Global Product Certification AI Systems and Compliance Monopolies

Introduction

Product certification AI systems are digital systems that use artificial intelligence, automated testing, machine learning, risk scoring, conformity assessment, document analysis, or continuous monitoring to determine whether products comply with legal, technical, safety, cybersecurity, environmental, or industry standards.

Examples include AI systems used to certify:

• medical devices and health technologies;
• autonomous vehicles and ADAS systems;
• industrial robots and machinery;
• AI-enabled consumer products;
• cybersecurity products;
• aviation and aerospace components;
• batteries and electric vehicles;
• telecommunications equipment;
• software and cloud-connected devices; and
• environmental and sustainability claims.

The competition-law concern arises when certification becomes dependent on a small number of AI-enabled certification platforms, databases, testing infrastructures, or conformity-assessment providers. A certification platform may then become a gatekeeper to market access.

The central question is:

Can a certification provider use control over AI-based conformity assessment, testing data, standards interfaces, or regulatory approval infrastructure to exclude competing products, certifiers, laboratories, or technology providers?

This creates a potential intersection between competition law, product regulation, AI governance, standards law, accreditation, essential facilities, interoperability, data access and administrative law.

1. Meaning of Product Certification AI Systems

A conventional certification process may involve:

1. product testing;
2. documentation;
3. technical assessment;
4. conformity determination;
5. certification;
6. surveillance and renewal.

An AI-based certification system can automate or augment these functions.

Typical architecture

Manufacturer → AI compliance platform → testing/data analysis → risk classification → certification decision → regulator/market access

The AI system may process:

• technical specifications;
• sensor data;
• software source-code characteristics;
• cybersecurity information;
• safety records;
• historical failures;
• laboratory test results;
• supply-chain information;
• environmental data;
• regulatory databases; and
• previous certification decisions.

The resulting system can become extremely difficult for competitors to replicate because it may possess enormous historical datasets and regulatory expertise.

2. Why Certification Can Create Monopoly Power

Certification is different from an ordinary commercial service because it can determine whether a product can legally enter a market.

If one organisation controls a certification infrastructure that regulators, purchasers, insurers and distributors all recognise, certification can become a bottleneck.

Potential competitive structure

Standards → Accreditation → Testing → AI assessment → Certification → Market access

Control at any of these stages may create market power.

For example:

If manufacturers must obtain certification from Platform A, and Platform A controls the industry's largest compliance dataset, competitors may find it impossible to establish an equivalent AI certification system.

This can produce data-driven certification monopolies.

3. Relevant Competition-Law Markets

Several relevant markets may exist simultaneously.

A. Certification services

The market for independent conformity-assessment or certification services.

B. Testing services

Laboratory testing may constitute a separate market where specialist facilities are required.

C. AI compliance software

Manufacturers may purchase automated regulatory-compliance software separately from certification.

D. Certification data

Historical testing and conformity datasets may themselves become an important competitive input.

E. Regulatory technology infrastructure

An AI platform could become infrastructure connecting manufacturers, laboratories and regulators.

F. Accreditation

Accreditation authorities may have a legally protected or highly concentrated position.

4. Network Effects

AI certification platforms can benefit from powerful network effects.

More certified products generate:

more data → better AI models → faster certification → more manufacturers → more data

This creates a feedback loop.

A new entrant may therefore face a structural disadvantage even if it possesses technically superior AI.

Example

Suppose Platform A has:

• 90% of historical test records;
• thousands of previous certification decisions;
• direct API integration with regulators;
• automated compliance templates; and
• recognition by major purchasers.

Platform B may technically offer a superior algorithm but lack the historical dataset and institutional integration required to compete.

The resulting monopoly is therefore not necessarily based on superior technology alone.

5. Data as a Certification Bottleneck

Historical certification data can have substantial competitive significance.

Such data may reveal:

• failure probabilities;
• acceptable safety margins;
• regulator interpretations;
• test tolerances;
• product-performance patterns;
• previous conformity decisions; and
• correlations between technical specifications and certification outcomes.

If an incumbent refuses reasonable access to such data, competition may be impaired.

The relevant competition-law theory can resemble essential-facility, refusal-to-deal, or data-access cases.

However, possession of valuable data does not automatically create a legal obligation to share it.

The critical questions include:

1. Is the data indispensable?
2. Can competitors realistically reproduce it?
3. Is access technically and economically feasible?
4. Is there a legitimate justification for refusal?
5. Would access eliminate incentives to innovate?
6. Is the incumbent simultaneously competing downstream?

6. Certification and Essential-Facility Doctrine

An AI certification infrastructure may become analogous to an essential facility when:

• certification is indispensable for market entry;
• only one or very few providers are recognised;
• duplication is practically impossible;
• refusal excludes competitors; and
• there is no legitimate justification.

The doctrine must nevertheless be applied cautiously.

Certification providers need independence, cybersecurity and protection of confidential product information. Mandatory data sharing could itself create risks.

Therefore, a competition remedy might involve:

• non-discriminatory access;
• interoperable APIs;
• transparent eligibility criteria;
• independent auditing;
• licensing of necessary datasets;
• separation between certification and commercial activities; or
• regulatory supervision.

7. Vertical Foreclosure

A particularly serious problem occurs where a certification company also sells competing products.

Consider:

Certification Platform A → certifies AI products

and

Platform A → sells its own AI products

The platform could theoretically:

• certify its own products faster;
• impose additional tests on rivals;
• delay competing certification;
• require proprietary APIs;
• deny access to certification datasets;
• manipulate risk classifications; or
• impose discriminatory compliance fees.

This resembles classic vertical foreclosure.

The competitive harm is greater where certification is mandatory for market access.

8. Self-Preferencing

AI certification systems may create novel self-preferencing problems.

An algorithm could assign favourable treatment to products that use:

• the incumbent's technology;
• its cloud infrastructure;
• its cybersecurity tools;
• its software libraries; or
• its preferred suppliers.

For example:

A certification AI might automatically classify products using its owner's proprietary architecture as "lower compliance risk" while sending rival architectures to additional manual testing.

Even if the algorithm does not explicitly discriminate, biased training data or model architecture could generate equivalent effects.

9. Algorithmic Discrimination

Competition authorities increasingly need to distinguish between:

legitimate risk-based certification

and

strategically discriminatory certification.

A certification algorithm could theoretically use:

• hidden variables;
• proprietary scoring;
• non-public thresholds;
• unexplained model weights;
• dynamic risk classifications.

The resulting decision may be difficult for manufacturers to challenge.

This creates an algorithmic opacity problem.

Competition law may therefore interact with requirements concerning:

• explainability;
• procedural fairness;
• auditability;
• human oversight; and
• contestability.

10. Certification Standards and Standards-Essential Infrastructure

Certification often depends upon technical standards.

If a dominant platform controls access to standards-related information, it may acquire substantial market power.

Potential concerns include:

• discriminatory licensing;
• excessive certification fees;
• exclusion of alternative standards;
• discriminatory interoperability requirements;
• refusal to recognise competing testing methodologies; and
• strategic modification of certification criteria.

Where intellectual property rights are involved, competition law may also intersect with FRAND principles.

11. AI Certification and Interoperability

Interoperability is particularly important.

A dominant certification platform might require manufacturers to use its:

• proprietary data format;
• proprietary API;
• proprietary testing environment;
• proprietary compliance ontology; or
• proprietary model-validation framework.

This can generate switching costs.

A manufacturer that has accumulated years of compliance records in Platform A may find migration to Platform B prohibitively expensive.

Thus:

Data portability can become a competition remedy in AI certification markets.

12. Tying and Bundling

A dominant certification provider could bundle certification with another service.

For example:

"You can obtain certification only if you purchase our compliance-management software."

Or:

"Certification is available only through our cloud infrastructure."

Such practices could raise tying and bundling concerns.

The competitive analysis would examine:

1. dominance in certification;
2. separability of certification and software;
3. coercion;
4. foreclosure;
5. efficiencies; and
6. consumer or regulatory benefits.

13. Certification Fees and Exploitative Conduct

A monopoly certification provider might impose:

• excessive certification fees;
• recurring AI-monitoring charges;
• expensive model-revalidation fees;
• high API-access charges;
• discriminatory renewal costs.

However, high prices alone do not necessarily establish an abuse of dominance.

Authorities would normally examine whether prices are excessive under the applicable jurisdiction's legal test and whether competition is being substantially harmed.

14. Predatory Certification

An incumbent could theoretically use below-cost certification pricing to eliminate competing certifiers.

For example:

Phase 1: certification offered below cost.

Phase 2: competing laboratories exit.

Phase 3: incumbent raises prices.

This resembles traditional predatory pricing but requires careful cost and recoupment analysis.

15. Merger Control

Concentration may also occur through acquisitions.

Potential transactions include:

• AI compliance software + certification body;
• certification body + testing laboratory;
• certification platform + standards database;
• AI model provider + accreditation organisation;
• cloud provider + certification infrastructure.

The transaction may raise concerns even when conventional revenue measures underestimate its importance.

Authorities may examine:

• data concentration;
• future competition;
• innovation competition;
• access to certification infrastructure;
• vertical foreclosure;
• interoperability;
• regulatory gatekeeping; and
• ecosystem effects.

16. Theories of Harm

The principal competition theories can be summarised as follows:

17. Important Case Laws

The following cases are especially useful because they establish principles concerning dominance, essential facilities, interoperability, standards, discriminatory access, tying, data-related exclusion and certification-type gatekeeping.

1. United States v. Terminal Railroad Association, 224 U.S. 383 (1912)

The Supreme Court dealt with control over essential railroad terminal facilities.

Principle

A group controlling indispensable infrastructure could not use that control to exclude competitors from access to the transportation network.

Relevance to AI certification

A certification infrastructure that is genuinely indispensable to market participation may generate analogous concerns.

If one entity controls the only practically available AI certification gateway, competition authorities may examine whether exclusionary access conditions unlawfully restrict competition.

18. United States v. Associated Press, 326 U.S. 1 (1945)

The case concerned exclusionary membership arrangements in an important news-distribution network.

Principle

A dominant network cannot necessarily use membership or access rules to exclude competitors where access to the network is competitively significant.

AI certification relevance

A certification platform could potentially become a network connecting:

manufacturers + laboratories + regulators + purchasers.

Exclusionary membership criteria could therefore have competitive effects beyond the immediate certification service.

19. Aspen Skiing Co. v. Aspen Highlands Skiing Corp., 472 U.S. 585 (1985)

The U.S. Supreme Court examined a dominant firm's termination of a cooperative arrangement with a smaller competitor.

Principle

A refusal to continue a profitable course of dealing can, under exceptional circumstances, constitute unlawful monopolization.

AI certification relevance

Suppose a dominant certification platform previously permitted rival certification software to access its infrastructure but suddenly terminates access specifically to eliminate the rival.

That history could become important evidence of exclusionary intent and competitive harm.

20. Verizon Communications Inc. v. Law Offices of Curtis V. Trinko, 540 U.S. 398 (2004)

Principle

The Supreme Court strongly cautioned against converting competition law into a general duty to deal.

A monopolist ordinarily does not have an unlimited obligation to assist competitors.

AI certification relevance

This is extremely important.

The fact that an incumbent controls valuable certification data does not automatically mean it must provide that data to competitors.

A competition authority would need to identify circumstances making the refusal legally problematic.

This protects legitimate incentives to innovate and invest in certification infrastructure.

21. Microsoft Corp. v. United States, 253 F.3d 34 (D.C. Cir. 2001)

The Microsoft litigation concerned exclusionary conduct involving operating systems, APIs and competing technologies.

Principle

Control over an important technological platform can be used to disadvantage competing products.

AI certification relevance

The case is highly relevant to:

• API access;
• interoperability;
• platform integration;
• technical standards;
• exclusion of competing technologies; and
• leveraging platform dominance.

An AI certification platform that controls APIs connecting manufacturers and regulators could potentially exercise comparable leverage.

22. European Commission — Microsoft (2004)

The European Commission found abuse concerning Microsoft's refusal to provide interoperability information and its tying of products.

Principle

Dominant technological platforms may, in appropriate circumstances, face competition-law obligations concerning interoperability and tying.

AI certification relevance

This is particularly relevant where certification software becomes a technological platform rather than merely a traditional certification service.

Potential concerns include:

• refusal to provide interoperability information;
• proprietary certification interfaces;
• tying certification to compliance software;
• discriminatory API access; and
• exclusion of competing compliance platforms.

23. Bronner v. Mediaprint, C-7/97

The Court of Justice considered whether access to an existing distribution network should be compelled under EU competition law.

Principle

The EU essential-facilities doctrine requires a demanding showing of indispensability and related conditions.

AI certification relevance

This case provides an important limiting principle.

Not every valuable AI certification database or platform is an essential facility.

The manufacturer would generally need to demonstrate that:

1. access is indispensable;
2. duplication is impossible or exceptionally difficult;
3. refusal is capable of eliminating effective competition; and
4. there is no objective justification.

24. IMS Health GmbH & Co. OHG v NDC Health, C-418/01

The case concerned access to a data structure protected by intellectual property rights.

Principle

Under exceptional circumstances, refusal to license intellectual property can constitute abuse of dominance where access is indispensable and refusal eliminates effective competition.

AI certification relevance

This is particularly significant for proprietary certification datasets.

An incumbent might argue:

"Our AI certification database is proprietary intellectual property."

The competition-law response is not automatically "share it."

Instead, the exceptional-circumstances framework becomes relevant.

25. Slovak Telekom v European Commission, C-165/19 P

The case concerned access and exclusionary conduct involving telecommunications infrastructure.

Principle

EU competition law distinguishes between ordinary refusal-to-deal situations and situations involving access obligations arising from a regulatory framework.

AI certification relevance

This distinction matters enormously for regulated certification markets.

Where regulation already imposes access or interoperability obligations, competition-law analysis may differ from a purely private commercial refusal.

26. Google Shopping, Google Search (Shopping), Case C-48/22 P

The case concerned Google's preferential treatment of its own comparison-shopping service within its dominant search infrastructure.

Principle

A dominant platform's design choices can constitute abusive self-preferencing where they disadvantage competing services.

AI certification relevance

An AI certification platform could similarly favour:

• its own certified products;
• its own compliance software;
• its own testing laboratories; or
• affiliated AI models.

The key issue would be whether the platform uses dominance in certification infrastructure to distort downstream competition.

27. Qualcomm Inc. v European Commission, Case T-235/18

The General Court considered exclusionary conduct involving payments and incentives in the semiconductor sector.

Relevance

The case illustrates the importance of examining how financial arrangements between a dominant technology firm and commercial partners can foreclose rivals.

For AI certification, analogous issues could arise if a dominant platform offers manufacturers:

• rebates;
• certification discounts;
• preferential processing;
• bundled testing;
• exclusive contracts.

28. Intel Corp. v European Commission, Case C-413/14 P

The case concerned rebates offered by a dominant undertaking.

Principle

The Court emphasised the need for careful analysis of whether rebates are capable of foreclosing an equally efficient competitor.

AI certification relevance

A dominant certification provider could offer:

"80% certification discounts if manufacturers use our compliance platform exclusively."

Such arrangements may require an effects-based foreclosure analysis.

29. Overall Legal Framework

The competition analysis differs by jurisdiction.

United States

Primary concepts include:

• Sherman Act §1;
• Sherman Act §2;
• Clayton Act;
• monopolization;
• attempted monopolization;
• tying;
• exclusive dealing;
• essential-facility principles; and
• merger control.

European Union

Relevant provisions include:

• Article 101 TFEU;
• Article 102 TFEU;
• EU Merger Regulation;
• Digital Markets Act where the undertaking falls within its scope;
• rules concerning standards and interoperability.

United Kingdom

Relevant provisions include:

• Competition Act 1998, Chapter II;
• Chapter I;
• Enterprise Act merger control;
• Digital Markets, Competition and Consumers Act 2024 where applicable;
• sectoral regulation and accreditation frameworks.

India

Relevant concepts include:

• Competition Act 2002;
• Section 3;
• Section 4;
• combinations under Sections 5–6;
• abuse of dominant position;
• denial of market access;
• discriminatory conditions;
• leveraging; and
• refusal to deal.

30. Regulatory Recognition Can Magnify Market Power

The most important distinctive feature of certification markets is regulatory recognition.

Imagine:

Regulator recognises Platform A

↓

Manufacturers must use recognised certification

↓

Distributors require certification

↓

Insurers rely on certification

↓

Customers prefer certified products

↓

Platform A becomes unavoidable

This can create a powerful regulatory network effect.

The monopoly therefore may not originate entirely from superior technology. It may arise from the interaction between:

law + accreditation + data + AI + network effects.

31. Accreditation and Competition

Accreditation systems themselves can become concentrated.

If only a small number of accreditation bodies can recognise AI certification providers, barriers to entry may increase.

Competition authorities should therefore distinguish between:

• necessary regulatory safeguards; and
• unnecessary exclusionary accreditation requirements.

A technically justified accreditation requirement is not anti-competitive merely because it raises entry costs.

The question is whether the requirement is proportionate and objectively justified.

32. Confidentiality Creates a Special Problem

Certification requires access to sensitive information.

Manufacturers may have to disclose:

• source code;
• technical architecture;
• security vulnerabilities;
• manufacturing processes;
• proprietary algorithms.

Consequently, open-data remedies cannot simply require complete disclosure.

Better remedies may include:

• controlled-access databases;
• anonymised datasets;
• secure APIs;
• independent auditors;
• data clean rooms;
• standardised metadata;
• limited-purpose licensing.

This balances competition with confidentiality.

33. AI Explainability and Competition

An opaque certification AI can create a serious competitive problem.

Suppose:

Product A receives certification in 48 hours.

while

Product B is repeatedly subjected to additional testing.

If Platform B cannot explain why the algorithm treated the products differently, the manufacturer may have difficulty challenging the decision.

Competition authorities should therefore consider whether the system permits:

• meaningful explanation;
• audit trails;
• human review;
• appeals;
• independent validation;
• consistent application of standards.

34. Dynamic Certification Monopolies

AI certification systems may produce dynamic rather than static dominance.

Traditional certification:

test → certify → renew.

AI certification:

continuously monitor → update risk score → change compliance requirements → automatically suspend or renew certification.

The platform may therefore exercise continuing influence over a manufacturer's market access.

This produces a new form of market power:

"Continuous compliance gatekeeping."

The certification provider does not merely decide whether the product enters the market; it can potentially influence whether the product remains in the market.

35. Competition Risks From Continuous Monitoring

Potential abuses include:

• discriminatory monitoring;
• excessive data requirements;
• automated suspension;
• discriminatory model updates;
• preferential treatment;
• unpredictable compliance thresholds;
• excessive renewal fees;
• exclusion from certification ecosystems.

Competition law may therefore need to consider algorithmic procedural fairness alongside traditional economic effects.

36. Remedies

Possible competition remedies include:

Structural remedies

• separation of certification and competing commercial activities;
• divestiture of testing infrastructure;
• separation of certification databases.

Behavioural remedies

• non-discriminatory certification;
• transparent criteria;
• reasonable certification fees;
• prohibition of self-preferencing;
• prohibition of tying.

Data remedies

• data portability;
• controlled access;
• interoperability;
• standardised data formats;
• API access.

Governance remedies

• independent algorithm audits;
• human review;
• appeal procedures;
• audit logs;
• regulator access.

37. Regulatory Sandbox Approach

Rather than immediately treating every certification platform as a monopoly, regulators can create controlled environments.

A regulatory sandbox could test:

• competing certification algorithms;
• interoperability;
• auditability;
• data portability;
• cybersecurity;
• false-positive rates;
• discriminatory outcomes.

This permits competition without compromising product safety.

38. Key Competition-Law Questions

When investigating an AI certification monopoly, authorities should ask:

Market power

1. What is the relevant certification market?
2. Is certification legally mandatory?
3. Is the provider accredited?
4. How easily can manufacturers switch?

Data

1. Does the incumbent possess unique historical data?
2. Can competitors reproduce the dataset?
3. Is access technically feasible?

Conduct

1. Is certification discriminatory?
2. Is there self-preferencing?
3. Are competitors denied API access?
4. Is certification tied to other products?

Effects

1. Are rival certifiers being foreclosed?
2. Are innovation incentives reduced?
3. Are manufacturers locked into the platform?
4. Are prices or compliance costs increasing?

Justification

1. Is the restriction genuinely necessary for safety?
2. Is confidentiality a legitimate justification?
3. Is cybersecurity being used legitimately?
4. Is the restriction proportionate?

39. Six Core Case-Law Principles for Examination

40. Conclusion

Global Product Certification AI Systems and Compliance Monopolies represent an emerging form of competition-law problem in which regulatory authority, technological infrastructure and commercial market power can converge.

The central danger is not simply that one company provides certification. The deeper concern arises when a firm controls several interconnected bottlenecks:

certification + accreditation + AI assessment + proprietary data + testing infrastructure + regulatory interfaces.

At that point, the certification platform may become a market-access gatekeeper.

The principal competition-law risks are:

1. essential-facility foreclosure;
2. refusal to deal;
3. self-preferencing;
4. vertical leveraging;
5. tying and bundling;
6. exclusive dealing;
7. discriminatory certification;
8. data foreclosure;
9. interoperability restrictions;
10. exclusionary rebates;
11. predatory pricing; and
12. acquisition of emerging certification competitors.

The case law from Terminal Railroad, Aspen Skiing, Trinko, Microsoft, Bronner, IMS Health, Google Shopping, Intel and Qualcomm demonstrates that competition law already contains many of the analytical tools needed to address these problems. The novel element is that AI can transform certification from a periodic technical assessment into continuously operating digital infrastructure.