AI Washing: The Financial Industry’s New Greenwashing Problem — And Why Regulators Are Coming for It”

Last Updated: March 18, 2026

Artificial intelligence has become the most powerful marketing magnet in modern finance. From robo-advisors promising algorithmic perfection to hedge funds claiming machine-learning alpha generation, AI capabilities are now standard pitch deck fodder. But beneath the glossy presentations and technical whitepapers lies a growing deception that regulators are calling “AI washing”—the practice of exaggerating, misrepresenting, or outright fabricating artificial intelligence capabilities to attract investors and customers.

Like greenwashing before it, where companies made false environmental claims to exploit sustainability trends, AI washing exploits the mystique and promise of artificial intelligence to mislead stakeholders. The consequences extend far beyond marketing ethics. When investment advisers claim AI-driven strategies that don’t exist, when fintech startups raise millions on fabricated machine-learning capabilities, and when public companies tout algorithmic sophistication that’s actually manual spreadsheet work, investor capital flows into illusory technology rather than genuine innovation.

This comprehensive analysis examines the mechanics of AI washing, documents real enforcement cases that have shaped regulatory responses, analyzes the global crackdown now underway, and provides investors with practical tools to identify and avoid these deceptions.

Understanding AI Washing: Definition, Mechanics, and Prevalence

What Is AI Washing?

AI washing refers to the practice of making false, misleading, or exaggerated claims about the use, capabilities, or sophistication of artificial intelligence in products, services, or investment strategies. The term deliberately echoes “greenwashing,” acknowledging the parallel between environmental deception and technological misrepresentation. Where greenwashing misleads about sustainability, AI washing misleads about capability—and in finance, capability translates directly to value, risk assessment, and competitive advantage.

The practice manifests in several distinct forms:

AI Washing Type	Description	Financial Sector Example
Capability Exaggeration	Overstating the sophistication or scope of actual AI systems	Claiming “proprietary AI algorithms” when using basic statistical models
Complete Fabrication	Claiming AI capabilities that do not exist whatsoever	Marketing “AI-powered investment forecasts” with no AI infrastructure
Human Masking	Presenting manual human work as automated AI processing	Offshore workers manually processing transactions marketed as “AI automation”
Third-Party Obfuscation	Using third-party AI tools while claiming proprietary technology	White-labeling generic AI platforms as unique in-house developed systems
Future Capability Claims	Promising AI features that are merely planned or conceptual	Raising capital for “AI-driven” products with no development timeline or technical team

The Scope of the Problem

The prevalence of AI washing extends beyond isolated bad actors. A comprehensive study by MMC Ventures analyzing 2,830 European startups found that a startling 40% of companies claiming to be AI startups had minimal actual AI utilization. This finding suggests that AI washing is not merely a marketing exaggeration but a systemic issue affecting nearly half of self-identified AI companies.

In the financial sector specifically, the problem is acute. Investment management, fintech lending, algorithmic trading, and financial advisory services have all seen rapid AI capability claims. The complexity of financial AI systems—often involving proprietary algorithms, confidential datasets, and sophisticated modeling—makes verification difficult for investors and regulators alike. This opacity creates fertile ground for deception.

Real Cases: When AI Washing Became Securities Fraud

Regulatory enforcement has moved from warning to action. The following cases demonstrate how AI washing crosses from marketing hype into securities fraud, with real financial penalties and legal consequences.

Case 1: Delphia (USA) Inc. — The Data Collection Deception (March 2024)

In March 2024, the Securities and Exchange Commission (SEC) charged Delphia (USA) Inc., a Toronto-based investment adviser, with making false and misleading statements spanning a three-year period about its use of AI and machine learning.

The Claims: Delphia marketed that it “uses machine learning to analyze collective data shared by its members to make intelligent investment decisions” and that it “put[s] collective data to work to make our artificial intelligence smarter so it can predict which companies and trends are about to make it big and invest in them before everyone else.” The company claimed its proprietary algorithm used client data to make stock selections across thousands of publicly traded companies up to seven financial quarters in the future.

The Reality: While Delphia did collect client data intermittently between 2019 and 2023, it never used that data with artificial intelligence or machine learning. The claimed predictive capabilities simply did not exist. The algorithms that supposedly processed collective intelligence were conventional investment approaches without AI integration.

The Penalty: Delphia agreed to a cease-and-desist order, censure, and civil penalty of $225,000 without admitting or denying the charges. The SEC emphasized that Delphia’s misleading statements were material because they represented to investors that AI-powered data analysis was a key characteristic distinguishing the firm from competitors.

Case 2: Global Predictions Inc. — The “First Regulated AI Financial Advisor” (March 2024)

Simultaneously with the Delphia action, the SEC charged Global Predictions Inc., a San Francisco-based investment adviser, for making false and misleading statements about its AI capabilities.

The Claims: Global Predictions falsely stated on its website that its technology incorporated “expert AI-driven forecasts” and claimed to be the “first regulated AI financial advisor” across its website, social media platforms, and client communications. These claims positioned the firm as a technological pioneer in the investment advisory space.

The Reality: When challenged by SEC investigators, Global Predictions could not produce documents substantiating its “first regulated AI financial advisor” claim. The “expert AI-driven forecasts” were not generated by the sophisticated machine learning systems implied but by conventional analytical methods.

The Penalty: Global Predictions agreed to pay a $175,000 civil penalty, accept censure, and cease-and-desist from future violations. Combined with Delphia, these cases established the SEC’s enforcement framework and signaled that AI-related misrepresentations would be treated with the same severity as other material misstatements.

Case 3: Rockwell Capital Management — The Non-Existent AI (February 2024)

In February 2024, the SEC settled fraud charges against Brian Sewell and his company, Rockwell Capital Management, for raising $1.2 million for a cryptocurrency investment fund.

The Claims: Sewell claimed the fund would employ “machine algorithms,” “artificial intelligence,” and a “machine learning model” to guide investment strategies and generate superior returns in cryptocurrency markets. These claims suggested sophisticated technological infrastructure capable of processing vast datasets to identify trading opportunities.

The Reality: The AI and machine-learning technology Sewell described never existed. The fund operated without any algorithmic infrastructure, yet continued to market based on these fabricated capabilities while raising capital from investors specifically attracted by the AI component.

The Penalty: Rockwell Capital Management agreed to pay disgorgement and prejudgment interest totaling $1,602,089, while Sewell personally paid a civil penalty of $223,229. The case demonstrated that AI washing in capital raising triggers not just regulatory penalties but full fraud liability.

Case 4: Presto Automation — The Human Behind the AI (January 2025)

In January 2025, the SEC announced its first AI washing enforcement action against a public company, charging Presto Automation Inc., a formerly Nasdaq-listed technology firm.

The Claims: Presto marketed “Presto Voice,” an AI product for restaurant drive-throughs, claiming it eliminated the need for human order-taking through sophisticated speech recognition and natural language processing. The company created the impression that its AI technology was proprietary and that automation had replaced human intervention in the ordering process.

The Reality: SEC investigation revealed that the AI speech recognition technology was actually owned and operated by a third party, not proprietary to Presto as implied. More significantly, the “vast majority” of drive-through orders required human intervention—directly contradicting marketing claims that AI had eliminated the need for human involvement. The automation rates claimed were essentially achieved through offshore human workers, not artificial intelligence.

The Penalty: Presto reached a non-monetary settlement with the SEC, agreeing to compliance undertakings without financial penalty but with significant reputational and operational consequences. The case established that even public companies face AI washing liability and that the SEC will conduct detailed technical forensics comparing marketing claims against actual system performance data.

Case 5: Nate Inc. — The $42 Million Manual Processing Fraud (April 2025)

In April 2025, both the SEC and Department of Justice (DOJ) charged Albert Saniger, former CEO of Nate Inc., with criminal and civil fraud for raising over $42 million based on fabricated AI capabilities.

The Claims: Saniger claimed that Nate’s mobile shopping app used proprietary AI to complete online purchases automatically, processing transactions without human intervention. The company marketed automation rates above 90%, suggesting near-total AI processing capability that would revolutionize e-commerce.

The Reality: According to SEC allegations, nearly all orders were manually processed by human workers in the Philippines and other locations—not by AI systems. The purported automation rates of 90%+ were essentially zero in reality. Saniger allegedly fabricated success metrics and continued to raise capital based on these misrepresentations.

The Status: Because Saniger resides in Spain, the SEC has not yet been able to serve him with the complaint under the Hague Convention, creating jurisdictional complexity in the enforcement action. The parallel DOJ criminal charges indicate the seriousness of AI washing as fraud rather than mere regulatory non-compliance.

Case 6: The “Human-Assisted AI” App Development Scam

A Wall Street Journal investigation revealed a startup that claimed its “human-assisted AI” could enable development of mobile apps with minimal effort and time, attracting nearly $30 million in investment from AI-focused venture capital funds.

The Reality: The “AI” was largely reliant on “good old fashioned human intelligence of software engineers”—human developers manually coding what was marketed as automated AI generation. The “human-assisted” framing served as linguistic cover for what was essentially a traditional software development shop with minimal AI integration.

The Regulatory Crackdown: Global Enforcement Landscape

The enforcement actions of 2024-2025 represent not isolated incidents but the beginning of systematic regulatory crackdowns. Authorities worldwide are recognizing AI washing as a threat to market integrity and investor protection, deploying new units and regulatory frameworks to combat it.

United States: The SEC’s Cybersecurity & Emerging Technologies Unit (CETU)

The SEC’s most significant structural response to AI washing came with the creation of the Cybersecurity & Emerging Technologies Unit (CETU). This specialized unit consolidates expertise in technology, securities law, and digital forensics to investigate:

• Misleading AI disclosures in SEC filings and public statements
• AI-driven deception and online scams targeting retail investors
• Promotional overstatements of “AI-driven” investment strategies
• False or incomplete disclosures about AI-related cybersecurity incidents
• Crypto and blockchain fraud with AI components

CETU represents a fundamental shift from passive oversight to active investigation. The unit’s mandate explicitly includes technical validation—requiring companies that claim AI capabilities to prove them through documentary evidence, system architecture analysis, and performance data review.

Former SEC Chair Gary Gensler established the enforcement framework with clear public statements: “If you claim to use AI in your investment processes, you need to ensure that your representations are not false or misleading.” Current enforcement leadership has maintained this posture, with the SEC’s examination priorities for fiscal year 2026 specifically including “training and security controls that firms are employing to identify and mitigate new risks associated with artificial intelligence.”

United Kingdom: FCA and ASA Coordination

The UK addresses AI washing through existing regulatory frameworks rather than specialized AI legislation:

• Financial Conduct Authority (FCA): Requires that all communications be “clear, fair, and not misleading”—a standard that applies equally to AI claims as to other financial promotions
• Advertising Standards Authority (ASA) and Competition and Markets Authority (CMA): Joint enforcement against misleading technology claims in consumer advertising
• “Finfluencer” Crackdown: Specific targeting of social media promoters marketing “AI trading bots” and automated investment schemes to retail investors

European Union: The AI Act and Transparency Obligations

The EU AI Act, which took effect on August 1, 2024, with full enforcement beginning August 2, 2027, creates the world’s most comprehensive AI regulatory framework. For financial services, the Act introduces:

• Transparency and documentation obligations: Companies must maintain technical documentation substantiating AI claims
• Risk-based categorization: AI systems used in financial services face heightened scrutiny as “high-risk” applications
• Administrative fines: Penalties for misleading statements or incomplete information about AI capabilities can reach 6% of global annual turnover

Canada: CSA Warnings and AIDA Framework

The Canadian Securities Administrators (CSA) have explicitly warned against AI washing in investment marketing. Canada’s proposed Artificial Intelligence and Data Act (AIDA), if revived, would impose AI risk-governance obligations similar to the EU regime, requiring evidence-based claims and technical documentation.

Enforcement Trends and Future Direction

Regulatory convergence is emerging around several key principles:

Regulatory Trend	Implication for Companies	Implication for Investors
Evidence-Based Claims	Must maintain technical documentation, testing data, and model validation records	Can demand proof of AI capabilities before investing
Cross-Functional Sign-Off	Engineering, legal, compliance, and marketing must jointly validate AI claims	Should verify that companies have internal AI governance committees
Individual Liability	Executives and board members face personal liability for AI misrepresentations	Can pursue legal action against individuals, not just corporations
Technical Forensics	Regulators employ digital forensics to verify AI architecture and training data	Should seek independent technical audits of claimed AI systems
Parallel Criminal Prosecution	DOJ pursuing criminal fraud charges alongside SEC civil actions	AI washing now carries potential criminal, not just civil, consequences

AI Washing vs. Greenwashing: Critical Parallels and Distinctions

The comparison between AI washing and greenwashing provides both analytical framework and predictive insight. Regulators have spent over a decade developing greenwashing enforcement mechanisms, and these are being rapidly adapted to AI deception.

Structural Similarities

Both practices share common characteristics that make them attractive to bad actors and damaging to markets:

• Exploitation of investor sentiment: Greenwashing targets environmental consciousness; AI washing targets technological optimism and fear of missing out on the AI revolution
• Asymmetric information: Both sustainability metrics and AI capabilities require specialized expertise to verify, creating information asymmetries that enable deception
• Competitive pressure: Markets reward both environmental credentials and AI sophistication, creating pressure to exaggerate when genuine capabilities are expensive or difficult to develop
• Material misrepresentation: Both can constitute securities fraud when they affect investment decisions, regardless of whether separate “greenwashing” or “AI washing” statutes exist
• Whistleblower-driven investigations: Both are often exposed by internal whistleblowers with technical knowledge of the deception

Why AI Washing May Be More Dangerous

Despite the parallels, AI washing presents distinct risks that may make it more problematic than greenwashing:

1. Immediate operational impact: Misrepresented AI capabilities in financial services directly affect investment performance, risk management, and operational resilience—consequences that manifest faster than environmental harms
2. Algorithmic opacity: While carbon footprints can eventually be measured, AI decision-making processes can remain opaque even to creators, making verification and ongoing monitoring more difficult
3. Cascading systemic risk: Financial institutions adopting purported AI risk models that don’t actually work can create systemic vulnerabilities affecting market stability
4. Rapid evolution: AI technology evolves faster than environmental science, making regulatory frameworks and verification standards perpetually behind the curve
5. Dual-use deception: AI washing often involves both marketing misrepresentation and operational fraud—manual processes masked as automation affects service delivery, not just investor perception

Regulatory Learning from Greenwashing

The SEC’s approach to AI washing explicitly builds on greenwashing enforcement experience. The creation of specialized units (the ESG Task Force in 2021 for greenwashing, CETU in 2025 for AI washing) demonstrates regulatory recognition that theme-based trend marketing requires targeted oversight. The message is consistent across both domains: claims tied to core operations must be substantiated, and marketing buzzwords do not insulate companies from fraud liability.

Why AI Washing Persists: Incentives and Detection Challenges

Understanding why AI washing remains prevalent despite regulatory attention requires examining both the incentives driving the behavior and the structural difficulties in detecting it.

The AI Investment Premium

Companies with credible AI capabilities command valuation premiums in both public and private markets. Venture capital funds specifically targeting AI have raised billions, and public market investors pay multiples for “AI-enabled” business models. This premium creates powerful incentives for companies to rebrand existing technology as AI or exaggerate limited capabilities.

The case of the startup raising $30 million on “human-assisted AI” claims illustrates this dynamic. The AI label attracted specialized venture capital that might not have invested in a traditional software development company, regardless of actual technical capabilities.

Verification Asymmetries

AI systems are inherently difficult to verify externally. Proprietary algorithms, confidential training data, and complex model architectures create natural opacity. When companies claim “machine learning” or “predictive AI,” investors rarely have technical access to validate these claims. Even sophisticated institutional investors often rely on management representations and superficial demonstrations rather than technical audits.

The “Black Box” Defense

Companies can exploit legitimate AI complexity to resist scrutiny. When questioned about AI capabilities, firms may cite trade secrets, proprietary methodologies, or the inherent complexity of machine learning systems. This “black box” defense—legitimate for genuine AI—becomes a shield for AI washing when deployed to deflect due diligence.

Rapid Market Evolution

The velocity of AI development means that claims that seem futuristic may become plausible within months. Companies can make exaggerated claims hoping to develop actual capabilities before investor scrutiny intensifies, or before regulatory enforcement catches up. This “fake it till you make it” approach is particularly prevalent in startup fundraising, where technical development timelines are often optimistic.

Investor Protection: A Practical Checklist to Detect AI Washing

Given regulatory enforcement limitations and the prevalence of AI washing, investor self-protection remains essential. The following checklist provides systematic due diligence criteria for evaluating AI claims in financial investments.

Phase 1: Documentation Review

1. Technical Architecture Documentation

Request and review technical whitepapers, system architecture diagrams, and algorithmic descriptions. Genuine AI implementations can provide detailed (if redacted) documentation of:

• Model types (neural networks, random forests, natural language processing architectures)
• Training data sources, size, and preprocessing methodologies
• Infrastructure requirements (GPU clusters, cloud computing resources, data storage)
• Development team composition (data scientists, ML engineers, PhD-level researchers)

Red Flag: Companies that provide only marketing materials, case studies, or high-level concept diagrams without technical depth.

2. Patent and IP Portfolio

Verify claimed proprietary AI through patent filings, published research, and open-source contributions. While not all AI is patentable, significant claimed innovations typically leave intellectual property trails.

Red Flag: Claims of “proprietary algorithms” with no patent applications, published research, or technical team with relevant publication history.

3. Third-Party Validation

Seek independent technical audits, academic partnerships, or industry certifications. Reputable AI vendors often undergo SOC 2 audits, ISO certifications, or academic validation studies.

Red Flag: Absence of any third-party technical validation; refusal to permit independent technical review.

Phase 2: Operational Verification

4. Live Demonstration Requirements

Insist on live demonstrations with real-time data inputs, not scripted presentations or pre-recorded outputs. Genuine AI systems can process novel inputs and demonstrate adaptive responses.

Red Flag: Refusal to demonstrate systems with real-time inputs; reliance on pre-recorded videos or simulated outputs; “demo environments” that differ from production systems.

5. Human Intervention Transparency

Demand explicit disclosure of human involvement in processes claimed as “AI-automated.” The Presto Automation case demonstrates that many “AI” systems rely heavily on human workers.

Key Questions:

• What percentage of [claimed AI process] requires human review or intervention?
• Where are human workers located and what functions do they perform?
• How does the system handle edge cases or exceptions?

Red Flag: Vague answers about “human oversight” without specifics; claims of “100% automation” for complex cognitive tasks; offshore operations centers performing functions attributed to AI.

6. Performance Metrics Audit

Verify that claimed performance metrics (accuracy rates, automation percentages, return improvements) are:

• Calculated using consistent, industry-standard methodologies
• Audited by independent third parties
• Disclosed with appropriate time periods and sample sizes
• Reconcilable with overall business performance

Red Flag: Metrics that cannot be independently verified; performance claims inconsistent with overall business results; metrics that “improve” suspiciously consistently across reporting periods.

Phase 3: Team and Infrastructure Assessment

7. Technical Team Verification

Evaluate the claimed AI team’s credentials, experience, and capacity:

• Review LinkedIn profiles and publication records of key technical personnel
• Verify academic credentials and previous AI/ML employment
• Assess team size relative to claimed AI capabilities (a “proprietary AI platform” claimed to be developed by 2-3 engineers is implausible)

Red Flag: Technical teams lacking relevant AI/ML backgrounds; teams too small for claimed capabilities; high turnover in technical roles; predominance of marketing/sales personnel over engineers.

8. Infrastructure Investment Evidence

Genuine AI systems require substantial infrastructure investment. Review:

• Cloud computing expenses (AWS, Azure, GCP) relative to company size
• GPU and specialized hardware procurement
• Data storage and processing capabilities
• Research and development expenditure as percentage of revenue

Red Flag: Minimal technology infrastructure spending; R&D expenditures inconsistent with claimed AI development; reliance on consumer-grade computing resources for claimed sophisticated AI.

Phase 4: Regulatory and Legal Review

9. Disclosure Consistency Analysis

Compare AI claims across different documents and contexts:

• SEC filings vs. marketing materials vs. investor presentations
• Current claims vs. historical statements (has “AI” replaced previous descriptions of the same technology?)
• Claims in different jurisdictions (some companies make bolder claims in less regulated markets)

Red Flag: Inconsistent descriptions of AI capabilities; significant escalation in AI claims without corresponding technical announcements; claims in marketing materials that don’t appear in regulatory filings.

10. Regulatory History and Compliance

Research the company’s regulatory history:

• SEC examination letters or enforcement actions
• FINRA complaints or disciplinary actions
• State securities regulator actions
• Consumer protection complaints related to technology claims

Red Flag: History of regulatory actions for misleading claims; pattern of name changes or corporate restructuring following regulatory scrutiny; frequent changes in compliance personnel or legal counsel.

Phase 5: Ongoing Monitoring

11. Capability Evolution Tracking

Genuine AI systems evolve and improve. Monitor whether claimed capabilities advance over time:

• Are new features and improvements announced regularly?
• Do performance metrics show realistic improvement curves (not linear perfection)?
• Does the company acknowledge limitations and ongoing development challenges?

Red Flag: Static capabilities despite claims of “learning” systems; perfect performance metrics that never decline; inability to describe specific improvements or development roadmap.

12. Whistleblower and Employee Feedback

Monitor employee sentiment and whistleblower activity:

• Glassdoor and Blind reviews mentioning technology capabilities
• LinkedIn posts from former technical employees
• SEC whistleblower program awards related to the company

Red Flag: Pattern of technical employee departures; Glassdoor reviews suggesting disconnect between marketing claims and internal reality; anonymous reports questioning technology capabilities.

The Future of AI Washing Enforcement

The regulatory response to AI washing will likely intensify through several predictable channels:

Technical Forensics Capabilities

The SEC’s CETU and similar units worldwide are developing specialized technical capabilities to investigate AI claims. This includes digital forensics for analyzing AI architecture, training data verification, and algorithmic performance testing. As these capabilities mature, the gap between claimed and actual AI capabilities will become easier to detect forensically.

Standardized AI Disclosure Frameworks

Expect development of industry-standard AI disclosure requirements, potentially modeled on existing cybersecurity disclosure frameworks. These may include:

• Mandatory AI capability attestations by technical officers
• Standardized definitions of terms like “AI-powered,” “machine learning,” and “automated”
• Required disclosure of human intervention percentages
• Third-party AI system audits for public companies

Private Litigation Expansion

As AI washing enforcement establishes legal precedents, private securities litigation will likely expand. The elements of securities fraud—material misrepresentation, scienter, reliance, and damages—apply clearly to AI washing cases. Class action attorneys are already developing expertise in AI-related securities claims.

International Coordination

The cross-border nature of AI development and investment will drive international regulatory coordination. The Nate Inc. case, with its Spanish-resident defendant and US-based enforcement, illustrates the jurisdictional complexities that require international cooperation. Expect development of mutual enforcement assistance treaties specifically addressing AI fraud.

Conclusion: Vigilance in the AI Investment Era

AI washing represents more than marketing misconduct—it constitutes a fundamental threat to capital allocation efficiency and market integrity. When investment flows to companies with fabricated AI capabilities, legitimate innovators are starved of capital, investors are defrauded, and market trust erodes. The regulatory crackdown now underway signals recognition that AI washing cannot be dismissed as harmless hype.

For investors, the imperative is clear: treat AI claims with the same skepticism applied to any material investment representation. The checklist provided offers systematic protection, but ultimately, investor education and due diligence discipline remain the primary defenses. The technology is complex, but the investment principle is simple—verify before trusting, and demand evidence rather than accepting assertions.

For the financial industry, the enforcement trajectory is equally clear. The SEC, DOJ, and international regulators have established that AI washing triggers serious legal consequences. The creation of specialized units, the imposition of substantial penalties, and the pursuit of individual liability demonstrate that AI claims are now fully within regulatory enforcement scope.

The parallel with greenwashing offers both caution and hope. A decade of greenwashing enforcement has not eliminated environmental misrepresentation, but it has raised standards, increased verification, and created legal frameworks for accountability. AI washing enforcement is on a similar trajectory, and the companies that survive will be those that align marketing claims with technical reality.

The AI revolution is real, transformative, and investable. But separating genuine AI innovation from washing requires the disciplined application of skepticism, technical literacy, and regulatory awareness. In this environment, the educated investor possesses significant advantage over those swept up in AI enthusiasm without critical evaluation.

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References

1. Corporate Compliance Insights. (2026, February 24). “The Rising Tide of AI-Washing Cases in Securities Fraud Litigation.” Corporate Compliance Insights. https://www.corporatecomplianceinsights.com/rising-tide-ai-washing-cases-securities-litigation/
2. Norton Rose Fulbright. (2024). “SEC heightens enforcement for AI related disclosures.” Norton Rose Fulbright Knowledge Publications. https://www.nortonrosefulbright.com/en/knowledge/publications/9ab5047f/sec-heightens-enforcement-for-ai-related-disclosures
3. Holland & Knight. (2025, December 23). “2025 Cybersecurity and AI Year in Review.” Holland & Knight Insights. https://www.hklaw.com/en/insights/publications/2025/12/2025-cybersecurity-and-ai-year-in-review
4. StoneTurn. (2025, October 30). “Preparing for Continued SEC AI Washing Enforcement.” StoneTurn Insights. https://stoneturn.com/insight/next-generation-compliance-preparing-for-continued-sec-ai-washing-enforcement/
5. Winston & Strawn. (2024). “SEC Targets ‘AI Washing’ by Companies, Investment Advisers and Broker-Dealers.” Winston & Strawn Capital Markets and Securities Law Watch. https://www.winston.com/en/blogs-and-podcasts/capital-markets-and-securities-law-watch/sec-targets-ai-washing-by-companies-investment-advisers-and-broker-dealers

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