HR & People
Video-Interview Emotion & Micro-Expression Scoring
Unacceptable Risk (Prohibited)UnverifiedDiscuss / dispute
Scoring of recorded or live candidate interviews by inferring emotional state, confidence or engagement from facial micro-expressions, voice and phrasing.
Consensus classification rationale: Art. 5(1)(f) prohibits placing on the market, putting into service or using AI systems to infer emotions of a natural person in the areas of workplace and education institutions, except where the system is intended to be put in place or into the market for medical or safety reasons. Candidate assessment is workplace use and neither exception applies, so this is a prohibition and not a high-risk classification: no control set, architecture or vendor makes it lawful in the Union. In the US the exposure is different in kind — Title VII disparate-impact risk enforced by the EEOC, and where the interview is recorded and analysed, the Illinois AI Video Interview Act notice, explanation, consent and 30-day destruction duties.
Decision attributes in force
Autonomyhuman-in-the-loop — Drives the human-oversight duties (Art. 14, Art. 26(2)) and Art. 50 disclosure.
Profiling of natural personsyes — Feeds the Art. 6(3) second-subparagraph override directly — profiling makes the derogation categorically unavailable.
Affected subjectsnatural-person — Instruments scoped to natural persons drop out of scope when only legal entities are assessed.
Deployer typeprivate-enterprise — Selects between the recorded alternate classification readings.
Role in the value chaindeployer — Splits provider duties, deployer duties and upstream GPAI duties.
Indicative decision support, not legal advice. Risk classification depends on your concrete deployment context and can change with scope drift — validate the result with qualified counsel.
Target market(s)European UnionUnited States (federal)change
Changes which instruments below count as in scope for this profile.
Target markets: European Union, United States (federal)
Regulatory footprint
9 instruments across 3 of 7 regulatory domains, plus 5 standards references- AI law1 instrument
- Data protection1 instrument
- Cyber & resiliencenone triggered
- Online safety & platformsnone triggered
- Product safetynone triggered
- Financial servicesnone triggered
- Sector & employment7 instruments
- Standards5 references
By jurisdiction
- EU3European UnionEU AI Act, EU Employment Equality Directives (2000/78 et al.), GDPR
- US-IL2thinUnited States — IllinoisIllinois AI Video Interview Act, Illinois Biometric Information Privacy Act (BIPA)
- US2United States (federal)ADA Title I & ADEA (US employment anti-discrimination), EEOC / Title VII Algorithmic Fairness (US)
- DE2thinGermany§ 26 BDSG — Beschäftigtendatenschutz (DE), AGG (German General Equal Treatment Act)
The AI Act is one dimension of this footprint, not the whole of it — every domain above carries its own obligations and deadlines. See the instruments in the graph →
Confidence in this chain of evidenceConfidence: Check-worthy
The chain holds, but at least one hop rests on a secondary source, an ageing verification or a practice-derived step. Check the flagged hops before you rely on them.
Computed weakest-link over 30 evaluated hops across 1 target market: a chain is only as strong as its weakest step, so the band follows the worst hop rather than an average that would hide it. Five factors per hop — source tier, verification age, status certainty, community hardening, derivation kind — all read from graph data, never from a hand-set score.
Why this band9 factors lowered the band — each links to the claim behind it
- Source tier: EU Employment Equality Directives (2000/78 et al.) carries no resolvable citation — the claim is uncited. open node →
- Source tier: § 26 BDSG — Beschäftigtendatenschutz (DE) carries no resolvable citation — the claim is uncited. open node →
- Source tier: AGG (German General Equal Treatment Act) carries no resolvable citation — the claim is uncited. open node →
- Source tier: JTC 21 Technical Package (prEN 18228/18229/18281–83) rests on a secondary source (tracker or summary), not on the primary text. open node → primary source →
- Source tier: IEEE CertifAIEd™ carries no resolvable citation — the claim is uncited. open node →
- Source tier: prEN 18229-1 (Trustworthiness Framework, part 1) rests on a secondary source (tracker or summary), not on the primary text. open node → primary source →
- Status certainty: JTC 21 Technical Package (prEN 18228/18229/18281–83) is "draft", not settled in-force law. open node → primary source →
- Status certainty: prEN 18229-1 (Trustworthiness Framework, part 1) is "enquiry", not settled in-force law. open node → primary source →
- Verification age: IEEE CertifAIEd™ has no recorded verification date. open node →
Compliance brief
This use case is prohibited under the EU AI Act (Unacceptable Risk (Prohibited)) — it may not be placed on the market or put into service.
What is owed
- Art. 5. Bans subliminal manipulation, exploitation of vulnerabilities, social scoring, untargeted facial-image scraping, workplace/education emotion recognition, biometric categorisation…
- Art. 5(1)(f). Prohibits placing on the market, putting into service or using AI systems to infer emotions of a natural person in the areas of workplace and education institutions.
- Art. 5(1)(d). Prohibits placing on the market, putting into service or using an AI system for making risk assessments of natural persons in order to assess or predict the risk of a natural pers…
- Art. 5(1)(c). Prohibits placing on the market, putting into service or using AI systems for the evaluation or classification of natural persons or groups over a period of time based on their so…
- Art. 5(1)(g). Prohibits biometric categorisation systems that categorise natural persons individually on the basis of their biometric data to deduce or infer race, political opinions, trade-uni…
Dates that bind
- 2024-08-01 — AI Act enters into force. Regulation (EU) 2024/1689 in force; countdown for all staged obligations starts.
- 2025-02-02 — Prohibitions + AI literacy. Art. 5 prohibited practices ban applies (manipulation, social scoring, untargeted face scraping, workplace emotion recognition); Art. 4 AI literacy duty.
Maximum exposure
- EU AI Act: Tiered: €35m / 7% (prohibited practices); €15m / 3% (Art. 9–15 high-risk obligations incl. data governance, documentation, logging); €7.5m / 1% (Art. 99(5) — incorrect, incomplete or misleading information to notified bodies or national competent authorities)
- EEOC / Title VII Algorithmic Fairness (US): EEOC charges, disparate-impact litigation, consent decrees.
- Illinois AI Video Interview Act: No fine schedule stated in the Act itself; enforcement route recorded as unsettled rather than asserted.
- Illinois Biometric Information Privacy Act (BIPA): Private right of action with liquidated damages per violation as set out in § 20 of the Act; amounts and the per-scan/per-person accrual question are litigated — recorded as such, not computed here.
- GDPR: Up to €20m or 4% of worldwide annual turnover
- EU Employment Equality Directives (2000/78 et al.): Art. 17 leaves penalties to the Member States, which must lay down rules on sanctions for infringements of the national transposing provisions that may comprise payment of compensation to the victim and must be effective, proportionate and dissuasive (mirrored in 2000/43 Art. 15 and 2006/54 Art. 25).
- ADA Title I & ADEA (US employment anti-discrimination): ADA Title I adopts the Title VII enforcement machinery — EEOC charge processing, EEOC or Attorney General suits and private civil actions under 42 U.S.C. §§ 2000e-4, 2000e-5, 2000e-6, 2000e-8 and 2000e-9 (42 U.S.C. § 12117(a)); the ADEA is enforced through the Fair Labor Standards Act remedies of 29 U.S.C. §§ 211(b), 216 and 217, with liquidated damages 'payable only in cases of willful violations', plus a private civil action for 'such legal or equitable relief as will effectuate the purposes of this chapter' (29 U.S.C. § 626(b), (c)(1)).
- § 26 BDSG: The BDSG's own fine provision (§ 43) reaches only breaches of § 30 (fines up to 50,000 EUR) and does not cover § 26; breaches of § 26 are enforced through the directly applicable GDPR — administrative fines under Art. 83 and compensation claims under Art. 82 (e.g. 200 EUR damages awarded in BAG 8 AZR 209/21).
- AGG (German General Equal Treatment Act): Enforcement is private-law only: employers owe damages and, for non-pecuniary harm, monetary compensation (§ 15, capped at three months' salary where the applicant would not have been hired even without discrimination), civil-law counterparties owe removal, injunction, damages and compensation (§ 21), discriminatory contract terms are void (§ 7 Abs. 2) and the burden of proof shifts under § 22; the Act provides no administrative fines or criminal penalties and claims must be asserted within two months (§ 15 Abs. 4, § 21 Abs. 5).
First five actions
- Confirm in writing whether this organisation builds/places the system on the market (provider) or only operates it (deployer), since the role is not yet established.
- Commission and confirm the Art. 5, Art. 5(1)(f), Art. 5(1)(d) obligations named above as active workstreams with an accountable owner.
- Design and document a human-oversight procedure appropriate to how this system is used.
- Produce the technical documentation and evidence artefacts already mapped to this use case (HITL Escalation Queue & Review UI, Adverse-Decision Reason Generator, Bitemporal Memory (GDPR×Art.12)) before they are requested.
- Put 2024-08-01 — AI Act enters into force — into the compliance calendar with an owner and lead time.
Terms used above: · · ·
Classification precedent
Consensus reading: Unacceptable Risk (Prohibited) open in the graph →
Art. 5(1)(f) prohibits placing on the market, putting into service or using AI systems to infer emotions of a natural person in the areas of workplace and education institutions, except where the system is intended to be put in place or into the market for medical or safety reasons. Candidate assessment is workplace use and neither exception applies, so this is a prohibition and not a high-risk classification: no control set, architecture or vendor makes it lawful in the Union. In the US the exposure is different in kind — Title VII disparate-impact risk enforced by the EEOC, and where the interview is recorded and analysed, the Illinois AI Video Interview Act notice, explanation, consent and 30-day destruction duties.
What the reading rests on — the provisions this classification actually pulls in:
- Art. 5 — Prohibited Practices
- Art. 5(1)(f) — Emotion inference at work and in education
- Art. 5(1)(d) — Predicting criminal offences from profiling alone
- Art. 5(1)(c) — Social scoring
- Art. 5(1)(g) — Biometric categorisation of sensitive attributes
- Art. 5(1)(e) — Untargeted scraping of facial images
- EU AI Act (Regulation (EU) 2024/1689)
- EEOC / Title VII Algorithmic Fairness (US) (Title VII, 42 U.S.C. §2000e; 29 CFR Part 1607 (UGESP))
No dissenting reading is recorded for this case. That means nobody has filed one yet — not that the classification is beyond argument. file a dissent with a source →
Baseline: of 100+, 40% were not definitively classifiable (18% clearly high-risk, 42% clearly low-risk). appliedAI Institute — AI Act risk classification of AI systems from a practical perspective
Applicable Regulations (9)
EU AI Act (Regulation (EU) 2024/1689)
unverified · verified 2026-08-12 source (as amended) EUR-LexAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — artificialintelligenceact.eu still serves the unamended 13 June 2024 text with no disclaimer, and the Commission's AI Act Service Desk pages still show pre-omnibus text with a visible omnibus disclaimer. Read the OJ or consolidated text on EUR-Lex. in force EU
Horizontal, risk-based product-safety law for AI systems and GPAI models. Extraterritorial market-place principle. Staged applicability 2025–2030 (Digital Omnibus: Art. 50 → 2 Aug 2026, Annex III → 2 Dec 2027, Annex I → 2 Aug 2028). (Digital Omnibus: Regulation (EU) 2026/1744, in force 27 July 2026).
Sanctions: Tiered: €35m / 7% (prohibited practices); €15m / 3% (Art. 9–15 high-risk obligations incl. data governance, documentation, logging); €7.5m / 1% (Art. 99(5) — incorrect, incomplete or misleading information to notified bodies or national competent authorities)
EEOC / Title VII Algorithmic Fairness (US) (Title VII, 42 U.S.C. §2000e; 29 CFR Part 1607 (UGESP))
US employment-discrimination enforcement applied to algorithmic selection tools: four-fifths adverse-impact analysis, validation of selection procedures and recordkeeping.
Sanctions: EEOC charges, disparate-impact litigation, consent decrees.
Illinois AI Video Interview Act (820 ILCS 42)
Employers that ask applicants to record a video interview and use AI analysis of the recording must notify the applicant that AI may be used, explain how the AI works and what general types of characteristics it uses, obtain consent, limit sharing, and destroy the video within 30 days of an applicant's request.
Sanctions: No fine schedule stated in the Act itself; enforcement route recorded as unsettled rather than asserted.
Illinois Biometric Information Privacy Act (BIPA) (740 ILCS 14)
Private entities collecting biometric identifiers or biometric information must publish a written retention and destruction schedule, give written notice of the specific purpose and term, obtain a written release before collection, and may not sell or profit from the data. BIPA carries a private right of action.
Sanctions: Private right of action with liquidated damages per violation as set out in § 20 of the Act; amounts and the per-scan/per-person accrual question are litigated — recorded as such, not computed here.
GDPR (Regulation (EU) 2016/679)
Applies unchanged next to the AI Act for all personal data in training, fine-tuning, RAG and inference. Key friction points: Art. 22 automated decisions, Art. 17 erasure vs. AI Act logging, Art. 35 DPIA.
Sanctions: Up to €20m or 4% of worldwide annual turnover
EU Employment Equality Directives (2000/78 et al.) (Council Directive 2000/78/EC of 27 November 2000 establishing a general framework for equal treatment in employment and occupation (anchor act) — bundled for the employment scope with Council Directive 2000/43/EC of 29 June 2000 implementing the principle of equal treatment between persons irrespective of racial or ethnic origin, and Directive 2006/54/EC of the European Parliament and of the Council of 5 July 2006 on the implementation of the principle of equal opportunities and equal treatment of men and women in matters of employment and occupation (recast))
in-force · verified 2026-09-11 in force EU
The three directives prohibit direct and indirect discrimination in access to employment, self-employment and occupation (including selection criteria, recruitment conditions and promotion), vocational training, and employment and working conditions including dismissals and pay — on grounds of religion or belief, disability, age and sexual orientation (2000/78), racial or ethnic origin (2000/43) and sex (2006/54). Indirect discrimination arises whenever an apparently neutral provision, criterion or practice puts persons of a protected group at a particular disadvantage unless it is objectively justified by a legitimate aim with appropriate and necessary means, and Art. 10 shifts the burden of proof to the respondent once facts from which discrimination may be presumed are established. An AI system that screens, ranks, scores or otherwise conditions access to jobs, promotion, pay or dismissal therefore operates as such a criterion or practice: the employer or intermediary must be able to justify any disparate effect it produces, and for disability must also provide reasonable accommodation (Art. 5).
Sanctions: Art. 17 leaves penalties to the Member States, which must lay down rules on sanctions for infringements of the national transposing provisions that may comprise payment of compensation to the victim and must be effective, proportionate and dissuasive (mirrored in 2000/43 Art. 15 and 2006/54 Art. 25).
ADA Title I & ADEA (US employment anti-discrimination) (Americans with Disabilities Act of 1990, Title I (Employment), 42 U.S.C. §§ 12111–12117, Pub. L. 101–336, title I, July 26, 1990, 104 Stat. 327, 330, as amended by the ADA Amendments Act of 2008, Pub. L. 110–325; Age Discrimination in Employment Act of 1967, 29 U.S.C. §§ 621–634, Pub. L. 90–202, Dec. 15, 1967, 81 Stat. 602 (note: Genetic Information Nondiscrimination Act of 2008, Title II, 42 U.S.C. § 2000ff et seq., Pub. L. 110–233))
Federal statutes that prohibit covered employers (15+ employees under the ADA, 20+ under the ADEA), employment agencies and labor organizations from discriminating in job application procedures, hiring, advancement, discharge, compensation, training and other terms of employment on the basis of disability (42 U.S.C. § 12112) or age 40 and over (29 U.S.C. § 623, § 631(a)). The ADA expressly treats as discrimination the use of qualification standards, employment tests or other selection criteria that screen out or tend to screen out individuals with disabilities unless job-related and consistent with business necessity, the failure to administer tests so that results reflect skills rather than impairments, the denial of reasonable accommodation, and pre-employment disability-related inquiries; the ADEA prohibits practices that deprive or tend to deprive individuals of employment opportunities because of age, subject to a reasonable-factors-other-than-age defence. An AI system that scores, ranks, filters or assesses applicants or employees is such a selection criterion or method of administration, so its screen-out effects and accommodation handling fall directly under these provisions; a system that elicits or is designed to detect health/disability status additionally falls under the § 12112(d)(2)(A) inquiry/exam bar, but passive inference of disability or age from behavior, speech or appearance is not itself a violation of that provision (see ada-12112 for the § 12112(d)(2)(A) gate).
Sanctions: ADA Title I adopts the Title VII enforcement machinery — EEOC charge processing, EEOC or Attorney General suits and private civil actions under 42 U.S.C. §§ 2000e-4, 2000e-5, 2000e-6, 2000e-8 and 2000e-9 (42 U.S.C. § 12117(a)); the ADEA is enforced through the Fair Labor Standards Act remedies of 29 U.S.C. §§ 211(b), 216 and 217, with liquidated damages 'payable only in cases of willful violations', plus a private civil action for 'such legal or equitable relief as will effectuate the purposes of this chapter' (29 U.S.C. § 626(b), (c)(1)).
§ 26 BDSG — Beschäftigtendatenschutz (DE) (§ 26 Bundesdatenschutzgesetz (BDSG) vom 30. Juni 2017 (BGBl. I S. 2097) — Datenverarbeitung für Zwecke des Beschäftigungsverhältnisses)
in-force · verified 2026-09-11 in force DEthin
§ 26 BDSG is Germany's national employment-data rule adopted under the opening clause of Art. 88 GDPR: it permits processing of employees' (including applicants' and former employees') personal data only where necessary for the decision to establish, for the performance or for the termination of an employment relationship, sets stricter conditions for consent (Abs. 2) and for special categories such as health or biometric data (Abs. 3), and allows processing on the basis of works/collective agreements (Abs. 4). Any AI system that screens applicants, scores performance, analyses video interviews or identifies staff biometrically processes Beschäftigtendaten and must satisfy this necessity test. Following the CJEU judgment C-34/21 (30 March 2023), the Bundesarbeitsgericht held on 8 May 2025 (8 AZR 209/21) that § 26 Abs. 1 BDSG does not meet Art. 88(2) GDPR and must remain unapplied, so lawfulness is assessed directly under Art. 6 GDPR while § 26 remains formally in force.
Sanctions: The BDSG's own fine provision (§ 43) reaches only breaches of § 30 (fines up to 50,000 EUR) and does not cover § 26; breaches of § 26 are enforced through the directly applicable GDPR — administrative fines under Art. 83 and compensation claims under Art. 82 (e.g. 200 EUR damages awarded in BAG 8 AZR 209/21).
AGG (German General Equal Treatment Act) (Allgemeines Gleichbehandlungsgesetz (AGG) vom 14. August 2006 (BGBl. I S. 1897), zuletzt geändert durch Artikel 15 des Gesetzes vom 22. Dezember 2023 (BGBl. 2023 I Nr. 414))
in-force · verified 2026-09-10 in force DEthin
The AGG is Germany's general anti-discrimination statute transposing Directives 2000/43/EC, 2000/78/EC, 2004/113/EC and 2002/73/EC (recast as 2006/54/EC); it prohibits direct and indirect disadvantage on grounds of race or ethnic origin, sex, religion or belief, disability, age or sexual identity in employment (§§ 6-18, expressly including selection criteria and recruitment conditions for applicants) and in civil-law mass transactions and private insurance (§§ 19-21). An AI system used for candidate screening, performance or bonus scoring, credit decisioning or insurance pricing applies 'Kriterien oder Verfahren' within § 3 Abs. 2, so facially neutral model features that disproportionately disadvantage a protected group are unlawful unless objectively justified and proportionate, and once indicia of disadvantage are shown § 22 places the burden of proving no violation on the operator.
Sanctions: Enforcement is private-law only: employers owe damages and, for non-pecuniary harm, monetary compensation (§ 15, capped at three months' salary where the applicant would not have been hired even without discrimination), civil-law counterparties owe removal, injunction, damages and compensation (§ 21), discriminatory contract terms are void (§ 7 Abs. 2) and the burden of proof shifts under § 22; the Act provides no administrative fines or criminal penalties and claims must be asserted within two months (§ 15 Abs. 4, § 21 Abs. 5).
Legal Obligations (15)
density
Art. 5 — Prohibited Practices
Bans subliminal manipulation, exploitation of vulnerabilities, social scoring, untargeted facial-image scraping, workplace/education emotion recognition, biometric categorisation of sensitive attributes.
unverified · no verification date source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — read the OJ or consolidated text on EUR-Lex.
Art. 5(1)(f) — Emotion inference at work and in education
Prohibits placing on the market, putting into service or using AI systems to infer emotions of a natural person in the areas of workplace and education institutions. Exception: use for medical or safety reasons. The most common real-world trip-wire, because sentiment analytics in HR tooling and classroom software crosses it without any biometric intent.
in-force · verified 2026-08-12 source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — read the OJ or consolidated text on EUR-Lex.
Art. 5(1)(d) — Predicting criminal offences from profiling alone
Prohibits placing on the market, putting into service or using an AI system for making risk assessments of natural persons in order to assess or predict the risk of a natural person committing a criminal offence, based solely on the profiling of a natural person or on assessing their personality traits and characteristics. Applicable since 2 February 2025. The line to the Annex III tiers 6(d) and 8(a) runs through the basis of the score: a score that supports a human assessment already based on objective and verifiable facts directly linked to a criminal activity (prior convictions, offence history) is high-risk; profiling or personality traits alone are the prohibited practice (Recital 42).
in-force · verified 2026-09-07 source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — read the OJ or consolidated text on EUR-Lex.
Art. 5(1)(c) — Social scoring
Prohibits placing on the market, putting into service or using AI systems for the evaluation or classification of natural persons or groups over a period of time based on their social behaviour or known, inferred or predicted personal characteristics, where the social score leads to detrimental or unfavourable treatment in social contexts unrelated to the context in which the data was originally generated, or to treatment that is unjustified or disproportionate to the behaviour. The prohibition binds public and private actors alike; scoring by or on behalf of a public authority that governs access to public services is its paradigm case. In force since 2 February 2025 (Art. 113(a)).
in-force · verified 2026-08-26 source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — read the OJ or consolidated text on EUR-Lex.
Art. 5(1)(g) — Biometric categorisation of sensitive attributes
Prohibits biometric categorisation systems that categorise natural persons individually on the basis of their biometric data to deduce or infer race, political opinions, trade-union membership, religious or philosophical beliefs, sex life or sexual orientation. Exception: lawful labelling or filtering of lawfully acquired biometric datasets, and categorisation of biometric data in the area of law enforcement. Inferring such an attribute is also GDPR Art. 9 special-category processing. In force since 2 February 2025 (Art. 113(a)).
in-force · verified 2026-08-26 source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — read the OJ or consolidated text on EUR-Lex.
Art. 5(1)(e) — Untargeted scraping of facial images
Prohibits placing on the market, putting into service or using AI systems that create or expand facial-recognition databases through the untargeted scraping of facial images from the internet or CCTV footage. The prohibited element is the untargeted collection for a recognition database, not face recognition as such. In force since 2 February 2025 (Art. 113(a)).
in-force · verified 2026-08-26 source (as amended) EUR-Lexconvenience mirror — not updated artificialintelligenceact.euAmended by Regulation (EU) 2026/1744. Verified 16 Aug 2026: the popular mirrors have not yet been updated — read the OJ or consolidated text on EUR-Lex.
GDPR Art. 22 — Automated Decisions
Right not to be subject to solely automated decisions with legal/similar effect; requires meaningful human involvement or explicit legal basis + safeguards.
unverified · no verification date read the article EUR-Lex
GDPR Art. 27 — EU Representative
A controller or processor not established in the Union that falls within Art. 3(2) scope (offering goods or services to, or monitoring the behaviour of, data subjects in the Union) must designate in writing a representative established in a Member State where the relevant data subjects are. The representative is mandated to be addressed by supervisory authorities and data subjects, in addition to or instead of the controller/processor, on all compliance issues — without prejudice to legal action against the controller/processor itself. Exempt: (a) occasional processing that does not involve large-scale special-category or criminal-conviction data and is unlikely to result in a risk to individuals, or (b) public authorities or bodies.
in-force · verified 2026-09-10 read the article EUR-Lex
GDPR Art. 17 — Erasure
Right to erasure collides with AI Act Art. 12 immutable logging — resolved architecturally via bitemporal data modelling + physical partition scrub.
unverified · no verification date read the article EUR-Lex
GDPR Art. 25 — Data Protection by Design
Privacy by design & default: minimisation, pseudonymisation, PII filters in pipelines and vector stores.
unverified · no verification date read the article EUR-Lex
GDPR Art. 35 — DPIA
Data-protection impact assessment for high-risk processing — pairs with AI Act fundamental-rights impact assessment (Art. 27) for public-facing high-risk systems.
unverified · no verification date read the article EUR-Lex
GDPR Art. 33/34 — Personal-Data Breach Notification
Notification of a personal-data breach to the supervisory authority and, where the risk to individuals is high, to the affected individuals themselves.
in-force · verified 2026-08-11 read the article EUR-Lex
GDPR Art. 32 — Security of Processing
Controller and processor implement technical and organisational measures appropriate to the risk, including pseudonymisation and encryption, and measures ensuring the ongoing confidentiality, integrity, availability and resilience of processing systems. Access to personal data by an unauthorised recipient — including one reached through a derived index such as a vector store — is the harm this article addresses.
in-force · verified 2026-08-17 read the article EUR-Lex
GDPR Art. 9 — Special Categories of Personal Data
Processing of health, biometric and other special-category data is prohibited unless one of the Art. 9(2) conditions applies; where it is permitted, the appropriate safeguards travel with it. This is the anchor for de-identification of clinical imaging and for the minimisation of health data in training and retrieval corpora.
in-force · verified 2026-08-17 read the article EUR-Lex
GDPR Art. 88 — Processing in the Employment Context
Opening clause: Member States may provide more specific rules for processing employees' personal data in the employment context, by law or by collective agreement, including suitable safeguards for human dignity, legitimate interests and fundamental rights, with particular regard to monitoring systems at the workplace. It is the bridge through which national employment rules — in Germany the BetrVG co-determination right and § 26 BDSG — govern workplace AI alongside the GDPR itself.
in-force · verified 2026-08-17 read the article EUR-Lex
Control Objectives (2)
obligation (article) → operationalized_by → control objective → satisfied_by → component/pattern; control objective → evidenced_by → evidence artifact
Art. 5
control layer: community mandate — propose objectives
Art. 5(1)(f)
control layer: community mandate — propose objectives
Art. 5(1)(d)
control layer: community mandate — propose objectives
Art. 5(1)(c)
control layer: community mandate — propose objectives
Art. 5(1)(g)
control layer: community mandate — propose objectives
Art. 5(1)(e)
control layer: community mandate — propose objectives
GDPR Art. 22
control layer: community mandate — propose objectives
GDPR Art. 27
control layer: community mandate — propose objectives
GDPR Art. 17
control layer: community mandate — propose objectives
GDPR Art. 25
Vector & Chunk-Level Access Control
Practice-derived control objective (not named by any provision's own text): the requesting principal's read rights on every retrieved source segment are enforced before generation. Token- or claim-based ACL filtering is applied twice — at the chunker, which writes the source ACL into chunk metadata at ingestion, and at query time in the vector store, which filters candidates by the caller's entitlements — and a response-grounding check re-validates the caller's rights on each cited segment BEFORE the answer is composed. Testable: retrieval probe with a low-privilege principal against a restricted corpus; ACL drift reconciliation between source system and index; red-team reconstruction attempt from similarity results alone.
ISO/IEC 42001 clause A.7 (indicative)
evidenced by: Vector ACL Verification Report
GDPR Art. 35
control layer: community mandate — propose objectives
GDPR Art. 33/34
control layer: community mandate — propose objectives
GDPR Art. 32
Vector & Chunk-Level Access Control
Practice-derived control objective (not named by any provision's own text): the requesting principal's read rights on every retrieved source segment are enforced before generation. Token- or claim-based ACL filtering is applied twice — at the chunker, which writes the source ACL into chunk metadata at ingestion, and at query time in the vector store, which filters candidates by the caller's entitlements — and a response-grounding check re-validates the caller's rights on each cited segment BEFORE the answer is composed. Testable: retrieval probe with a low-privilege principal against a restricted corpus; ACL drift reconciliation between source system and index; red-team reconstruction attempt from similarity results alone.
ISO/IEC 42001 clause A.7 (indicative)
evidenced by: Vector ACL Verification Report
GDPR Art. 9
control layer: community mandate — propose objectives
GDPR Art. 88
control layer: community mandate — propose objectives
Take this into your GRC tooling
A control mapping your ISO/IEC 42001 or CSA AICM workbook can ingest, and an Annex IV skeleton to start the technical file from. Indicative mappings only — cells we are not confident about are exported empty rather than filled in.
Standards & Evidence
ISO/IEC 42005 (AI Impact Assessment)
Guidance for AI system impact assessments — supports DPIA/FRIA-style analyses.
unverified · no verification date publisher ISO
evidence for: GDPR Art. 35
ISO/IEC 27001:2022 + A.8.28
Information-security management; control A.8.28 (secure coding) is the natural anchor for AI code-generation and QA workflows alongside ISO 42001.
unverified · no verification date publisher ISO
evidence for: GDPR Art. 32
JTC 21 Technical Package (prEN 18228/18229/18281–83)
CEN-CENELEC JTC 21 technical package under standardisation request M/593 (prEN 18228 trustworthiness, 18229 risk management, 18281–83 CV/NLP evaluation et al.); staged drafts, none OJEU-cited yet — Annex III applicability (Dec 2027) is Omnibus-coupled to their availability.
evidence for: EU AI Act
IEEE CertifAIEd™
Ethics certification (transparency, accountability, algorithmic bias, privacy) for products and professionals; interfaces with the EU ALTAI assessment list.
unverified · no verification date
evidence for: EU AI Act
prEN 18229-1 (Trustworthiness Framework, part 1)
Part 1 of the JTC 21 trustworthiness deliverable — the framework layer other prEN 18xxx documents build on.
evidence for: EU AI Act
Evidence you will need (7)
The concrete deliverables this use case's obligations ask for — grouped by what kind of artifact they are. Documentation is the largest single conformity cost block, so the list is a work plan, not a reading list. Full evidence matrix →
Assessments (2)
A structured judgement about risk, rights or a management system.
FRIA / AI Impact Assessment (AIIA)text-derivedserves 3 obligations
Fundamental-rights impact assessment (Art. 27, deployer-side) generalized to the AI Impact Assessment: societal, legal and operational risk evaluation per ISO/IEC 42005 and ISO 42001 Clause 8.2, defining HITL intervention parameters and acceptable-use bounds. Cadence: pre-deployment, refreshed annually and on major model updates — a stale AIIA is a finding, not a document.
verifiability: documented artefact — verifiable on inspection
chain: Art. 26 — Deployer Obligations · Art. 27 — Fundamental Rights Impact Assessment · EU AI Act · Clinical Imaging Triage & Patient Follow-Up
Data Protection Impact Assessment (DPIA)text-derivedserves 2 obligations
GDPR Art. 35 assessment for high-risk processing; supervisory-authority consultation where residual risk stays high. ISO/IEC 42005 provides the AI-specific method.
verifiability: documented artefact — verifiable on inspection
chain: § 26 Abs. 1 S. 1 — Erforderlichkeit für Begründung, Durchführung, Beendigung · GDPR Art. 35 — DPIA
Test reports (1)
Measured results from testing, evaluation or red-teaming.
Vector ACL Verification Reportpractice-derived — dispute welcome
Practice-derived artifact: the measured result of probing the retrieval path with low-privilege principals, the ACL reconciliation between source repositories and the index, and the outcome of the response-grounding rights re-check. Records which corpora were probed, which principals were used and every segment that was returned without an entitlement.
verifiability: self-asserted
chain: GDPR Art. 32 — Security of Processing → CO: Vector & Chunk-Level Access Control
Log records (1)
Machine-generated records produced while the system runs.
Event Logs & Decision Tracestext-derivedserves 17 obligations
The single highest-leverage artifact: hash-chained, WORM-stored logs with structured decision traces. Required capability fields per FprEN ISO/IEC 24970: input/output traces, execution timestamps, acting user/agent identity, referenced sources, human overrides. Audit-packet spec per event: model version, system-prompt/context hash, hyper-parameters (temperature, top-p), output payload, confidence score, active policy-ruleset versions, human override record. Simultaneously serves AI Act Art. 12, GDPR accountability, DORA incident reporting, NIS2 logging, PLD disclosure duties and its rebuttable defect presumption; financial-sector regimes push retention to 7 years (SEC 17a-4-class WORM rules). Credibility bar: anchor hash-chain heads externally (qualified timestamp / eIDAS ledger) so integrity survives an insider with admin rights.
verifiability: externally-anchored
chain: Art. 12 — Record-Keeping / Logging · CRA Art. 14 — Vulnerability & Severe-Incident Reporting · DORA Art. 19 — Major ICT-Incident Reporting · GDPR Art. 33/34 — Personal-Data Breach Notification · HIPAA Breach Notification Rule · NIS2 Art. 23 — Significant-Incident Reporting · +21 more
Process records (3)
Traces that a process actually happened, and who did it.
Human-Oversight Protocol & Intervention Recordspractice-derived — dispute welcomeserves 5 obligations
Art. 14 evidence: documented oversight design (gates, thresholds, veto powers), reviewer qualification, and the record of actual approvals, overrides and escalations — also the GDPR Art. 22 meaningful-human-involvement proof.
verifiability: documented artefact — verifiable on inspection
chain: Art. 11 — automated individual decision-making · Art. 14 — Human Oversight · GDPR Art. 22 — Automated Decisions · Art. 12 — Record-Keeping / Logging → CO: Log Access & Retention Governance · Art. 14 — Human Oversight → CO: Oversight Competence & Authority · Clinical Imaging Triage & Patient Follow-Up
Individual Explanation Letters & Counterfactual Recordspractice-derived — dispute welcomeserves 5 obligations
Practice-derived artifact: the issued adverse-decision explanations together with the attribution run, model version and counterfactual scenario that each letter rested on, so an authority or a court can check that the stated reasons are the reasons the system actually used.
verifiability: self-asserted
chain: Art. 11 — automated individual decision-making · Art. 18 — Obligation to assess the creditworthiness of the consumer · Art. 21 — examination of an application · Art. 86 — Right to explanation of individual decision-making · GDPR Art. 22 — Automated Decisions
Personal-Data Breach Notification Recordtext-derivedserves 2 obligations
The GDPR Art. 33(5) record of every personal-data breach: facts, effects, remedial action, plus the notification sent to the supervisory authority and, where required, the data subjects.
verifiability: tamper-evident
chain: GDPR Art. 33/34 — Personal-Data Breach Notification · HIPAA Breach Notification Rule
Architecture Blueprint
Required Technical Components (12)
HITL Escalation Queue & Review UI
HITL escalation queue & review UI ('Human-as-a-Tool': the agent calls the human like any other tool via propose-action objects). Confidence- and risk-threshold routing, SLA timers, structured accept/modify/reject verdicts with digital reviewer signature at gate release — each verdict is itself Art. 14 evidence and feeds the active-learning loop.
from: GDPR Art. 22
Adverse-Decision Reason Generator
Practice-derived component: converts feature attributions (SHAP or an equivalent attribution method) into an individually understandable, legally defensible explanation of an adverse decision — the role the AI system played, the main elements the decision rested on, and counterfactual scenarios stating what would have had to differ for a different outcome. Reason codes are generated from the decisioning path, not from a marketing template, and every issued letter is retained with the model version and the attribution run behind it. Honesty condition: a reason is only usable if acting on it would actually change the outcome, which non-monotonic feature interactions can break (see the post-hoc instability threat).
from: GDPR Art. 22
Bitemporal Memory (GDPR×Art.12)
valid_from/valid_to + transaction time on every record: GDPR erasure removes data from the active retrieval path while the HMAC-chained immutable log survives for Art. 12 / PLD defence; tenant-scoped partitions allow physical scrub of PII.
from: GDPR Art. 17
PII Scrubbing / DLP-NER Layer
Automated detection, pseudonymisation and blocking of personal data in inputs, retrievals and outputs.
from: GDPR Art. 25
Per-Tenant Retrieval Segmentation
Retrieval is scoped by tenant and by caller entitlement at query time, preventing cross-client and cross-role leakage through shared indexes.
from: GDPR Art. 25
Segmented Vector Store (RBAC + CMEK)
Vector indexes, embeddings and document stores are logically and physically partitioned per client, with role-based access and customer-managed encryption keys.
from: GDPR Art. 25 · GDPR Art. 32
PII/PHI Redaction & Tokenisation Engine
The engine behind inline tokenisation: pre-model interception that replaces identifiers with reversible tokens before a payload leaves the isolation boundary, plus a detokenisation gate that re-identifies only for authorised callers inside the boundary and logs every re-identification. Complements the DLP/NER scrubbing layer, which blocks or masks rather than preserving reversible reference.
from: GDPR Art. 25
DICOM De-Identification Pipeline
Practice-derived component: removal and replacement of identifying attributes in imaging studies before they leave the clinical system — header attributes per the DICOM confidentiality profiles, burned-in pixel text detected and masked, private tags dropped rather than trusted, and a consistent pseudonym per patient so longitudinal studies stay linkable without re-identifying anyone. Re-identification risk on the de-identified corpus is measured, not assumed.
from: GDPR Art. 25 · GDPR Art. 9
Live Risk Register / Posture Management
Continuously updated risk register wired to runtime posture: threat-model deltas, open defects, control status, exposure per system. Includes Shadow-AI discovery — continuous scanning for unsanctioned agents, MCP servers and AI API usage outside the register; an unregistered agent is an unmanaged Art. 12/26 liability and the empirical driver of proportionate (not blanket) controls.
from: GDPR Art. 35
Unified Incident-Response Runbook
One procedure reconciling AI Act Art. 73, GDPR Art. 33 (72h), DORA and NIS2 (24h/72h) timelines and recipients.
from: GDPR Art. 33/34
Retrieval Rails (ACL-aware RAG)
Relevance, freshness and per-user permission checks on every retrieved chunk; curated, versioned index.
from: GDPR Art. 32
Bias Testing & Data Quality Pipeline
Representativeness checks, bias metrics and mitigation per ISO/IEC 5259; versioned datasets with lineage.
from: EEOC / Title VII Algorithmic Fairness (US)
Delivery Stack & Pipeline Stage (2)
Service-as-a-Software delivery: the engines, patterns and artifacts this workflow needs on top of the generic obligations. See the full pipeline
WORM / Immutable Audit Vault
Append-only, hash-chained audit vault (WORM object-lock storage, AES-256 at rest, TLS 1.3 in transit). Guarantees tamper-evidence within the organization's trust domain — which stops your own team, but not an admin who can rebuild the vault. Pair with an external trust anchor and key ceremonies outside the operating team for evidence that holds against the insider scenario.
pipeline stage 4 — Output audit & human-in-the-loop gateway
Unified Incident-Response Runbook
One procedure reconciling AI Act Art. 73, GDPR Art. 33 (72h), DORA and NIS2 (24h/72h) timelines and recipients.
Build or Buy — Vendor Layer (6)
The graph models vendor CATEGORIES as first-class nodes and keeps named vendors as community-maintained, disputable desc content with lastVerified dates. A category is stable; a vendor list is a currency-layer object like any standard node.
Agent Orchestration & SDLC Toolkits
Developer middleware for multi-agent networks, tool-use chains, RAG abstraction, state and memory persistence, and model routing. Named products live in marketExamples; prose here describes the class. Regulatory posture: orchestration code is where autonomy tiering, propose-action objects and fallback routing get implemented — the framework choice constrains which controls are cheap and which are retrofits. Selection metrics: see meta.marketLandscape.selectionMetrics.orchestration.
unverified · verified 2026-08-18 community-maintained
selection metrics: broad model-API abstraction, state/memory management, error recovery, fallback routing hooks
supplies: HITL Escalation Queue & Review UI
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| LangChain / LangGraph | agent framework | Graph-structured agent runtime; interrupt/pause nodes support implementing human approval at defined steps. Typical: multi-step agents, approval workflows. | not checked | supports implementing Art. 14 oversight (claimed)supports Art. 12 step logging (claimed) |
| LlamaIndex | RAG framework | Indexing and query abstractions over documents and structured sources. Typical: enterprise RAG, document agents. | open source | retrieval-governance positioning |
| Microsoft AutoGen | multi-agent framework | Conversational multi-agent patterns with pluggable tool executors. Typical: multi-agent research, code agents. | not checked | research/OSS, no vendor certification |
| CrewAI | multi-agent framework | Role-based agent teams with task delegation and process templates. Typical: process automation, role-based agents. | not checked | vendor-stated security posture |
and 6 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Agent Observability & Model Risk Management
Tracing, evaluation, drift monitoring and model-validation records. This layer is where Art. 12 record-keeping becomes technically real (step-level traces, prompt/response records, retention control) and where model-risk practice in the SR 11-7 tradition — validation evidence, performance and drift monitoring, challenger comparison — is operated. Gateways and tracing tools produce the logs; the retention, integrity and access regime around them is still yours.
unverified · verified 2026-08-18 community-maintained
selection metrics: Trace completeness per agent step; log retention and immutability options; drift/quality metrics available out of the box; evaluation dataset support; export into your audit vault; self-host option.
supplies: Adverse-Decision Reason Generator · Bias Testing & Data Quality Pipeline
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| LangSmith | agent tracing & evaluation | Trace capture and evaluation over LangChain/LangGraph runs with dataset-based scoring. Typical: step tracing, regression evaluation. | not checked | SOC 2 (claimed)supports Art. 12 record-keeping (claimed) |
| Langfuse | agent tracing & evaluation | Open-source tracing, prompt management and evaluation; self-hostable for retention control. Typical: self-hosted tracing, cost/latency analytics. | open source | GDPR-positionedsupports Art. 12 record-keeping (claimed) |
| Arize AI / Phoenix | ML & LLM observability | Production monitoring with drift and performance analysis; Phoenix is the open-source tracing side. Typical: drift monitoring, production analytics. | not checked | SOC 2 (claimed)drift-monitoring positioning (SR 11-7 style, claimed) |
| Helicone | LLM gateway & logging | Proxy-level logging of prompts, costs and latency across providers. Typical: gateway logging, cost control. | not checked | SOC 2 (claimed)supports Art. 12 record-keeping (claimed) |
and 11 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
AI GRC & Governance Platforms
Second-line systems of record: model/agent inventory incl. third-party SaaS AI, automated risk tiering, policy administration, cross-framework mapping and control deduplication, audit-evidence generation, intake workflows. Named products live in marketExamples, which is the single source of truth for this layer — prose here describes the class, not the field. What the class buys you: one register a second line can defend, and evidence assembled once and reused across frameworks. Selection metrics: see meta.marketLandscape.selectionMetrics.grc. One compilation-reported item is deliberately kept as unverified: a claimed updated US banking model-risk guidance 'SR 26-2'. Two secondary compilations repeating it is corroboration of the rumour, not of the guidance; it stays flagged pending verification against Federal Reserve primary sources, and a curator verification proposal is filed. All alignments in this layer are vendor-positioned claims, never certifications.
unverified · verified 2026-08-18 community-maintained
selection metrics: multi-model/multi-cloud cataloging incl. third-party SaaS, automated risk tiering, regulatory reporting, independent-2nd-line deployability, cross-framework control deduplication
supplies: Adverse-Decision Reason Generator · Live Risk Register / Posture Management
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Credo AI | AI governance platform | Policy packs, risk tiering and evidence workflows mapped across frameworks. Typical: AI registry, policy administration. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | ISO 42001 alignment (claimed)EU AI Act readiness positioning |
| Holistic AI | AI governance & audit | Risk assessment, bias auditing and regulatory reporting workflows. Typical: bias audit, regulatory reporting. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | NYC LL144 audit support (claimed)EU AI Act readiness positioning |
| IBM watsonx.governance | AI governance platform | Governance, factsheets and monitoring integrated with the IBM stack. Typical: factsheets, model monitoring. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | ISO 42001 alignment (claimed)Art. 11 documentation support (claimed) |
| ModelOp | AI/model governance | Model and agent inventory with automated lifecycle controls for large estates. Typical: model inventory, control automation. Scope overlap: Its scope overlaps this platform's own; we have a commercial interest in the comparison. | not checked | model-risk positioning (SR 11-7 style, claimed)ISO 42001 alignment (claimed) |
and 3 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Grounding, Retrieval & Agent Memory
The grounding layer between raw sources and the model: document parsers, embedding models, vector databases and — new in the agentic era — persistent agent memory stores. Memory is the hard part: once a personal fact is embedded, GDPR Art. 17 erasure has to reach the vector and the memory record, not just the source row, and embeddings are partially reconstructable (see IronCore in the privacy layer). Retrieval quality is also a data-governance question under Art. 10: what got parsed, chunked and indexed is what the system 'knows'.
unverified · verified 2026-08-18 community-maintained
selection metrics: Parsing fidelity on your worst document class; retrieval precision/recall on a labelled set; tenant and ACL isolation model; per-vector encryption and erasure path; memory TTL and record semantics; self-host option.
supplies: Bitemporal Memory (GDPR×Art.12) · Per-Tenant Retrieval Segmentation · Segmented Vector Store (RBAC + CMEK) · Retrieval Rails (ACL-aware RAG)
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Docling | document parser | Open-source layout-aware parsing of PDFs and office formats into structured chunks. Typical: RAG ingestion, air-gapped pipelines. | self-hostable | EU sovereignty positioning |
| LlamaParse | document parser | Managed parsing service tuned for tables and complex documents feeding RAG. Typical: RAG ingestion, table extraction. | not checked | SOC 2 (claimed) |
| Amazon Textract | document parser | OCR and form/table extraction with per-page pricing inside AWS. Typical: document intake, claims processing. | not checked | SOC 2 (claimed)HIPAA-eligible (claimed)ISO 27001 (claimed) |
| Diffbot | web/knowledge extraction | Structured extraction and knowledge-graph construction from web sources. Typical: market monitoring, entity resolution. | not checked | vendor-stated security posture |
and 12 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Confidential Computing & Privacy Engines
Data-in-use protection and pre-model privacy interception: enclave and runtime encryption, key management, tokenisation vaults, PII detection and redaction, application-layer and vector encryption. Named products live in marketExamples; prose here describes the class. Select on: enclave attestation support, key custody model (external HSM / BYOK), detokenisation audit trail, latency added per call, and coverage of the identifier classes your regime actually names. Selection metrics: see meta.marketLandscape.selectionMetrics.privacy.
unverified · verified 2026-08-18 community-maintained
selection metrics: enclave attestation support, key custody (external HSM / BYOK), detokenisation audit trail, added latency per call, coverage of the identifier classes your regime names, in-boundary deployment option
supplies: PII/PHI Redaction & Tokenisation Engine · DICOM De-Identification Pipeline
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Anjuna | confidential computing | Runs workloads inside hardware enclaves without application rewrites. Typical: data-in-use protection, regulated inference. | not checked | confidential-computing positioningDORA-positioned (claimed) |
| Fortanix | confidential computing & KMS | Enclave runtime plus key management and tokenisation services. Typical: key management, data-in-use protection. | not checked | FIPS 140-2 (claimed)DORA-positioned (claimed)HIPAA-positioned (claimed) |
| Skyflow | privacy vault | Polymorphic data vault de-identifying records before they reach a model. Typical: PII vaulting, pre-model redaction. | not checked | SOC 2 (claimed)HIPAA-positionedGDPR-positioned |
| Private AI | PII detection & redaction | Detection and redaction of identifiers across text, documents and audio. Typical: inline redaction, document de-identification. | not checked | GDPR-positionedHIPAA-positioned |
and 1 more in the stack advisor →
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Secure Data Infrastructure & Vector Storage
Governed retrieval substrate: vector databases, lakehouses and catalogs with tenant/namespace isolation, RBAC and client-managed keys (CMEK), lineage into RAG chunks, air-gap options, and code-level data and AI lineage. Named products live in marketExamples; prose here describes the class. What the class buys you: retrieval that can be scoped per requester and traced back to a source record. The Art. 10 runtime data-governance duties land here. Selection metrics: see meta.marketLandscape.selectionMetrics.data.
unverified · verified 2026-08-18 community-maintained
selection metrics: namespace/tenant isolation, RBAC + CMEK, lineage into RAG chunks, SOC 2 / ISO 27001 attestations, air-gap capability
supplies: Retrieval Rails (ACL-aware RAG)
Filters to self-hostable, customer-VPC and open-source options when personal or confidential data cannot leave the EU.
| Example | Sub-category | What it does | Hosting | Claimed alignments |
|---|---|---|---|---|
| Azure AI Search | managed retrieval | Managed hybrid search with security trimming against tenant identities. Typical: ACL-aware RAG, enterprise search. | not checked | ISO 27001 (claimed)SOC 2 (claimed) |
| Databricks Unity Catalog | governed lakehouse | Catalog and lineage spanning tables, features and RAG chunks. Typical: lineage evidence, governed RAG. | not checked | SOC 2 (claimed)lineage/Art. 10 support (claimed) |
| Relyance AI | code-level data & AI lineage | Parses source repositories to map data and inference flows at code level, with CI checks on changes to those flows. Typical: data lineage, shift-left privacy review. Scope overlap: Its AI-governance reporting scope overlaps this platform's own; we have a commercial interest in the comparison. | SaaS (vendor cloud) | GDPR programme tooling (claimed)EU AI Act readiness positioning |
| Snowflake Cortex | governed lakehouse | Model calls inside the warehouse boundary with masking and clean rooms. Typical: in-warehouse inference, governed analytics. | not checked | SOC 2 (claimed)ISO 27001 (claimed)HIPAA-eligible (claimed) |
Community-maintained, disputable examples — not an endorsement and not a ranking. Alignments are as claimed by vendors or the source compilation, not verified by RAIN; a certification is shown as a certification only where a certificate or registry reference is recorded.
Disclosure: RAI·N·avigator operates in this category too, so we have a commercial interest in any comparison here. That is why this layer maps product classes to control objectives and lists named products as community-maintained examples — we publish no rankings, no quadrants and no coverage assertions about any vendor, including ourselves.
Procurement rule: Derived from three-lines-of-defense separation: the second-line GRC platform must be procured and deployed independently of any first-line runtime or model vendor — a governance tool that only sees its own vendor's models cannot govern a multi-model estate, and closed third-party SaaS AI can only be governed contractually (intake, attestation, AI-BOM disclosure), never by inline inspection.
Outsourced delivery BPO · SaaS · Service-as-a-Software caveats
Delivery Model — BPO · SaaS · Service-as-a-Software
Spectrum
BPO: input-priced (billable hours/FTEs), linear headcount scaling, human error & attrition as primary risk
SaaS: capability-priced (software access), client operates the workload, implementation/adoption failure as primary risk
Service-as-a-Software: outcome-priced (SLA on completed work), provider-managed AI executes 60–80% of cognitive tasks with specialist supervision, algorithmic bias & non-compliance as primary risk
Caveats in regulated markets
— Outcome SLAs move compliance risk onto the provider — but NOT the buyer's deployer duties: Art. 26 oversight, log retention and FRIA obligations stay with the enterprise even when execution is outsourced.
— Provider role analysis is the central legal question: a productized platform that fine-tunes, re-purposes or chains models can flip into the Art. 25 provider role with full high-risk obligations.
— Certified operations (ISO 42001) function as a procurement moat and shortcut third-party risk assessment — but organizational certificate ≠ product conformity (never conflate, see meta.assuranceEcosystem).
— The buyer's evidence chain must reach into the provider: contractually mandated AI-BOM disclosure, ZDR certificates, bias-audit reports and logging-ledger access are the artifacts that make an outsourced workflow auditable.
Threat Profile
Demographic Bias in Automated Screening
Name, language or geography features act as proxies for protected characteristics, producing systematically different outcomes across groups.
mitigate with: Bias Testing & Data Quality Pipeline, Algorithmic Bias & Fairness Audit Report
Latent Demographic Proxy Leakage in Unstructured Text
Free text carries protected characteristics without naming them: postcodes stand in for ethnicity and income, university and school names for class and nationality, employment gaps for parental leave, disability or migration. Dropping the protected field therefore removes the audit trail, not the signal — the model reconstructs the characteristic from the proxies and the disparity survives 'blind' screening.
mitigate with: PII Scrubbing / DLP-NER Layer, Bias Testing & Data Quality Pipeline, Two-Tier Air-Gapped De-Identification Ingestion