Applied AI
Every executive team is being pushed to deploy AI faster than their governance is ready for, with privilege violations, copyright exposure, biased decisioning, and vendor lock-in as the actual risks. We build AI-governance programs aligned with the NIST AI RMF, EU AI Act, and sector-specific guidance — letting your organization move fast safely.
Applied AI 101
What Applied Artificial Intelligence (Applied AI) actually means.
Applied AI is AI that does real work inside your organization — not a vendor demo, not a proof of concept that never ships, and not a deployment that ignores your regulatory environment and data handling obligations. It is AI selected for the right use cases, governed before it is deployed, and integrated into operations in a way your legal, compliance, and technical teams can stand behind.
The distinction matters. Most organizations already have AI in use — staff are pasting sensitive documents into public models, marketing has launched a chatbot, and someone in operations is running a pilot nobody approved.
Applied AI is the practice of making that intentional: identifying where it creates real value, building the governance that protects you from the exposure, and deploying it in a way that doesn't create a liability you haven't accounted for.
AI is in use — but there is no policy, no oversight committee, no model inventory, and no documented process for approving or reviewing what gets deployed.
- No AI use policy or acceptable-use rules providing written guidance
- No approval process for new deployments
- Audit would find no documented controls
Staff are using public LLMs with sensitive, confidential, or privileged data — and leadership either doesn't know the extent, understand the exposure, or care.
- Public models ingesting internal data and training on it
- No visibility into what's being submitted
- Privilege, HIPAA, or GLBA exposure in the wild
Healthcare, financial services, legal, or federal — sectors with specific legal obligations that generic vendor frameworks were never designed to satisfy.
- Obligations span HIPAA, GLBA, FERPA, ITAR, and ABA Model Rules
- EU AI Act and US state laws already apply
- Cyber-insurance AI questionnaires going unanswered
The use case is approved and the organization wants to move from pilot to production — with a private or self-hosted model rather than a public-cloud dependency.
- Pilot proved value, production needs infrastructure
- Self-hosted or private-cloud deployment required
- Needs technical depth without a vendor conflict
AI that isn't governed isn't a competitive advantage — it's a liability you haven't discovered yet.
Overview
Use AI at pace — with the governance, controls, and oversight the risk actually requires.
The Pressure
The board wants AI deployed now, but the legal and regulatory exposure is no longer hypothetical.
- Privilege and confidentiality leaks — public LLMs ingest whatever staff paste in, a live concern under ABA Formal Opinion 512 for law firms and parallel duties elsewhere.
- Copyright and IP exposure from generative outputs, plus biased decisioning that lands in EEOC complaints or state AG enforcement.
- Active regulation — the NIST AI RMF 1.0 and Generative AI Profile, OMB M-24-10 for federal agencies, the EU AI Act with extra-territorial reach, and state laws (Colorado AI Act, NYC Local Law 144) are all in enforcement.
- Vendor lock-in that only becomes obvious after workflows are built on a single model or hyperscaler.
- Cyber-insurance carriers are now adding AI-specific questions to renewal questionnaires.
The Gap
The frameworks on the market come with conflicts of interest and ignore the rules you actually live under.
- Conflicted authors — most circulating AI-governance frameworks were built by AI vendors, AI law firms, or AI training companies selling the thing they govern.
- Sector-blind — generic frameworks ignore HIPAA, GLBA, FERPA, ITAR, and the ABA Model Rules.
- Shadow pilots — deployments start in marketing or sales with no IT or legal in the loop, exactly the high-risk use case the EU AI Act was written to catch.
- Infrastructure debt — private-LLM deployments need depth most teams lack, so they default back to the same hyperscaler relationships they meant to diversify away from.
- Boilerplate contracts — AI vendor agreements go unreviewed on training-data use, output IP, and breach notification.
How We Help
We build vendor-neutral AI governance that reduces your exposure, protects sensitive data, and keeps your vendor options open.
- Use-case prioritization — separate the real-ROI, manageable-risk cases from the hype.
- Framework-mapped program to the NIST AI RMF 1.0, EU AI Act, OMB M-24-10, and sector obligations — multi-framework where required, integrated rather than bolted on.
- Vendor and model evaluation plus operationally sustainable private-deployment patterns that avoid infrastructure overbuild.
- AI vendor contract review targeting training-data use, model-output IP, breach notification, and cross-border transfer.
- Integrated delivery with our Consulting, GRC, Third-Party Risk Management, and Cloud Migration practices when private infrastructure is part of the work.
Your organization deploys AI with documented controls that hold under audit, managed data exposure, and the vendor flexibility to change course.
Engagement Complexity
We meet you where you need to begin.
Not every organization needs the same level of engagement. Some need a focused, fast answer. Others are ready for a full program. The right starting point is wherever you actually are.
Start here if you know you need AI but have no idea where to begin. We map the need before anything is scoped.
Separating the AI opportunities with real ROI and manageable risk from the ones that are noise, hype, or liability waiting to happen.
Acceptable-use rules and AI policy foundations for organizations that need governance in place before anything is deployed.
AI literacy for the people who will actually use it — what it is, what it isn't, responsible use, and when to trust or question the output.
First structured test with a defined scope, a hypothesis, and a clear success measure before anything scales.
Time entries auto-drafted from attorney work activity — fewer write-offs, less narrative reconstruction.
AI-accelerated review of large datasets with human oversight built in — faster review, lower cost per document.
Clause extraction, risk flagging, and contract comparison at scale — without reading every line manually.
AI embedded inside your existing tools and processes — not bolted on as a separate product staff won't use.
AI-drafted clinical notes from practitioner dictation and activity — reducing documentation burden without sacrificing accuracy or compliance.
AI identification of anomalous transaction and behavioral patterns for financial institutions — earlier signals, fewer false positives.
Policy, oversight committee, model inventory, and audit trails — controls that hold up under regulatory scrutiny.
Neutral model and platform selection — OpenAI, Anthropic, Google, Meta, and self-hosted options assessed against your constraints.
AI programs mapped to NIST AI RMF, EU AI Act, OMB M-24-10, and your sector's specific legal obligations.
AI correlation of threat data across classified and unclassified sources — turning raw intelligence volume into actionable analyst output for NatSec and defense organizations.
AI-driven pattern recognition across large investigative datasets for law enforcement agencies — surfacing connections that manual review at scale cannot.
Self-hosted LLM behind your firewall with full data control, no training-data exposure, and no hyperscaler dependency.
Capabilities
What we deliver
AI Governance Framework
Policy, oversight committee, risk assessment, model inventory, audit trails — survivable under audit.
Use-Case Identification
Find the AI use cases where ROI is real and risk is manageable. Skip the rest.
Vendor & Model Selection
Vendor-neutral evaluation of OpenAI, Anthropic, Google, Microsoft, Meta, and self-hosted options.
Pilot to Production
Hypothesis-driven pilots with measurable outcomes; promotion to production with operations support.
Privacy & Compliance
HIPAA, GLBA, FERPA, attorney-client privilege — AI architectures that respect each.
Workforce Enablement
Training, governance councils, and the change management that makes adoption real.
Use AI without the regret
Let's separate the high-value use cases from the hype — and build the governance to scale safely.