AI Governance Briefing — April 21, 2025
Zeph Tech is stress-testing general-purpose AI models against systemic-risk scenarios so May’s code-of-practice submissions show robust evaluation coverage ahead of August obligations.
Executive briefing: General-purpose AI (GPAI) providers face near-term EU AI Act checkpoints: Article 56 expects documented evaluation methodologies, Article 55 relies on codes of practice to bridge the gap before harmonised standards arrive, and Article 53 elevates oversight when a model could trigger systemic risk. Zeph Tech is turning these obligations into a full safety-testing playbook with adversarial drills, reproducible benchmarks, and incident-ready reporting. This briefing sits inside the AI pillar hub at Zeph Tech AI tools, the dedicated EU AI Act governance guide, and companion briefs on systemic-risk mitigation cycles and provenance and labelling so stakeholders can track one cohesive GPAI assurance path.
Legal checkpoints we build against
- Codes of practice (Article 55). Providers must collaborate on codes that translate statutory duties into verifiable methods before harmonised standards are published.
- Evaluation transparency (Article 56). GPAI providers need to document testing methodologies, metrics, and limitations, and share them with deployers so downstream users can implement safeguards.
- Systemic-risk triggers (Article 53). When a GPAI system can materially influence democratic processes, critical infrastructure, or public health and safety, providers owe heightened monitoring and fast incident reporting.
- Provider-deployer support (Articles 53–56). Providers must furnish technical guidance, mitigations, and corrective updates so deployers can address misuse or failures quickly.
Assurance blueprint
The programme anchors every test to an Article 53–56 requirement and keeps artefacts audit-ready.
| EU AI Act expectation | Zeph Tech control | Evidence produced |
|---|---|---|
| Codes of practice outline evaluation and mitigation | Shared evaluation handbook with threat models, test harnesses, and severity scoring | Versioned handbook, change log, reviewer sign-off |
| Testing covers misuse, emergent behaviours, and safety limits | Adversarial red-team suites (prompt injection, model self-escalation, content integrity) plus domain tests for elections, health, finance | Test scripts, reproducible seeds, false-positive/false-negative rates |
| Systemic-risk monitoring and escalation | Control tower that watches model outputs for policy violations and incident thresholds aligned to Article 53 triggers | Alert playbooks, on-call rosters, incident tickets with timestamps |
| Downstream deployer support | Integrator guides with safe-configuration defaults, rate limits, content filters, and rollback steps | Configurator templates, API guardrail settings, deployment checklists |
| Transparency for limitations | Capability cards that state intended use, known gaps, and non-permitted contexts | Capability card PDFs, release notes, cross-references to known issues |
Discover risks -> Design tests -> Run & score -> Gate release -> Monitor -> Detect incident -> Triage & report -> Patch & document
Alt text: Linear diagram showing the GPAI lifecycle from risk discovery through testing, gated release, monitoring, incident detection, reporting, and patching.
Evaluation depth and coverage
Testing focuses on the statutory themes—misuse resistance, emergent behaviour detection, and mitigation efficacy—described in the Commission’s Q&A and the EU AI Act.
- Adversarial resilience: Prompt-injection and data-exfiltration attempts, output manipulation, safety bypass trials, and hallucination stressors. Each scenario records inputs, outputs, and recovery steps.
- Emergent-behaviour probes: Long-context tasks, tool-use chaining, and multi-agent simulations to watch for uncontrolled escalation or capability shifts that could signal systemic risk.
- Domain harms: Election integrity (mis/disinformation generation, synthetic persona creation), health misinformation, financial manipulation, and critical infrastructure guidance misuse.
- Guardrail verification: Safety filters, rate limits, and policy classifiers tested for both over-blocking and under-blocking; results feed the deployer configuration guide.
- Calibration checks: Transparency on confidence and limitations, including citation accuracy and grounding performance to prevent over-reliance by deployers.
Scoring, gating, and documentation
Every release is gated by minimum scores tied to Article 56 transparency and Article 53 systemic-risk obligations.
- Severity matrix: We map each failure to impact (consumer harm, societal stability, infrastructure risk) and likelihood, flagging systemic-risk candidates for AI Office notice prep.
- Release thresholds: Builds ship only when critical severity defects are mitigated or compensating controls are in place, and when documentation of known limitations is complete.
- Traceability: Test cases link to risk statements, mitigations, and code commits so authorities and deployers can verify claims.
| Impact | Likelihood | Action |
|---|---|---|
| High societal/critical infrastructure | Likely | Block release; prepare Article 53 incident dossier |
| High | Unlikely | Mitigate or ship with compensating controls and documented limitations |
| Medium | Likely | Strengthen guardrails and monitor; include deployer alerts |
| Medium/Low | Unlikely | Document and monitor |
Alt text: Table showing how impact and likelihood drive release actions, including blocking, compensating controls, or documentation.
Provider-to-deployer support
Because the EU AI Act expects GPAI providers to equip deployers with mitigations, every evaluation cycle produces downstream assets.
- Safe defaults: Recommended API parameters, redaction settings, and output filters that align with the capability card.
- Integration drills: Joint tests with pilot deployers to validate the provider’s mitigations in real workloads (e.g., customer service triage, content moderation queues).
- Incident starter pack: Customer-ready notice templates, rollback steps, and patch validation instructions to shrink response times if a systemic-risk event emerges.
Metrics and evidence expected by authorities
Metrics mirror the Q&A emphasis on demonstrating safety and control.
- Coverage: Percentage of identified misuse scenarios tested and percentage of mitigations validated.
- Time to mitigation: Mean time from detection to patch for high-severity issues.
- Residual risk: Count of known limitations disclosed to deployers and any compensating controls.
- Systemic-risk watch: Number of outputs or behaviours that meet Article 53 risk indicators and status of follow-up monitoring.
- Documentation freshness: Days since last update to capability cards, evaluation handbook, and deployer guides.
Records and retention
We maintain a single repository with:
- Signed evaluation reports with methodology descriptions and reproducibility artefacts.
- Incident simulations, including communication templates, to demonstrate readiness for Article 53 notifications.
- Change histories for guardrails, model versions, and deployment guidance.
- Meeting minutes with regulators or standards bodies to show participation in codes of practice.
Timeline to August 2025 obligations
| Month | Milestone | Evidence |
|---|---|---|
| April 2025 | Complete baseline adversarial suite and publish capability card draft | Test results, capability card v0.9, mitigations list |
| May 2025 | Submit code-of-practice contribution and integrator guide v1.0 | Submission package, deployer configuration defaults |
| June 2025 | Run systemic-risk drills and authority-notice dry runs | Drill reports, incident templates, on-call rota |
| July 2025 | Finalize evaluation handbook and release gating thresholds | Signed handbook, severity matrix, approvals |
| August 2025 | Publish refreshed capability cards and deployer mitigations aligned to latest AI Office guidance | Updated docs, evidence of deployer distribution |
Next steps for stakeholders
- Product leads: Integrate the release gates into roadmaps and block launches that lack Article 56-ready documentation.
- Security and safety teams: Expand red-team scenarios that map to Article 53 systemic-risk triggers and prepare rapid notification packets.
- Customer-facing teams: Align SLAs and messaging to the capability card so deployers receive consistent guidance.
- Governance owners: Track participation in code-of-practice forums and record any commitments made to authorities.
Zeph Tech will keep this briefing synced with the AI pillar hub, the EU AI Act governance guide, and related briefs on systemic-risk mitigation cycles and provenance labelling so every GPAI release remains defensible under Articles 53–56.
Coordination with standards and audits
Codes of practice are a bridge until harmonised standards arrive, so we mirror emerging drafts from CEN/CENELEC and keep mappings to NIST AI RMF and ISO/IEC 42001 controls. That alignment lets external auditors and national authorities compare Zeph Tech’s safeguards to familiar control families. For each quarterly release, we host a readiness review that walks auditors through the evaluation handbook, systemic-risk watchlist, and deployer support kit, ensuring every claim in the capability card is backed by runbooks and logs.
We also maintain delta logs that show what changed between model versions—training data refreshes, prompt-blocking updates, or moderation policy adjustments—so deployers and regulators can verify how safety posture evolves. Those deltas accompany distribution notices to customers, which satisfies the EU AI Act expectation that GPAI providers keep downstream users informed about new or residual risks.
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