GenShield AI
EU AI Act: What Changes for You
A practical guide to new obligations, risk classes, and a roadmap to stay compliant without slowing innovation.
Read the Playbook →Resources & Insights
Trusted Guidance for AI Security Leaders
Field-tested playbooks on compliance, model risk management, and trustworthy AI operations - written by the team securing Europe's most ambitious AI programmes.
Regulation
EU AI Act: What Changes for You
The EU AI Act introduces a harmonised rulebook for AI across the bloc. What felt theoretical is now concrete: obligations, audit trails, and ongoing monitoring are table stakes for any provider or deployer of AI systems. The upside? With the right operating model, compliance becomes an accelerator for trust and market entry.
1. Map Your Inventory Against Risk Classes
Start by cataloguing AI systems and labelling them against the Act's risk categories. High-risk systems - everything from biometric identification to credit scoring - require registration, conformity assessments, and post-market supervision.
- Build or import an AI asset register that tracks purpose, data sources, and model lineage.
- Assign a risk steward responsible for every high-risk use case.
- Document mitigations and fallback processes for users when automation fails.
2. Operationalise Continuous Compliance
Static documents won't satisfy regulators. You need living controls that capture drift, prompt misuse, or bias in production.
- Instrument runtime monitoring for input anomalies and output policy breaches.
- Automate logging for training data updates and model retraining events.
- Establish a cross-functional review board that approves significant changes before deployment.
3. Turn Compliance Into a Trust Signal
Customers expect proof, not promises. Package your AI Act readiness into business enablement.
- Publish assurance briefs that explain safeguards in plain language.
- Align your messaging with ISO/IEC 42001, NIS2, and sector-specific frameworks.
- Use GenShield AI's attestations to demonstrate how automation supports human oversight.
Need help accelerating your readiness? Our compliance pods deliver gap assessments, remediation sprints, and board-ready reporting.
Back to top ↑Defence
OWASP ML Top 10 (Explained)
The OWASP ML Top 10 crystallises the attack surface for machine learning workloads. Translating that list into action requires more than patchwork mitigation - it calls for a defence-in-depth posture that spans pipelines and runtime.
Build Security into Data Pipelines
Data is the choke point. Establish guardrails well before models reach inference.
- Automate dataset provenance checks to detect untrusted contributions and tampering.
- Embed differential privacy or anonymisation steps where personal data is involved.
- Validate labels and reject corrupted batches with statistical and semantic tests.
Harden Models and APIs
Adversaries target gradients, weights, and inference interfaces.
- Enable adversarial training for high-value models and rotate perturbation strategies.
- Throttle inference, enforce authentication, and monitor for scraping patterns.
- Use canary models to detect drift, poisoning, or model extraction attempts.
Monitor in Real Time
Visibility turns unknown unknowns into manageable incidents.
- Stream detections into your SIEM and correlate with traditional security telemetry.
- Trigger automated quarantine workflows when responses breach policy.
- Capture forensic artefacts to support retrospective analysis and regulator inquiries.
ModelGuard from GenShield AI ships with OWASP-aligned playbooks, red-team prompts, and reporting tailored for risk owners and auditors.
Back to top ↑Governance
AI TRiSM in Practice
AI Trust, Risk, and Security Management (TRiSM) is the connective tissue between policy and production. The mandate: sustain model performance while respecting ethics, privacy, and resilience. Here's how leading organisations operationalise TRiSM with SentinelX.
Codify Organisational Guardrails
Translate board-level principles into measurable controls.
- Issue model charters that define approved use cases, user cohorts, and fairness targets.
- Integrate DPIA and algorithmic impact assessments into your change requests.
- Set escalation paths for incidents that affect safety, legality, or reputational exposure.
Connect Monitoring with Governance
Dashboards and alerts are only useful when they trigger action.
- Route policy breaches to accountable owners with automated notifications.
- Feed runtime metrics into quarterly risk reviews and board packs.
- Link model KPIs with business outcomes to show value alongside control.
Scale with Confidence
Once guardrails are proven, accelerate responsibly.
- Reuse approved components from SentinelX blueprints to spin up new use cases quickly.
- Simulate scenario failures to test response readiness.
- Share trustworthy AI attestations with partners and regulators to unlock new markets.
SentinelX centralises policies, monitoring, and human oversight so your teams can build fast without sacrificing governance.
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