Regulatory Strategy — RCKCGROUP
The Stakes

Compliance isn't the last step. It determines whether the first step is even possible.

Every AI failure in a regulated institution traces back to the same root cause: the regulatory strategy was built after the solution was chosen — or never built at all.

At RCKCGROUP, regulatory strategy precedes everything. Before we recommend a platform, a use case, or a roadmap, we establish exactly what your compliance obligations require — and what they prohibit.

Consequence 01

Halted Deployments & Public Failures

When AI systems are deployed without proper HIPAA authorization, FedRAMP authorization to operate, or FISMA compliance, regulators halt them publicly. The reputational damage to institutions serving vulnerable populations outlasts the technology failure itself.

74%
of government AI projects face significant compliance delays
Consequence 02

Budget Overruns from Late-Stage Compliance

Retrofitting regulatory compliance into an AI system costs 3–5× more than building it in from the start. Organizations that skip upfront regulatory strategy often find themselves rebuilding entire architectures after procurement — at the worst possible time in the budget cycle.

average cost multiplier of late-stage compliance remediation
Consequence 03

Vendor Lock-In Without Legal Protection

AI vendors who are not compliant with your regulatory environment can create contractual, legal, and mission exposure that takes years to unwind. A platform-agnostic regulatory strategy gives procurement teams defensible evaluation criteria — and legal protection when vendors fall short.

1 in 3
healthcare AI vendors fail to meet HIPAA BAA requirements
Consequence 04

Leadership Accountability Without a Paper Trail

When AI systems cause harm in regulated environments, leadership is held accountable — regardless of whether they were involved in technical decisions. A documented regulatory strategy is the difference between institutional accountability and personal liability.

$1.9M
average HIPAA settlement for data exposure involving AI systems
02 — How We Work

Four principles that make regulatory strategy work in practice.

Every regulatory strategy engagement at RCKCGROUP is built on four non-negotiable principles. They are what separates a defensible compliance architecture from a compliance document that nobody reads.

01

Regulatory Obligations Are Mapped Before Strategy Is Written

No use case, platform recommendation, or roadmap is produced until we have a complete picture of your applicable regulatory obligations. Most consultants skip this step. We treat it as the only defensible starting point.

Compliance-First
02

Strategy Is Built for Legal Review, Not Technical Audiences

Regulatory strategy outputs are written for your legal counsel, compliance officer, and board — not your IT department. Every recommendation is framed in the language of obligation, risk, and defensibility rather than systems architecture.

Executive-Ready
03

Platform-Agnostic Advice, Always

We have no vendor relationships, no referral arrangements, and no incentive to recommend a specific platform. Our regulatory strategy gives you the evaluation criteria to assess any AI vendor against your specific compliance obligations — and the documentation to enforce those requirements in contracts.

Vendor-Neutral
04

Strategy Survives Leadership Transitions

We build regulatory strategy as institutional documentation — not as a consultant's deliverable. When leadership changes, budgets shift, or vendors are replaced, your regulatory strategy remains the authoritative record of how AI decisions were made and why they met your obligations.

Durable
03 — The Frameworks

Every framework we address — and what it means for your AI strategy.

Click any framework to see how it applies to AI deployments in your sector and what obligations it creates for your organization.

06
Frameworks
HIPAA
Health Insurance Portability & Accountability Act
Healthcare · Behavioral Health · Any AI handling PHI

HIPAA is the foundational compliance framework for any AI system that touches protected health information (PHI). The Privacy Rule, Security Rule, and Breach Notification Rule each create specific obligations for AI systems — from how training data is handled to how AI-generated clinical recommendations are stored and transmitted.

Privacy Rule
Defines permissible uses of PHI in AI training data, inference, and output storage. Requires minimum necessary standard for all AI processing.
Security Rule
Mandates administrative, physical, and technical safeguards for AI systems that store or transmit electronic PHI (ePHI).
BAA Requirement
All AI vendors with access to PHI must execute a Business Associate Agreement — and many standard AI vendor contracts are non-compliant by default.
AI Note
Most commercial LLM and AI platform agreements do not include HIPAA-compliant BAAs by default. Identifying which vendors can meet this requirement — and what modifications are needed — is one of the most critical early steps in any healthcare AI strategy.

FedRAMP establishes the security authorization requirements for cloud products used by federal agencies. Any AI platform deployed in or connected to a federal environment must either be FedRAMP authorized or be subject to an agency-specific ATO process. The consequences of non-compliance include halted contracts and program termination.

Authorization Levels
FedRAMP Low, Moderate, and High impact levels each define different security control requirements — the right level depends on the sensitivity of data your AI system handles.
ATO Process
Authority to Operate (ATO) is the formal approval process for non-FedRAMP-authorized systems. We guide organizations through ATO strategy and documentation.
Continuous Monitoring
FedRAMP authorization is not a one-time event — it requires continuous monitoring and monthly reporting, which must be built into AI system operations.
AI Note
The majority of leading AI platforms — including many large language model APIs — are not FedRAMP authorized. Federal agencies must understand this gap before committing to a vendor, and build alternative pathways into their procurement strategy.

FISMA requires federal agencies to develop, document, and implement an information security program for systems supporting their operations. AI systems are information systems under FISMA — and the law's requirements for categorization, documentation, and continuous monitoring apply directly.

System Categorization
Every federal AI system must be categorized by impact level (low, moderate, high) — a step that most AI procurement processes skip entirely.
Security Controls
NIST SP 800-53 provides the security control catalog for FISMA compliance — AI systems must implement applicable controls, including those specific to AI/ML risk.
Annual Reporting
FISMA requires annual reporting to OMB and Congress — AI systems that aren't properly documented create reporting gaps that expose agencies to oversight action.
AI Note
NIST's AI Risk Management Framework (AI RMF) was developed specifically to complement FISMA for AI systems. We integrate both frameworks to ensure your AI strategy is aligned with both legacy FISMA requirements and emerging federal AI governance standards.

TRICARE is the health care program for uniformed service members, retirees, and their families. AI deployments in military health and behavioral health environments face the intersection of HIPAA privacy requirements, DoD security requirements, and the specific clinical and ethical considerations governing care for military personnel and their families.

Beneficiary Data
TRICARE beneficiary data is subject to both HIPAA and DoD-specific privacy protections — AI systems must navigate both frameworks simultaneously.
Mental Health Parity
AI in military behavioral health must comply with mental health parity laws — AI-based care decisions that create disparate access or coverage outcomes create regulatory and mission risk.
DoD Security
AI systems in DoD environments operate under DoD Instruction 8500.01 and related cybersecurity policy — requirements that exceed standard FISMA obligations.
AI Note
The military behavioral health context introduces specific ethical and clinical AI considerations — including bias in mental health screening tools, suicide risk assessment AI, and the particular importance of human oversight in high-stakes clinical decisions for active duty populations.

NIST's AI RMF is the federal standard for trustworthy AI governance. While currently voluntary for most organizations, it is rapidly becoming an expected baseline — particularly in federal procurement and healthcare AI contexts. It provides the governance, accountability, and risk management structure that HIPAA, FedRAMP, and FISMA assume exists but don't define.

GOVERN Function
Establishes the organizational policies, roles, and accountability structures for AI — the governance layer that gives all other compliance obligations their institutional home.
MAP · MEASURE · MANAGE
The operational core of the AI RMF — identifying AI risks, measuring their likelihood and severity, and managing them through documented controls and processes.
Trustworthy AI Principles
Explainability, fairness, transparency, and human oversight — the AI RMF gives organizations the framework to operationalize these principles in regulated environments.
AI Note
We integrate NIST AI RMF into every regulatory strategy engagement as the governance backbone — it provides the organizational structure that makes HIPAA, FedRAMP, and FISMA compliance sustainable over time rather than a one-time audit exercise.

42 CFR Part 2 provides stricter privacy protections for substance use disorder (SUD) treatment records than HIPAA alone. For behavioral health organizations — particularly those serving military populations where SUD intersects with mental health care — AI systems that touch SUD records face a distinct and more stringent compliance layer that most AI vendors are unprepared for.

Disclosure Restrictions
42 CFR Part 2 prohibits disclosure of SUD records without specific patient consent — AI systems that aggregate clinical data must explicitly exclude or segregate Part 2 records.
Re-Disclosure Prohibition
Once disclosed under Part 2 consent, records cannot be re-disclosed without new consent — AI systems that share data downstream must enforce this prohibition programmatically.
2020 Amendments
Recent amendments allow more coordinated care while maintaining privacy protections — AI systems must be updated to reflect the amended consent and disclosure framework.
AI Note
Behavioral health AI is uniquely exposed under 42 CFR Part 2 — clinical AI tools that identify patterns in patient data, flag risk, or generate clinical summaries may inadvertently expose SUD treatment information without explicit Part 2 compliance controls. This is a critical gap in most behavioral health AI deployments.
04 — Strategy Architecture

Three layers. One defensible
compliance architecture.

Every regulatory strategy we build operates across three interdependent layers — governance, operations, and documentation.

Layer 01
Governance
Architecture

The organizational foundation of your regulatory strategy — defining who owns AI decisions, how they are made, and how accountability is documented. Governance architecture establishes the institutional structures that make your compliance posture defensible to regulators, auditors, and legal counsel.

AI Governance Policy Decision Authority Matrix Accountability Framework Ethics Review Process
Layer 02
Operational
Compliance

The day-to-day compliance controls that must be embedded into AI system operations — data handling procedures, access controls, audit logging, incident response protocols, and the specific technical safeguards required by each applicable framework. Operational compliance is where most AI deployments fail.

Data Handling Procedures Access Control Framework Audit Trail Requirements Incident Response Plan
Layer 03
Documentation
& Audit Readiness

The documentary record that proves your organization met its regulatory obligations — and that leadership made responsible, defensible decisions. Audit-ready documentation is not a retrospective exercise; we build it concurrently with your AI strategy so it exists before you need it.

Compliance Evidence Package Regulatory Decision Log Vendor Assessment Records Board Approval Documentation
100%
Principal-led
engagements
6+
Frameworks mapped
per engagement
3
Architecture layers
in every strategy
0
Vendor relationships
influencing advice
05 — What You Receive

Six documents your organization owns and can defend.

Every deliverable is built for a specific institutional audience — legal, compliance, executive, and operational — and designed to remain useful beyond the engagement.

Regulatory Exposure Report

A plain-language summary of your compliance obligations mapped to your specific AI use cases — written for legal and compliance review, not technical teams.

  • Framework-by-framework analysis
  • Use case risk classification
  • Legal review ready

AI Governance Policy

A fully drafted AI governance policy tailored to your organization's existing policy framework — covering decision authority, accountability, ethics review, and incident response.

  • Board-approvable policy language
  • Role and responsibility definitions
  • Aligns with NIST AI RMF

Vendor Evaluation Framework

Platform-agnostic scoring criteria for AI vendors that reflect your regulatory requirements — so procurement decisions are defensible and contractually enforceable.

  • Compliance verification checklist
  • BAA & ATO requirement guide
  • Contract language recommendations

Approval Pathway Documentation

Step-by-step documentation of every internal approval, audit checkpoint, and governance sign-off required before AI deployment — so nothing surfaces in procurement.

  • Pre-deployment checklist
  • Stakeholder sign-off matrix
  • Timeline for approval phases

Data Handling & Privacy Architecture

A documented data governance framework for your AI systems — covering PHI handling, training data provenance, inference data minimization, and breach notification triggers.

  • Data flow diagrams for AI systems
  • Minimum necessary standard guidance
  • Breach notification protocol

Audit-Ready Evidence Package

A compiled documentation package that demonstrates your organization met its regulatory obligations — built to survive an OIG audit, HHS review, or IG inquiry without additional preparation.

  • Regulatory decision log
  • Compliance evidence index
  • Board approval records
06 — Engagement Model

Four weeks to a defensible
regulatory architecture.

A focused, principal-led engagement. No junior analysts. No offshore teams. No templates.

Week 1

Regulatory Scoping & Intake

Principal-led discovery call. Organizational profile, applicable framework identification, and current compliance posture review. Existing policies and vendor contracts reviewed for gaps.

Week 2

Framework Analysis & Mapping

Detailed analysis of each applicable framework against your use cases and data environment. Exposure points identified, risk levels assigned, and obligation inventory completed.

Week 3

Strategy & Architecture Development

Governance architecture drafted. Vendor evaluation criteria built. Approval pathway documented. Data handling framework designed. All deliverables reviewed with legal counsel if available.

Week 4

Delivery & Executive Briefing

Full deliverable package transferred. Board briefing delivered. Audit-ready evidence package compiled. 90-day implementation priorities defined. Your team owns everything — no ongoing dependency.

"The first call is always with Richard Kohn — not a project manager, not an analyst, not a coordinator. Every engagement is led by the principal from intake to delivery. That's not a feature. It's how we guarantee the quality of the strategy you receive."
— RCKCGROUP Engagement Standard
07 — Who This Is For

Built for leaders who are
accountable for what comes next.

This service is designed for organizations at a compliance inflection point — where the cost of getting AI wrong is institutional, not just operational.

Government Organizations

Federal, state, and defense agencies adopting AI under federal compliance mandates
  • Federal agencies that must demonstrate FedRAMP and FISMA compliance before deploying AI in any production environment
  • State health agencies that receive federal funding and face both federal and state AI regulatory requirements
  • DoD and defense health organizations operating under TRICARE and DoD cybersecurity policy
  • Agencies facing IG or Congressional scrutiny of AI adoption plans and needing a defensible documentation trail
  • CIOs and CTOs who need board-ready regulatory strategy before any procurement decision is made

Healthcare & Behavioral Health

Clinical systems and behavioral health organizations with HIPAA and 42 CFR Part 2 obligations
  • Healthcare systems deploying AI in clinical decision support, documentation, or care coordination workflows
  • Behavioral health organizations handling SUD records subject to 42 CFR Part 2 alongside HIPAA
  • Compliance officers and legal counsel who need a regulatory architecture before vendor selection begins
  • Organizations that have received HIPAA enforcement notices or are under OIG review and need immediate compliance documentation
  • CMOs and CNOs who need clinical AI governance structures that satisfy both regulatory and professional ethics requirements
Ready to Begin

Build AI strategy your
regulators can't challenge.

The first conversation is a direct exchange with Richard Kohn — not a discovery call with a sales team. We'll assess your regulatory exposure and tell you exactly what a strategy engagement needs to address.

No pitch deck. No sales cycle. A direct conversation.