GDPR and the EU AI Act are often treated as overlapping checklists, but they govern different layers of risk. GDPR is primarily about lawful processing of personal data and data-subject rights. The AI Act is primarily about AI system risk, role-based obligations, and safeguards around development/deployment in specific contexts. For SMEs, compliance success depends on integrating both frameworks into one operating model.
Why teams confuse GDPR and AI Act
Both frameworks use concepts like transparency, accountability, risk management, and documentation. But the legal trigger is different. GDPR activates when personal data is processed. The AI Act activates when AI systems are developed, placed on market, or deployed in covered contexts. Some AI use cases involve little personal data but still create AI Act obligations. Other processes may be GDPR-heavy with minimal AI Act exposure.
Practical overlap zones
1) Automated decision and human impact
When AI outputs influence hiring, eligibility, pricing, or service access, both rights and system-risk concerns emerge. GDPR asks whether processing is lawful and rights are protected. The AI Act asks whether controls, oversight, and documentation are appropriate for risk level.
2) Transparency
GDPR requires privacy information around data processing. The AI Act adds AI-specific transparency obligations in defined contexts (for example AI interaction/content disclosures). SMEs should harmonize notice design so users are not forced to decode fragmented legal text.
3) Accountability evidence
GDPR expects records and policy evidence; AI Act expects system-level evidence (risk classification, oversight, monitoring, technical documentation where required). Teams should keep one traceable evidence structure mapped to both frameworks.
Where they differ most
- Primary object: GDPR = personal data lifecycle; AI Act = AI system lifecycle.
- Control granularity: GDPR centers processing principles; AI Act centers role/risk-specific system controls.
- Role model: GDPR has controller/processor; AI Act has provider/deployer/importer/distributor/authorized representative.
- Assessment style: GDPR often uses DPIA for high-risk processing; AI Act may require additional system governance artifacts and (for relevant cases) FRIA-like analyses.
One integrated SME compliance architecture
Use a shared control stack:
Unified inventory
Track both data and AI-system metadata in a single register.Dual-role mapping
Document GDPR role (controller/processor) and AI Act role (provider/deployer/etc.) per use case.Joint risk workflow
Run rights/data risk and AI system risk in coordinated reviews.Common ownership model
Assign product, legal, compliance, and operations owners with clear escalation.Single evidence index
Store notices, assessments, logs, incident records, and review approvals in one auditable repository.
Example: AI-assisted recruitment workflow
- GDPR lens: lawful basis, fairness, data minimization, rights handling.
- AI Act lens: potential Annex III high-risk context, oversight requirements, logging, documentation quality.
- Operational output: one documented workflow with both rights and system controls; no duplicate committees.
Common integration failures
- Separate legal and technical tracks with no synchronization.
- Privacy notices updated, but AI interaction notices missing.
- No trigger to re-assess controls after model change.
- Evidence scattered across teams with no index.
90-day implementation plan for SMEs
Days 1-30: inventory + role mapping + priority use-case list.
Days 31-60: transparency harmonization + oversight controls + logging standards.
Days 61-90: integrated evidence pack + governance cadence + incident simulation.
Final takeaway
GDPR and the AI Act should be run as one coordinated governance program. SMEs that integrate controls reduce legal ambiguity, move faster in audits/procurement, and avoid expensive rework. The goal is not parallel compliance projects. The goal is one reliable operating system for trustworthy AI and lawful data use.