable
- Academic freedom expectations constraining blunt policy tools
- Lean security teams covering sprawling estates
Recommended starting engagements
- Campus AI inventory and shadow AI discovery
- AI governance framework that respects academic decentralization
- AI security training for IT and research computing staff
- Focused assessment of student-facing AI services
Legal and Professional Services
Client confidentiality meets AI-assisted work product
Law firms and professional services firms are adopting AI for research, drafting, and knowledge management — in businesses where client confidentiality is the product and a single disclosure incident is a client-relationship event.
AI adoption patterns
- AI-assisted research, drafting, and document review
- Knowledge management assistants over matter and engagement files
- Client-facing deliverables produced with AI assistance
- Practice-group-level tool adoption ahead of firm policy
Primary risks
- Privileged and confidential client material in AI data flows
- Matter-level access boundaries not enforced at the retrieval layer
- Client outside-counsel guidelines restricting AI use — sometimes contractually
- Work-product quality risk from unverified AI output
Regulatory and operational pressures
- Professional responsibility and privilege obligations
- Client security audits and outside-counsel guidelines
- Malpractice exposure tied to AI-assisted work
- Competitive pressure to adopt AI for efficiency
Recommended starting engagements
- AI use policy and governance sized for a partnership
- Assessment of knowledge-management AI against matter boundaries
- AI security training for practice technology teams
- Client-ready AI security posture documentation
Discuss your sector's AI risk
Sector context changes everything about how AI risk should be assessed and governed. Email us — the first conversation is confidential and refreshingly free of discovery-call theater.