New Research: Europe’s AI Security Controls Trail Global Benchmarks as Attack Surface Expands
Kiteworks, which empowers organisations to effectively manage risk in every send, share, receive, and use of private data, today released its Data Security and Compliance Risk: 2026 Forecast Report. The comprehensive analysis reveals that European organisations trail global benchmarks on the security controls needed to detect AI-specific threats, respond to AI-enabled breaches, and govern AI data flows.
The research, based on a survey of security, IT, compliance, and risk leaders across 10 industries and 8 regions, exposes a widening gap between Europe’s regulatory leadership, with regulations such as the EU AI Act, and its actual AI security posture.
European organisations trail on AI anomaly detection (France 32%, Germany 35%, the UK 37% vs. 40% global), training-data recovery (40% to 45% vs. 47% global), and software bill of materials (SBOM) visibility for AI components (20% to 25% vs. 45%+ in leading regions). When AI systems behave unexpectedly – or when AI-enabled attacks target European infrastructure – most organisations lack the detection capabilities to identify the threat. This can result in compliance fines and negative brand exposure as well as breaches of sensitive data.
“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.”
The report identifies six predictions for European organisations in 2026:
1. AI-specific breach detection will lag other regions. France (32%), Germany (35%), and the UK (37%) all trail the 40% global benchmark on AI anomaly detection (the capability to identify when AI models behave unexpectedly). When AI-enabled attacks exploit model vulnerabilities or AI systems access data outside their intended scope, European organisations will be slower to detect the breach, exacerbating the detrimental impact of the exposure.
2. AI incident response will remain incomplete. Training-data recovery (the ability to diagnose AI failures by examining what the model learned from) sits at 40% to 45% across Europe versus 47% global and 57% in Australia. Without this capability, organisations can’t forensically analyse AI incidents or prove what went wrong to regulators.
3. AI supply chain visibility will remain a blind spot. SBOM adoption for AI components sits at 20% to 25% across Europe versus 45%+ in leading regions. Organisations can’t secure AI models built on third-party components they can’t see. As attackers increasingly target vulnerabilities in AI libraries, datasets, and frameworks, this visibility gap stops being a compliance checkbox and becomes an open door. Organisations without component inventories can’t detect exposure, can’t trace compromise origins, and can’t respond until damage is already done.
4. Third-party AI vendor incidents will catch organisations unprepared. Only 4% of French organisations and 9% of UK organisations have joint incident response playbooks with their AI vendors. When a vendor’s AI system is compromised – and that compromise flows into European infrastructure – organisations won’t have the detection mechanisms, communication channels, or containment protocols in place. The breach spreads before they know it exists.
5. AI governance evidence will remain manually generated. European organisations cluster in “continuous but manual” compliance rather than automated evidence generation. This creates dual financial exposure. Regulators assessing fines will find documentation that is slow to produce and inconsistent in quality, while insurers adjudicating breach claims may deny coverage entirely if organisations cannot demonstrate adequate AI governance controls were in place. Governance thus becomes a payout gap.
6. AI incident response will remain incomplete. Training-data recovery (the ability to diagnose AI failures by examining what the model learned from) sits at 40% to 45% across Europe versus 47% global and 57% in Australia. Without this capability, the risk window becomes more severe while compliance exposure becomes more difficult, with organisations unable to forensically analyse AI incidents or prove what went wrong to regulators.
The implications extend beyond compliance. AI systems are increasingly processing sensitive data, making autonomous decisions, and integrating with critical infrastructure. Every AI model that can’t be monitored for anomalies is a system where adversarial inputs, data poisoning, or model manipulation go undetected. Every third-party AI component that can’t be tracked is a dependency where upstream compromises silently inherit into your environment. Every AI vendor relationship without a joint incident playbook is a breach that spreads unchecked across organisational boundaries.
These aren’t governance failures waiting for a regulatory audit. They’re attack surfaces waiting for an adversary. Compliance gaps carry the abstract risk of penalties. Security gaps carry the concrete certainty of compromise: data exfiltration, manipulated outputs, operational disruption. The difference is between a fine you can budget for and a breach you can’t predict.
The global report, which includes 15 predictions across data visibility, AI governance, third-party risk, and compliance automation, identifies “keystone capabilities” – unified audit trails and training-data recovery – that predict success across all other security metrics, showing a measurable advantage for organisations that have implemented them.
“The AI Act establishes what responsible AI governance looks like. The question for European organisations is whether they can secure what they’re governing,” adds Klinkhamer.
“By end of 2026, the organisations that have closed the gap between AI policy and AI security through anomaly detection, training-data recovery, supply chain visibility, vendor incident coordination will be positioned for both compliance and resilience. Those still running AI workloads without detection capabilities will learn about their security gaps the hard way: from attackers, not auditors.”
Download the full Data Security and Compliance Risk: 2026 Forecast Report here. For the European brief on the report, get it here.
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