As AI copilots and assistants become integral to business operations, security focus has traditionally been on protecting the AI models themselves. However, recent incidents demonstrate that the real security risk lies in the workflows embedding these models. Two malicious Chrome extensions masquerading as AI helpers were found stealing ChatGPT and DeepSeek chat data from over 900,000 users, highlighting the threat of data exfiltration via third-party tools. Additionally, researchers have shown that prompt injections hidden in code repositories can manipulate AI coding assistants, such as IBM's, to execute malware on developers' machines. These attacks do not compromise the AI algorithms but exploit the context in which AI operates—its inputs, outputs, and integrations. AI systems rely on probabilistic decision-making without inherent trust boundaries, making them susceptible to carefully crafted inputs that cause unintended behavior. This expands the attack surface to every integration point and data channel the AI interacts with. Traditional security controls, designed for deterministic software and clear trust boundaries, fail to detect or prevent these threats because malicious payloads are natural language inputs rather than code, and AI behavior depends heavily on context. Furthermore, AI workflows are dynamic, with integrations and capabilities evolving rapidly, rendering periodic security reviews insufficient. Effective security requires treating the entire AI workflow as the protection boundary, gaining visibility into all AI tools in use—including shadow AI services—and enforcing strict access controls and output monitoring. Middleware guardrails should inspect AI outputs before they leave the environment, and OAuth tokens must be scoped to minimum necessary permissions. User education on the risks of unvetted browser extensions and prompt sources is also critical. Emerging dynamic SaaS security platforms, such as Reco, offer real-time monitoring and anomaly detection tailored to AI workflows, helping organizations maintain control without hindering productivity.

For European organizations, the impact of these workflow-based AI threats is significant. Sensitive corporate data, including confidential documents and customer information, can be exfiltrated through malicious AI-integrated browser extensions or manipulated AI outputs. This risks violating GDPR and other data protection regulations, potentially leading to legal penalties and reputational damage. The manipulation of AI workflows to execute unauthorized actions or malware can disrupt business operations, compromise system integrity, and lead to financial losses. Organizations heavily reliant on AI copilots for document summarization, email drafting, or customer interactions face increased exposure. The stealthy nature of these attacks—blending into normal service-to-service traffic and natural language inputs—makes detection difficult with traditional security tools. Additionally, the dynamic and evolving nature of AI workflows complicates maintaining effective security postures. The risk extends beyond data confidentiality to include integrity and availability of business processes automated or assisted by AI. European enterprises adopting AI rapidly without comprehensive workflow security controls may inadvertently introduce new attack vectors, increasing their cyber risk profile.

European organizations should adopt a holistic approach to securing AI workflows rather than focusing solely on AI model protection. First, conduct thorough discovery and inventory of all AI tools and integrations in use, including official platforms like Microsoft 365 Copilot and unsanctioned browser extensions or shadow AI services. Implement strict access controls by applying the principle of least privilege to AI agents and services, ensuring OAuth tokens and API keys are scoped narrowly. Deploy middleware guardrails that inspect AI outputs for sensitive data before allowing external transmission, preventing inadvertent data leaks. Monitor AI behavior and data access patterns continuously to detect anomalies such as unusual data queries or unexpected output content. Educate employees about the risks of installing unvetted browser extensions and copying prompts from unknown sources, emphasizing secure AI usage practices. Vet third-party AI plugins and extensions rigorously before deployment. Consider adopting dynamic SaaS security platforms specialized in AI workflow monitoring and anomaly detection to scale protection efforts. Regularly update security policies to reflect the evolving AI integration landscape and conduct frequent audits rather than relying on periodic reviews. Finally, integrate AI workflow security into broader cybersecurity frameworks and incident response plans to ensure rapid detection and mitigation of emerging threats.