Navigating The Transparency Paradox in AI Regulation

Transparency was meant to build trust in artificial intelligence. Instead, it has opened a new fault line between regulators demanding disclosure and companies guarding their most valuable secrets. From Brussels to Denver, lawmakers are discovering that algorithmic sunlight can burn as easily as it disinfects.

The New Rules of Openness

The era of voluntary AI ethics has ended. In June 2024, the European Union formally adopted the AI Act, the first binding framework governing the development and deployment of high-risk systems. Articles 53 and 54 require developers to produce detailed technical documentation, disclose training data summaries, and provide regulators with model information sufficient for conformity assessment. Colorado’s SB 24-205, effective Feb. 1, 2026, mirrors this approach by obligating both developers and deployers of high-risk AI to maintain impact assessments and risk-mitigation records. The intent is accountability; the consequence is exposure.

Under these laws, disclosure is no longer optional. Firms must explain how their models work, what data they use, and how bias is managed. Yet each layer of transparency risks revealing proprietary methods or datasets that have long been protected under trade secret statutes. The collision between public accountability and private innovation is now one of the central legal questions of the AI age.

From Voluntary Ethics to Mandatory Disclosure

Until recently, transparency was a voluntary virtue. The NIST AI Risk Management Framework, released in Jan. 2023, and ISO/IEC 42001, published in Dec. 2023, encouraged explainability and documentation as best practices, not statutory duties. That changed as AI systems began influencing credit decisions, hiring, and legal research. Lawmakers, citing algorithmic bias and opaque outcomes, reclassified transparency from ethical aspiration to legal requirement. The FTC now warns that undisclosed or misleading AI claims may constitute unfair or deceptive practices under the FTC Act.

Colorado’s statute and the EU Act both demand algorithmic documentation that regulators or affected individuals can inspect. The result is a procedural transparency regime: risk assessments must be recorded, updated, and on request shared with authorities. Companies must also provide summaries of model logic in plain language. These disclosures go well beyond traditional product-safety filings, pushing into the intellectual core of AI design.

When Disclosure Undermines Protection

Trade secret law depends on secrecy. In the United States, the Defend Trade Secrets Act of 2016 protects information that derives economic value from not being generally known. Once a process or dataset becomes publicly ascertainable, that protection evaporates. Article 2(1) of the EU Trade Secrets Directive 2016/943 sets a similar standard: information must be kept secret and subject to reasonable measures of confidentiality. The paradox is clear: complying with an AI transparency law may require revealing exactly what a firm must keep secret to preserve its legal protection.

Legal scholars call this the transparency-trade-secret paradox. Regulators need insight to enforce fairness, while innovators need opacity to survive competition. If a company’s bias-testing data, model weights, or feature-selection methods are disclosed to a regulator and later leaked, its market advantage disappears. Yet withholding them can trigger penalties or reputational damage for non-compliance. The law offers few safe harbors between these extremes.

Balancing Acts in Colorado and Brussels

Colorado’s SB 24-205 attempts a compromise. It allows firms to summarize, rather than publish, model details and to designate disclosures as confidential when provided to the state attorney general. The EU Act also acknowledges trade-secret protection, stating in Article 70 that regulators must maintain confidentiality of proprietary information obtained through conformity assessments. In practice, however, both regimes leave open questions about secondary disclosure: what happens if that information surfaces in litigation, public-records requests, or cross-border investigations.

European regulators will supervise compliance through national market authorities empowered to audit high-risk AI systems. U.S. enforcement will likely fall to state attorneys general and the FTC. Each side of the Atlantic faces the same dilemma: transparency strong enough to expose misconduct must be strong enough to reveal value. The result is a governance tightrope that law firms must learn to walk on behalf of clients deploying AI tools.

Private Oversight and Contractual Shields

Because public disclosure threatens trade secrets, many firms now rely on private verification models. Third-party auditors sign nondisclosure agreements, conduct bias and security reviews, and issue attestation reports. This tiered transparency approach, open to regulators but closed to competitors, mirrors financial audit protocols. Vendor contracts increasingly contain clauses defining what constitutes confidential AI information and how it may be shared with insurers or regulators.

Insurance markets are accelerating this shift. Cyber-liability underwriters in both the U.S. and the EU now require documentation of AI risk controls, human-review procedures, and provenance records. Verified adherence to frameworks such as NIST RMF or ISO/IEC 42001 can reduce premiums. Transparency, in other words, has become an economic metric as much as a regulatory one: rewarded when structured, punished when improvised.

When Transparency Becomes Discovery

Disclosure obligations also collide with civil procedure. In U.S. discovery, opposing counsel may seek algorithmic documentation submitted under regulatory mandate. If that data enters the record without protective orders, trade-secret status can be lost. European defendants face similar exposure under the Trade Secrets Directive, which offers confidentiality measures but limited guarantees once material reaches court. Lawyers advising AI clients must draft disclosure protocols that anticipate litigation, not just regulation.

The FTC and European Data Protection Board have both signaled that transparency cannot override privacy law. In mixed datasets containing personal information, disclosing data provenance can itself trigger liability under the General Data Protection Regulation (GDPR). The challenge is layering compliance: explain enough to satisfy fairness mandates without violating privacy or forfeiting secrets. Few firms have mastered the geometry of that equation.

Emerging Doctrines of Responsible Secrecy

Both regions are converging on a pragmatic doctrine: responsible secrecy. Under this model, firms disclose governance processes, not algorithms; validation outcomes, not raw training data. The goal is verifiable accountability without competitive self-harm. Transparency thus becomes a managed exposure: structured, deliberate, and legally insulated.

Law firms advising AI clients should integrate this principle into compliance design. Maintain an internal AI register cataloging systems, data sources, and risk assessments. Use NDAs and trade-secret legends in every submission. When possible, deliver summaries rather than source materials. These tactics transform transparency from disclosure into demonstration, showing regulators that control exists without giving away the blueprint.

The Next Phase of Transatlantic Convergence

The United States and European Union are moving toward functional alignment. The European Commission’s AI Office coordinates oversight across member states, while U.S. agencies, from the FTC to the Department of Commerce, are exploring harmonized reporting standards. Cross-border firms may soon face mutual-recognition frameworks where a single audit satisfies both jurisdictions. That prospect raises a new question: will trade-secret protections travel with the audit data, or be lost in translation?

At the same time, policymakers are considering confidential supervisory disclosure models borrowed from banking law, allowing companies to share sensitive AI information with regulators under strict secrecy. Such systems could reconcile transparency with protection, provided global regulators agree on security and access protocols. The alternative is regulatory fragmentation: different disclosure duties, inconsistent confidentiality, and rising compliance costs for every multinational firm using AI.

While the EU and U.S. emphasize transparency-based regulation, other jurisdictions take divergent approaches. China’s regulatory framework, including measures implemented by the Cyberspace Administration of China, focuses more heavily on state oversight and content control than on disclosure to affected individuals. These differing regulatory philosophies create challenges for multinational technology companies attempting to develop unified compliance strategies across markets.

Enforcement Mechanisms and Penalties

The stakes for non-compliance are substantial. Under the EU AI Act, violations can result in administrative fines. For prohibited AI practices, penalties can reach the higher of 35 million euros or seven percent of total worldwide annual turnover. For violations of obligations by AI system providers, fines can reach the higher of 15 million euros or three percent of annual turnover. These penalty structures underscore the seriousness with which European regulators view AI compliance.

Colorado’s enforcement model operates differently. Under SB 24-205, violations constitute deceptive trade practices under the Colorado Consumer Protection Act, with enforcement authority vested exclusively in the state attorney general. While the law does not specify monetary penalties as precisely as the EU framework, it provides for injunctive relief and the attorney general’s typical enforcement powers under consumer protection law.

What It Means for Law Firms

For legal practitioners, the transparency-trade-secret conflict is no longer theoretical. Law firms using AI research or drafting tools must determine how much model information vendors can lawfully withhold. Contracts should specify ownership of derived data, audit rights, and indemnification for regulatory disclosures. The ABA Formal Opinion 512, issued in July 2024, requires lawyers to supervise AI outputs and preserve documentation adequate for client defense. Doing so without breaching trade-secret barriers will demand meticulous policy drafting.

Ultimately, the goal is balance. Transparency should verify integrity, not invite theft. Secrecy should protect innovation, not conceal harm. Between those poles lies the emerging architecture of AI governance: a system where accountability is proven, not presumed, and where trust survives only if it can keep a secret.

Sources

• American Bar Association Formal Opinion 512: Generative Artificial Intelligence Tools (2024)
• Chicago Unbound: “Trade Secrecy’s Information Paradox” (University of Chicago Law School 2024)
• Colorado SB 24-205: Artificial Intelligence Act (2024)
• Defend Trade Secrets Act of 2016 (18 U.S.C. § 1836 et seq.)
• European AI Office
• EU: General Data Protection (Regulation 2016/679)
• EU Trade Secrets Directive (2016/943)
• EU: Artificial Intelligence Act (Regulation 2024/1689)
• Federal Trade Commission Act (15 U.S.C. §45)
• ISO/IEC 42001:2023 Artificial Intelligence Management System
• NIST AI Risk Management Framework (2023)

This article was prepared for educational and informational purposes only. It does not constitute legal advice and should not be relied upon as such. All cases, statutes, and sources cited are publicly available through official publications and reputable outlets. Readers should consult professional counsel for specific legal or compliance questions related to AI use.

See also: From Transparency to Proof: How Global Standards Are Redefining Legal AI Accountability