How Artificial Intelligence Strategies Create Legal Risk

The rapid integration of artificial intelligence into business operations has created unprecedented legal exposure for companies across all sectors. As organizations rush to deploy AI systems to gain competitive advantages, many are discovering that their AI strategies inadvertently trigger complex liability issues spanning intellectual property, employment discrimination, securities fraud, data privacy, and trade secret protection. For attorneys handling patent litigation, trade secret misappropriation, and software breach of contract matters, understanding these emerging risks has become essential to effectively representing clients in an increasingly AI-driven commercial landscape.

The Rise of “AI Washing” and Securities Liability

One of the most visible legal risks emerging from AI deployment concerns what regulators have termed “AI washing”—the practice of exaggerating or misrepresenting a company’s AI capabilities to investors and consumers. The Securities and Exchange Commission has made enforcement in this area a priority, with Chair Gary Gensler explicitly warning in February 2024 that misleading statements about AI models violate securities laws.

The SEC’s enforcement actions demonstrate the agency’s serious approach to these violations. In March 2024, the Commission settled charges against two investment advisers, Delphia and Global Predictions, for making false statements about AI-based capabilities they did not possess. These cases established that securities laws require full, fair, and truthful disclosure about AI use, with no tolerance for marketing hyperbole that crosses into material misrepresentation.

Private securities litigation has followed suit. According to the Stanford Law School Securities Class Action Clearinghouse, AI-related securities class actions surged in 2024, with 15 new filings that year alone. These cases typically allege that companies overstated AI capabilities in public statements, leading to inflated stock prices that later crashed when the truth emerged. For companies, the litigation risk extends beyond regulatory fines to include shareholder class actions seeking damages for stock price declines.

The Federal Trade Commission has also entered this enforcement space through Operation AI Comply, announced in September 2024. The FTC brought actions against five companies for allegedly using deceptive AI claims to harm consumers, making clear that consumer protection laws apply with full force to AI-related marketing.

A software expert witness examining AI washing claims must be prepared to analyze the technical capabilities of AI systems against marketing representations, identifying material discrepancies between promised functionality and actual performance. This often requires detailed examination of training data, model architecture, testing results, and deployment limitations.

Algorithmic Bias and Employment Discrimination

Employment decisions driven by AI screening tools have become a major source of litigation risk. The landmark case of Mobley v. Workday, Inc. illustrates how algorithmic bias claims can survive dismissal and expose both employers and AI vendors to liability. In July 2024, the U.S. District Court for the Northern District of California ruled that Workday, a provider of AI-driven applicant screening tools, could be held liable as an agent of employers under Title VII, the Americans with Disabilities Act, and the Age Discrimination in Employment Act.

The court’s reasoning has significant implications for the AI industry. Judge Rita Lin emphasized that Workday’s screening tools allegedly participate in hiring decisions by recommending some candidates while rejecting others, placing the company’s AI at “the heart of equal access to employment opportunities.” Critically, the court declined to “draw an artificial distinction between software decision-makers and human decision-makers,” noting that such a distinction would “potentially gut anti-discrimination laws in the modern era.”

In May 2025, the court certified the case as a nationwide collective action under the Age Discrimination in Employment Act, potentially covering hundreds of millions of job applicants. This certification signals growing judicial willingness to scrutinize AI employment tools for discriminatory impacts.

The Equal Employment Opportunity Commission has provided clear guidance that employers remain liable for discriminatory outcomes produced by algorithmic decision-making tools. The EEOC’s technical assistance document emphasizes that employers cannot delegate away responsibility for discrimination by outsourcing decisions to AI systems. Several states, including Colorado, have enacted legislation specifically addressing algorithmic bias, with Colorado’s AI Act requiring deployers of high-risk AI systems to use reasonable care to protect consumers from algorithmic discrimination.

An artificial intelligence expert witness in employment discrimination cases must be capable of examining training data for historical biases, analyzing how algorithms weight various factors, conducting disparate impact testing, and evaluating whether alternative selection procedures could achieve business objectives with less discriminatory effect.

Trade Secret Vulnerabilities in the AI Era

The intersection of AI tools and trade secret protection creates novel misappropriation risks that many organizations fail to anticipate. In West Technology Group LLC v. Sundstrom, filed in the U.S. District Court for the District of Connecticut, plaintiffs alleged that a former employee used Otter, an AI-powered meeting transcription service, to record confidential meetings without proper authorization. The complaint asserts that this unauthorized recording and transcription of trade secret information to a third-party AI platform constitutes misappropriation under the Defend Trade Secrets Act.

This case exemplifies a widespread problem: employees increasingly use AI productivity tools like ChatGPT, Otter, and similar services to streamline work tasks, often without considering that sharing confidential information with these third-party systems may constitute an unauthorized disclosure that destroys trade secret protection. Samsung experienced this issue firsthand when several software engineering employees used ChatGPT to check company source code, potentially exposing proprietary algorithms to OpenAI’s training data.

The legal theory supporting these claims is straightforward. Trade secret status requires that information remain confidential and that the owner take reasonable measures to maintain secrecy. When employees input trade secrets into AI systems operated by third parties with unknown security parameters and data retention policies, courts may find that the information no longer qualifies for protection because it has been shared beyond the boundaries of confidential relationships.

AI also poses reverse engineering risks. The Eleventh Circuit’s 2024 decision in Compulife Software, Inc. v. Newman demonstrates judicial willingness to find that automated data scraping can constitute improper means of trade secret acquisition, even when the underlying data points are individually public. The court held that using automated tools to compile millions of publicly available insurance quotes into a database could constitute trade secret misappropriation, recognizing that aggregated data may have protectable value even when individual elements do not.

For companies deploying AI, the practical implications are significant. Organizations must implement clear policies restricting the input of confidential information into external AI systems, provide employee training on these restrictions, and consider whether AI-generated insights themselves qualify as trade secrets requiring protection.

Data Privacy Compliance Failures

AI systems’ voracious appetite for training data has created substantial exposure under privacy regulations including the General Data Protection Regulation and the California Consumer Privacy Act. Litigation alleging privacy violations related to AI training has proliferated, with multiple federal cases filed in the Northern District of California challenging how companies collect and use personal data to train generative AI models.

In 2024, companies using AI technology faced lawsuits under various state and federal privacy theories. Cases brought under California’s Invasion of Privacy Act alleged that AI companies intercepted communications or collected personal data without consent in violation of state wiretapping laws. These cases raise fundamental questions about when and how companies can collect data for AI training purposes.

The California Privacy Protection Agency is developing comprehensive regulations governing automated decision-making technology, with draft rules expected to take effect in 2025. These regulations will likely require pre-use notices to consumers, meaningful opt-out rights, and detailed explanations of how AI systems affect individuals. Similar regulatory frameworks are emerging in multiple states, creating a patchwork of compliance obligations for companies deploying AI across state lines.

Under the GDPR, AI systems face particular scrutiny regarding data minimization, purpose limitation, and transparency requirements. Companies must ensure that AI training and deployment uses only necessary personal data, that data collected for one purpose is not repurposed for AI applications without proper legal basis, and that data subjects receive clear information about AI-driven processing. Violations carry severe penalties, with GDPR fines reaching €5.88 billion cumulatively by January 2025.

Intellectual Property Infringement in AI Training

Copyright litigation targeting AI developers has become one of the most contentious areas of AI-related legal risk. Major publishers, including The New York Times, have filed suits alleging that AI companies used massive amounts of copyrighted material to train large language models without authorization or compensation. These cases present novel questions about fair use, transformative use, and the economic impact of AI-generated content on copyright holders.

The New York Times v. OpenAI, filed in December 2023, alleges that OpenAI and Microsoft used millions of Times articles to train ChatGPT without permission, creating a system that can reproduce Times content or generate similar articles that compete with the newspaper’s journalism. The complaint argues that this use exceeds any fair use defense and causes economic harm by providing a substitute for the original copyrighted works.

Similar litigation has emerged in the music industry, with major record companies suing AI music generators for allegedly training on copyrighted sound recordings and lyrics. These cases test whether AI training constitutes copyright infringement at the input stage, and whether AI-generated outputs that closely resemble copyrighted works constitute infringement at the output stage.

For businesses deploying AI systems, these copyright disputes create uncertainty about whether third-party AI services were lawfully trained and whether using those services exposes companies to contributory infringement liability. Companies must conduct due diligence on AI vendors’ training practices and consider contractual protections requiring vendors to indemnify against intellectual property claims.

Breach of Contract and Licensing Violations

Software licensing terms increasingly include restrictions on using licensed software for AI training purposes. When companies scrape data from websites, use software tools to extract information for AI development, or deploy AI systems in ways that violate terms of service, they face potential breach of contract claims in addition to other liability theories.

The technical complexity of modern AI systems makes contract compliance challenging. Many AI applications integrate multiple third-party services, each with distinct licensing terms that may conflict or create unexpected restrictions. A software expert witness evaluating these disputes must trace data flows through complex systems, identify which contractual provisions apply to specific uses, and determine whether technical implementations comply with licensing restrictions.

Service providers have begun to include explicit AI-related provisions in their terms of service. Some prohibit using their platforms to collect training data, others restrict using their APIs to develop competing AI services, and many reserve rights to use customer data for their own AI development. Failing to understand and comply with these provisions creates breach of contract exposure that can result in service termination, damages claims, and loss of access to critical business systems.

Establishing Robust AI Governance

The legal risks outlined above share a common thread: they arise from deploying AI systems without adequate governance frameworks to identify and mitigate legal exposure. Companies that treat AI as purely a technical matter, delegating decisions entirely to engineering teams, consistently find themselves in litigation that could have been avoided through proper risk assessment and compliance planning.

Effective AI governance requires cross-functional collaboration among legal, compliance, technical, and business teams. Organizations must establish clear policies for AI development and deployment, implement bias testing and monitoring procedures, conduct regular audits of AI systems for compliance with applicable laws, maintain detailed documentation of AI decision-making processes, and train employees on acceptable uses of AI tools.

When disputes arise, having documented AI governance processes becomes critical to defending against claims. Courts increasingly expect companies to demonstrate that they implemented reasonable measures to prevent discrimination, protect privacy, respect intellectual property rights, and ensure accurate representations about AI capabilities. Companies that cannot produce evidence of such measures face significantly greater liability exposure.

The technical evaluation required in AI litigation demands expertise that goes beyond traditional software analysis. Artificial intelligence expert witness engagements typically require examining training data composition and quality, analyzing algorithmic design and implementation, testing for bias and discriminatory outcomes, evaluating model performance against claimed capabilities, and assessing whether AI systems comply with applicable technical standards and regulations.

Strategic Risk Management for AI Deployment

For counsel advising clients on AI initiatives, several practical steps can reduce legal exposure. First, companies should conduct comprehensive legal risk assessments before deploying AI systems, examining potential impacts under employment discrimination laws, privacy regulations, intellectual property protections, securities disclosure requirements, and contract law. Second, organizations should implement technical controls including bias testing protocols, data minimization practices, explainability features that support transparency requirements, and monitoring systems that detect compliance issues.

Third, companies must establish clear documentation practices that create records supporting legal defenses, including written AI policies and procedures, records of risk assessments and mitigation measures, documentation of bias testing and remediation, and evidence of employee training on AI governance. Fourth, contractual protections should address AI-related risks through vendor agreements requiring compliance with applicable laws, indemnification provisions covering AI-related claims, and audit rights allowing verification of vendors’ AI practices.

The complexity of these issues makes expert testimony essential in most AI litigation. Whether the dispute involves employment discrimination, trade secret misappropriation, patent infringement, breach of contract, or other claims, technical experts must translate AI systems’ operation into terms that judges and juries can understand while providing opinions on whether those systems comply with legal standards.

Conclusion

Artificial intelligence offers tremendous business benefits but creates equally substantial legal risks across multiple practice areas. Companies that deploy AI without adequate attention to legal compliance face exposure to securities fraud claims, employment discrimination lawsuits, trade secret misappropriation actions, privacy enforcement, intellectual property litigation, and breach of contract disputes. As AI technology continues to advance and become more deeply integrated into business operations, these legal risks will only intensify.

For attorneys representing clients in AI-related disputes or advising on AI deployment, developing expertise in how AI systems function and where they create legal exposure has become essential. The technical complexity of these matters typically requires collaboration with experts who can analyze AI systems, identify compliance failures, and provide testimony that bridges the gap between technology and law.

Sidespin Group offers comprehensive AI strategy services for businesses seeking to deploy artificial intelligence while managing legal risk, as well as software expert witness services for litigation matters involving AI systems, algorithmic bias, trade secret protection, patent disputes, and breach of contract claims. Their team combines deep technical expertise in artificial intelligence with practical understanding of how AI deployment creates legal exposure across multiple domains, providing the specialized knowledge that complex AI litigation demands.