European security and compliance teams spend a lot of time talking about regulation. A new forecast report from Kiteworks suggests the harder problem sits elsewhere. According to the report, many European organizations have strong regulatory frameworks on paper, driven by GDPR and upcoming AI rules, and weaker operational systems that show how those rules work in daily practice. The gap, the report argues, shows up in areas like AI incident response, supply chain visibility, and compliance automation as organizations move toward 2026.
AI incident response remains underdeveloped
One of the most prominent gaps appears in AI-specific incident response. The report measures capabilities such as AI anomaly detection and training data recovery. Adoption levels in France, Germany, and the UK trail the global average in both categories.
AI anomaly detection adoption reaches 32 percent in France, 35 percent in Germany, and 37 percent in the UK. The global benchmark stands at 40 percent. Training data recovery shows similar results, with European adoption ranging from 40 to 45 percent compared with a 47 percent global figure.
The report links these figures to operational readiness. When AI systems behave unexpectedly, security teams rely on model-aware detection and access to training data to investigate root causes. Survey responses suggest that many organizations rely on incident response practices that do not address model behavior, data drift, or training set integrity.
Software supply chain visibility shows persistent gaps
Software supply chain controls form the second prediction. The report highlights software bills of materials and secure software development lifecycle practices as key indicators. SBOM management adoption remains limited across Europe, with 20 percent in France, 25 percent in Germany, and 23 percent in the UK. The global average sits at 28 percent, with leading regions exceeding 45 percent.
Secure SDLC practices show mixed results. Germany reports 45 percent adoption, France 32 percent, and the UK 37 percent. AI systems increase reliance on third-party components, external APIs, and shared libraries. Limited visibility into dependencies increases exposure across development and deployment pipelines.
Third-party response coordination stays limited
Third-party risk management appears as another area with low operational maturity. Continuous vendor monitoring adoption ranges from 28 to 35 percent across the three countries surveyed. Joint incident response playbooks show lower figures.
Only 4 percent of French organizations report formal joint incident playbooks with vendors. The UK reports 9 percent, and Germany reports 25 percent. The global average stands at 13 percent. The report frames joint playbooks as a coordination mechanism that defines communication paths, escalation steps, and shared responsibilities during incidents that involve suppliers or service providers.
Compliance processes rely on manual execution
The fourth prediction centers on compliance operations. Many European organizations classify their approach as continuous with partial automation. Automated policy-as-code adoption ranges from 35 to 40 percent across France and Germany, with the global benchmark at 43 percent.
Manual or semi-manual processes limit the ability to produce near real-time evidence during audits or regulatory inquiries. The report connects this issue to expanding regulatory oversight under the EU AI Act and ongoing GDPR enforcement. Evidence generation remains a recurring challenge as data flows increase across systems and jurisdictions.
Cross-border and third-party AI risks receive limited operational weight
The fifth prediction addresses cross-border AI governance and third-party AI risk. Survey respondents rank third-party AI vendor risk as a top concern at rates close to the global average. Implementation of cross-border mechanisms within workflows shows lower adoption.
European organizations report adoption rates between 28 and 32 percent for cross-border data mechanisms. Comparable figures in parts of the Middle East exceed 55 percent. The report describes this as an execution gap within a regulatory environment that already defines expectations for cross-border data handling.
A shift toward operational proof
Across all five predictions, the report emphasizes a common theme. Policy frameworks and governance models exist. Systems that produce continuous, verifiable proof of compliance show lower maturity.
The report outlines several priority investments for organizations preparing for 2026. These include building AI-aware incident response playbooks, expanding SBOM management, formalizing third-party response coordination, automating compliance evidence generation, and treating cross-border AI data flows as dedicated risk domains.
“Europe has led the world on AI governance frameworks with the AI Act setting the global standard for responsible AI deployment. But governance without security is incomplete,” says Wouter Klinkhamer, GM of EMEA Strategy & Operations, Kiteworks. “When an AI model starts behaving anomalously. Such as accessing data outside its scope, producing outputs that suggest compromise, or failing in ways that expose sensitive information. European organisations are less equipped than their global counterparts to detect it. That’s not a compliance gap. That’s a security gap.”