Sector-Specific AI Regulation
advanced12 min readUpdated 2026-03-15
Sector-specific AI regulation covering FDA oversight of AI in medical devices, SEC model risk guidance, OCC banking AI requirements, and FTC enforcement against deceptive AI practices.
While horizontal AI frameworks like the EU AI Act and NIST AI RMF apply broadly, some of the most consequential AI regulation comes from sector-specific regulators who have adapted existing authority to address AI risks within their domains. Red teamers working in regulated industries must understand these sector-specific requirements because they create binding obligations that go beyond voluntary frameworks and carry significant enforcement penalties.
FDA: AI in Medical Devices (SaMD)
The Food and Drug Administration regulates AI and machine learning through its authority over Software as a Medical Device (SaMD). This is one of the most mature sector-specific AI regulatory frameworks.
Regulatory Pathway
| Pathway | Risk Level | AI Examples | Timeline |
|---|---|---|---|
| 510(k) (Substantial equivalence) | Low-moderate | AI-assisted image analysis, clinical decision support | 3-6 months |
| De Novo (Novel low-moderate risk) | Low-moderate, novel | New AI diagnostic categories without predicate devices | 6-12 months |
| PMA (Premarket approval) | High risk | AI systems making autonomous clinical decisions | 12-24 months |
| Breakthrough Device | Variable, urgent need | AI diagnostics for conditions with no alternatives | Expedited review |
FDA AI/ML Action Plan Requirements
| Requirement | Description | Red Team Testing Approach |
|---|---|---|
| Good Machine Learning Practice (GMLP) | Fundamental principles for AI/ML device development | Verify adherence to documented development practices |
| Predetermined Change Control Plan | Documentation of anticipated model updates and validation criteria | Test whether model updates follow the documented plan |
| Real-World Performance monitoring | Continuous monitoring of AI device performance in clinical settings | Assess monitoring effectiveness, test for performance degradation detection |
| Algorithm transparency | Clear documentation of algorithm function, training data, and limitations | Verify documentation accuracy against actual system behavior |
| Bias and robustness testing | Testing across diverse patient populations and clinical conditions | Adversarial robustness testing, demographic bias assessment |
Red Team Testing for AI Medical Devices
| Test Category | Methodology | Clinical Relevance |
|---|---|---|
| Adversarial perturbation | Apply adversarial perturbations to medical images to test diagnostic accuracy | Could cause missed diagnoses or false positives |
| Distribution shift | Test model performance with out-of-distribution data (different demographics, equipment, facilities) | Real-world deployment exposes models to diverse patient populations |
| Data poisoning | Assess vulnerability of continuous learning systems to training data manipulation | Could systematically degrade diagnostic accuracy |
| Boundary conditions | Test with edge-case clinical presentations that fall between diagnostic categories | Where clinical AI most often fails |
| Temporal drift | Assess model performance against evolving disease presentations and treatment protocols | Medical knowledge evolves; models must keep pace |
Key FDA Guidance Documents
| Document | Focus | Red Team Relevance |
|---|---|---|
| Artificial Intelligence and Machine Learning in Software as a Medical Device (2021) | Overall regulatory framework | Foundational requirements |
| Clinical Decision Support Software Guidance (2022) | CDS-specific requirements | Scope determination for clinical AI |
| Marketing Submission Recommendations for a Predetermined Change Control Plan (2023) | Model update governance | Testing requirements for model changes |
| Diversity Considerations in Clinical Studies (2024) | Demographic representation | Bias testing requirements |
SEC: Model Risk in Financial AI
The Securities and Exchange Commission addresses AI primarily through its existing authority over market integrity, investor protection, and regulated entity governance.
SEC Focus Areas for AI
| Focus Area | Regulatory Basis | Requirements |
|---|---|---|
| Predictive analytics in investor interactions | Regulation Best Interest, Investment Advisers Act | Must not use AI to place firm interests above client interests |
| AI in trading | Market manipulation rules, Regulation SCI | AI trading systems must not create market instability |
| AI in disclosure | Disclosure requirements, anti-fraud provisions | AI-related risks must be disclosed to investors |
| AI washing | Anti-fraud provisions | Must not misrepresent AI capabilities to investors |
| Cybersecurity | Reg S-P, Reg SCI | AI systems must meet cybersecurity requirements |
SEC Enforcement Actions and Guidance
The SEC has taken enforcement action against AI-related misrepresentation and signaled increasing scrutiny:
| Action/Guidance | Date | Key Takeaway |
|---|---|---|
| AI washing enforcement (DWS, Global Predictions) | 2024 | SEC penalizes companies for misrepresenting AI capabilities |
| Proposed rules on predictive analytics | 2023-ongoing | May require elimination of conflicts of interest in AI-driven advice |
| Reg SCI amendments discussion | 2024-ongoing | Expanding cybersecurity requirements to cover AI systems |
| Staff guidance on AI in investment advice | 2024 | Investment advisers must supervise AI-driven recommendations |
Red Team Testing for SEC-Regulated AI
| Test Category | Methodology | Regulatory Concern |
|---|---|---|
| Conflict of interest detection | Test whether AI recommendations favor the firm over clients | Reg BI, fiduciary duty violations |
| Market manipulation potential | Assess whether AI trading systems can be manipulated to create market instability | Market manipulation rules |
| AI capability verification | Verify that AI capabilities match marketing claims | Anti-fraud, AI washing concerns |
| Data security | Test data protection for AI systems handling investor data | Reg S-P requirements |
| Model validation | Independent validation of AI model performance claims | SR 11-7 model risk management |
OCC: Banking AI Requirements
The Office of the Comptroller of the Currency, along with the Federal Reserve and FDIC, has established expectations for AI use in banking through existing model risk management guidance and new AI-specific supervisory approaches.
SR 11-7 / OCC 2011-12: Model Risk Management
The foundational guidance for AI oversight in banking is SR 11-7, which applies to all models including AI and ML systems:
| SR 11-7 Component | Traditional Application | AI/ML Extension |
|---|---|---|
| Model development | Documented methodology, theoretical basis | Explainability requirements, training data documentation |
| Model validation | Independent testing, backtesting | Adversarial testing, bias assessment, robustness evaluation |
| Model governance | Model inventory, approval process | AI model lifecycle management, version control |
| Ongoing monitoring | Performance tracking, threshold alerts | Drift detection, adversarial monitoring, fairness metrics |
AI-Specific Banking Regulations and Guidance
| Regulation/Guidance | Scope | Key AI Requirements |
|---|---|---|
| Fair lending laws (ECOA, FHA) | Lending decisions | AI lending models must not discriminate against protected classes |
| BSA/AML | Anti-money laundering | AI transaction monitoring must be effective and explainable |
| CRA (Community Reinvestment Act) | Community lending | AI must not create digital redlining or exclude underserved communities |
| FCRA (Fair Credit Reporting Act) | Credit decisions | Adverse action notices must explain AI-driven credit decisions |
| Interagency fair lending guidance (2023-ongoing) | All lending AI | Specific expectations for testing AI lending models for discrimination |
Red Team Testing for Banking AI
| Test Area | Methodology | Regulatory Requirement |
|---|---|---|
| Disparate impact analysis | Test lending model outcomes across protected classes (race, gender, age, national origin) | ECOA, FHA compliance |
| Model extraction | Attempt to extract proprietary model logic through API queries | Model security, competitive protection |
| Adversarial evasion | Test whether AML monitoring can be evaded through adversarial transactions | BSA/AML effectiveness |
| Explainability testing | Assess whether AI decisions can be adequately explained to consumers | FCRA adverse action requirements |
| Input manipulation | Test whether applicant-side data manipulation can bias outcomes | Model robustness, fraud detection |
FTC: Deceptive AI Practices
The Federal Trade Commission uses its authority under Section 5 of the FTC Act (prohibiting unfair or deceptive acts) and other statutes to address AI-related consumer harms.
FTC Enforcement Focus Areas
| Focus Area | Legal Authority | Examples of Enforcement |
|---|---|---|
| Deceptive AI claims | Section 5 (deception) | Companies claiming AI capabilities that do not exist |
| Unfair AI practices | Section 5 (unfairness) | AI that causes substantial consumer harm that is not reasonably avoidable |
| AI and civil rights | Section 5, ECOA | AI that perpetuates discrimination |
| AI and children | COPPA | AI systems collecting data from children without parental consent |
| AI and health claims | FTC Act, Health Breach Notification Rule | AI health products making unsupported claims |
FTC AI Enforcement Actions
| Case | Year | Issue | Outcome |
|---|---|---|---|
| Rite Aid (facial recognition) | 2023 | Inaccurate facial recognition wrongly identified customers as shoplifters | 5-year ban on facial recognition, required security program |
| DoNotPay ("robot lawyer") | 2024 | Misrepresented AI as equivalent to a human lawyer | $193,000 penalty, prohibited from misleading claims |
| Evolv Technology (weapons detection) | 2024 | AI weapons detection made inaccurate safety claims | Required to stop deceptive marketing, notify customers |
| Amazon (Alexa/Ring and children) | 2023 | Retained children's voice data beyond necessity | $25M penalty, required data deletion |
Red Team Testing for FTC Compliance
| Test Category | Methodology | FTC Concern |
|---|---|---|
| Capability verification | Independently verify AI performance against marketing claims | Deceptive practices if claims are unsupported |
| Consumer harm assessment | Identify scenarios where AI outputs could cause financial, physical, or reputational harm | Unfair practices if harm is substantial and unavoidable |
| Dark patterns in AI | Test whether AI interfaces manipulate consumers into unwanted actions | Deceptive design practices |
| Data handling | Assess how AI systems collect, retain, and use consumer data | Data privacy, COPPA compliance |
| Bias testing | Test for discriminatory outcomes across protected classes | Civil rights violations |
Cross-Sector Red Team Methodology
Engagement Scoping by Sector
When scoping engagements in regulated industries, identify the applicable sector-specific regulators:
| If the Client Is... | Primary Regulator(s) | Key Testing Focus |
|---|---|---|
| Healthcare AI company | FDA, HHS/OCR | Clinical safety, HIPAA, bias across demographics |
| Bank or lender | OCC/Fed/FDIC, CFPB | Fair lending, model risk management, AML effectiveness |
| Investment firm | SEC, FINRA | Conflict of interest, AI washing, market stability |
| Consumer-facing AI company | FTC | Deceptive practices, privacy, consumer harm |
| Insurance company | State insurance commissioners, NAIC | Actuarial fairness, discrimination, rate-making accuracy |
| Telecommunications | FCC | Accessibility, robocall/spam detection accuracy |
Sector-Specific Report Structure
Red team reports for regulated industries should include sector-specific sections:
| Section | Content |
|---|---|
| Regulatory landscape | Which sector-specific regulations apply and why |
| Control mapping | Findings mapped to specific regulatory requirements |
| Enforcement risk assessment | Likelihood and severity of regulatory action based on findings |
| Remediation with regulatory context | Recommendations framed in terms of regulatory compliance |
| Examiner readiness | Whether the organization could demonstrate compliance during a regulatory examination |
Emerging Trends
Several trends are shaping the future of sector-specific AI regulation:
| Trend | Impact | Red Team Preparation |
|---|---|---|
| Interagency coordination | Multiple regulators collaborating on AI oversight | Expect multi-regulator examination scenarios |
| Mandatory testing | Regulators moving from guidance to mandatory testing requirements | Build standardized testing capabilities for each sector |
| Real-time monitoring | Shift from periodic examination to continuous monitoring | Develop automated continuous testing tools |
| Explainability mandates | Increasing requirement for AI decision explanations | Build explainability assessment into standard methodology |
| Third-party AI risk | Regulators scrutinizing use of third-party AI services | Include third-party AI assessment in engagement scope |
Red teamers who develop deep expertise in sector-specific regulation create significant competitive differentiation. The intersection of technical AI security skills and regulatory domain knowledge is where the most impactful and valued assessments occur.