AI Act Audit — Prohibited Practices & AI-Content Transparency

Audit a target system (codebase, application, product spec, or described system) against two AI Act compliance layers:

1. Prohibited practices — Article 5 Reg. (EU) 2024/1689, as interpreted by the European Commission Guidelines C(2025) 5052 final (29.7.2025) — converted in full as references 00–09.
2. Transparency of AI-generated content — Article 50(2), (4) and (5), using the Code of Practice on Transparency of AI-Generated Content ("CoP") as the operational benchmark — converted in full as references 10–11.

Output: a structured Markdown audit report (see assets/report-template.md).

Scope and limits — state these in every report

• This audit covers Article 5 (prohibited practices) and Article 50(2)/(4)/(5) (transparency of AI-generated content). OUT of scope: high-risk classification and obligations (Art. 6 + Annexes I/III — postponed by the Digital Omnibus to 2 Dec 2027 for standalone Annex III systems and 2 Aug 2028 for product-embedded systems), Art. 50(1) (disclosure of AI interaction, e.g. chatbots) and Art. 50(3) (disclosure of emotion recognition / biometric categorisation systems), GPAI model obligations (Chapter V), and AI literacy (Art. 4). If the audit surfaces likely obligations under those layers, flag them in "Out-of-scope observations" — do not analyse them in depth.
• Timing (as amended by the Digital Omnibus on AI, Reg. (EU) 2026/1744, OJ 24.7.2026): the Art. 5 prohibitions apply since 2 February 2025; the Art. 50 transparency obligations apply since 2 August 2026. ONE transitional exception (Art. 111(4) AI Act as amended): providers of generative AI systems already placed on the market before 2 August 2026 have until 2 December 2026 to comply with the Art. 50(2) machine-readable marking obligation only — all other Art. 50 duties (incl. deep fake and published-text labelling) apply from 2 August 2026 with no grace period. The CoP additionally sets 2 February 2027 as the deadline for watermark-detection interoperability solutions (CoP S1, Measure 3.4(c)). The Digital Omnibus also adds new prohibitions applicable from December 2026 on AI generation of non-consensual sexual or intimate content depicting real persons, including "nudification" tools (creating nude imagery of real persons or digitally removing clothing) and strengthened prohibitions on AI-generated CSAM — see Step 3 note.
• Penalties: Art. 5 violations up to EUR 35 million or 7% of worldwide annual turnover (Guidelines ¶¶ 55–57); Art. 50 violations up to EUR 15 million or 3%.
• The Guidelines are non-binding and the CoP is a voluntary compliance pathway: adherence is not conclusive evidence of compliance, and providers/deployers may demonstrate Art. 50 compliance by other adequate means. In the CoP, only "will" measures are mandatory-under-the-Code; "encouraged"/"may" measures are voluntary — never report a missing optional measure as non-compliance, at most as a recommendation.
• The report is an engineering-level compliance screening, not legal advice — always recommend qualified legal review for any finding that is not clearly "No issue identified". Never give a definitive legal verdict; use the verdict scale below.

Workflow

Step 1 — Inventory the AI features

Identify every AI-relevant capability in the target. For a codebase, search for signals; for a described system, ask the user targeted questions about the same categories.

Signals to search for (grep/glob, adapt to stack):

• Model/LLM integrations & generative outputs: openai, anthropic, claude, gpt, gemini, mistral, llama, huggingface, transformers, diffusion, stable, dall, tts, text-to-speech, image generation, inference, completion, embedding
• Recommenders / personalization / engagement: recommend, personaliz, ranking, feed, engagement, retention, nudge, ab_test, experiment
• Scoring / rating of people: score, scoring, trust, rating, creditworth, risk_score, churn, segment, profil
• Biometrics / face / voice: face, facial, biometric, recognition, fingerprint, voiceprint, opencv, mediapipe, rekognition, dlib
• Emotion / sentiment about persons: emotion, sentiment, mood, affect, stress, attention
• Scraping / data collection: scrape, crawler, selenium, playwright, beautifulsoup, cctv, rtsp
• Crime/fraud prediction about individuals: fraud, predict, criminal, offence, recidiv, watchlist
• Vulnerable-audience targeting: age gates, children, minor, elderly, disability combined with targeting/ads logic, socio-economic segmentation
• Transparency infrastructure (or its absence): watermark, c2pa, content credentials, synthid, provenance, metadata signing, exif/xmp writing, AI-label UI components, "AI generated" strings in templates

Also review: privacy policy, ToS, data flows, third-party AI SDKs, content pipelines (does AI output get published/exported?), upload/CMS workflows, admin/HR tooling.

Record each feature as: name, what it does, data it uses, who it affects, whether it generates or manipulates audio/image/video/text, and the provider-vs-deployer role of the audited organisation.

Step 2 — Applicability screening

Read references/00-overview-scope-exclusions.md and check, per feature:

1. Is it an AI system (Art. 3(1))? Rule-based non-adaptive logic is generally out; ML/LLM-based inference is in.
2. Is there a placing on the market / putting into service / use in the EU (¶¶ 11–14)?
3. Which role: provider, deployer, or both (¶¶ 15–20)? Art. 5 binds both; Art. 50(2) binds providers of generative AI systems; Art. 50(4) binds deployers.
4. Do exclusions apply: national security/defence/military (¶¶ 22–28), R&D prior to market placement (¶¶ 30–33), personal non-professional use (¶¶ 34–35), open-source release conditions (¶ 36)?
5. Note: GPAI systems (e.g. an integrated LLM) can trigger prohibitions through their actual use even if the model provider's intended purpose is neutral (¶¶ 39–41); a downstream company that integrates a model and offers a generative system under its own name is typically the provider of that system for Art. 50(2).

If a feature is out of scope, record it with the reason and stop there for that feature.

Step 3 — Triage against the eight prohibitions (Article 5)

For each in-scope feature, screen with this table. If ANY trigger question is plausibly "yes", the prohibition is triggered for deep assessment in Step 5.

Sentiment analysis on text alone is generally not "emotion recognition" under (f) unless based on biometric data (see ¶¶ 244–252) — but check the reference before clearing it.

Digital Omnibus additions (applicable from December 2026): also screen any image/video generation or editing feature for the new prohibitions introduced by Reg. (EU) 2026/1744 — AI systems for creating non-consensual sexual or intimate content depicting real persons, including nudification/clothing-removal capabilities, and AI-generated child sexual abuse material. The full amended text is NOT included in this skill's references: for a triggered finding, cite Reg. (EU) 2026/1744, verdict at most 🟠 "Needs legal review" with a recommendation to verify against the official text (EUR-Lex, OJ L series 24.7.2026), and note the December 2026 applicability date.

Step 4 — Transparency screening (Article 50(2)/(4)/(5))

Run BOTH checks for every in-scope feature that generates or manipulates synthetic audio, image, video or text:

A. Provider marking & detection duties — Art. 50(2)+(5), CoP Section 1 (references/10-…). Triggered if the audited organisation places on the market or puts into service a generative AI system (including a product embedding a third-party model under its own responsibility). Check the mandatory ("will") measures:

1. Marking (Commitment 1, Measure 1.1): outputs marked machine-readably; default is multi-layered marking — digitally signed metadata (Sub-m. 1.1.1) plus imperceptible watermarking (Sub-m. 1.1.2). Single layer suffices only for: closed-environment embedded products, and free-form text (which cannot carry metadata; watermark layer per 1.1.2, not required under 200 tokens — see Glossary "Very short text").
2. Non-removal (Measure 1.2): preserve existing metadata markings on inputs; prohibit tampering in ToS/AUP; no circumvention tools.
3. Detection (Commitment 2): a detection solution available (spec, software, or API), free of charge (limited fee exceptions <1M monthly users), accessible to the exposed audience, privacy-compliant with zero-retention (Sub-m. 2.1.3), results disclosed clearly and accessibly (Measure 2.3, EAA/WCAG).
4. Quality (Commitment 3): effectiveness, reliability, robustness (typical processing + adversarial attacks), interoperability (staged; metadata standards now, watermark-detection interop by 2 Feb 2027).
5. Compliance process, testing, training, MSA cooperation (Commitment 4).

B. Deployer labelling duties — Art. 50(4)+(5), CoP Section 2 (references/11-…). Triggered if the audited organisation deploys AI to produce (i) deep fakes (Art. 3(60): AI image/audio/video resembling real persons/objects/places/events that would falsely appear authentic) or (ii) text published to inform the public on matters of public interest without human editorial control. Check:

1. Disclosure (Commitment 1): EU icon (Annex 1: "AI GENERATED" / "AI MODIFIED") or equivalent label meeting design specs (Measure 1.1) and placement specs (Measure 1.2): perceivable without user action, at first exposure, embedded or equivalent overlay, repeated for video/live/audio, accessibility (EAA/WCAG).
2. Internal processes (Commitment 2): compliance documentation, awareness/training, review + remediation of mislabelled content.
3. Artistic/creative/satirical works (Commitment 3): lighter regime — disclosure that does not hamper enjoyment of the work.
4. Editorial-control exception (Commitment 4): for published text, the Art. 50(4) subpara 2 exception applies where human review/editorial control exists and a person holds editorial responsibility — verify the policy elements (named responsible person, organisational measures, published contact).

If neither A nor B is triggered (no generative outputs, or outputs never leave a fully internal context), record "Not applicable" with the reason. Remember Art. 50(5): disclosure at the latest at first exposure, clear, distinguishable, accessible — this applies transversally.

Step 5 — Deep assessment of triggered items

For each triggered prohibition or transparency duty, read the full corresponding reference file and check every condition/mandatory measure it lists. For each: met / not met / uncertain, with evidence (file paths and line references for code, quotes for docs/UX copy) and citations — Guidelines paragraphs as (Guidelines ¶ N), Code of Practice as (CoP S1, Measure 2.1) / (CoP S2, Sub-measure 1.2.2).

An Art. 5 practice is prohibited only if all cumulative conditions are fulfilled; uncertainty on any condition → "Needs legal review", never "Prohibited". For Art. 50, a gap in a "will" measure of the CoP indicates likely non-compliance with the underlying obligation (unless compensated by demonstrably equivalent alternative means, which the report should note); gaps in "encouraged"/"may" items are recommendations only.

Step 6 — Write the report

Use assets/report-template.md. Verdict scale per finding:

• 🔴 Likely violation — Art. 5: all cumulative conditions plausibly met / Art. 50: mandatory obligation clearly unmet; urgent legal review + remediation required
• 🟠 Needs legal review — triggered, one or more conditions/obligations uncertain
• 🟡 Attention — currently compliant/out of the prohibition but close to the line (e.g. lawful persuasion, ¶¶ 127–133) or relying on an exception that needs documentation; document safeguards
• 🟢 No issue identified — obligations met or not triggered
• ⚪ Not applicable — no relevant feature

Every non-🟢/⚪ finding must include: evidence, the failed/uncertain conditions or measures, citations, and concrete remediation options. Write the report in English unless the user asks otherwise. Save it as a Markdown file and present it to the user.

Reference navigation

References 00–09 are the full converted text of Guidelines C(2025) 5052; bold markers **(N)** are official paragraph numbers (1–433) — always cite them. Inline trailing digits (e.g. harm.105) are footnote references, resolved in 09-footnotes.md. References 10–11 are the full converted Code of Practice on Transparency of AI-Generated Content; cite by Section/Commitment/Measure.

Read only the files relevant to the triggered items (plus 00-…, always). Do not load everything by default.

Rules

1. Always run Steps 1–2 even if the user asks about a single prohibition or only about transparency — context determines applicability.
2. Cite Guidelines paragraphs / CoP measures for every substantive claim in the report.
3. Be conservative: prefer "Needs legal review" over both false alarms and false clearances.
4. Distinguish CoP "will" (mandatory under the Code) from "encouraged"/"may" (voluntary): only "will" gaps count against compliance.
5. If the target has no AI features at all, still produce the report: inventory, "no AI system in scope" conclusion, and the standard scope/limits disclaimer.
6. If the user provides only a partial description, list the unanswered screening questions in the report under "Information gaps".