Most surveyed firms have AI on their roadmaps, but lack controls to govern data security.

Key takeaways

• Universal AI adoption, limited readiness: 100% of surveyed organizations reported plans to deploy agentic AI systems by 2026, but a majority reported lacking core governance and containment controls.
• Purpose and control gaps: 63% of respondents said they cannot enforce purpose limitations on AI systems, and 60% reported they cannot quickly shut down or contain misbehaving AI agents.
• Insufficient system isolation: 55% of organizations said they cannot isolate AI systems from broader enterprise networks, increasing the risk of unintended data access.
• Weak data governance foundations: 61% reported they cannot tag or classify data used by AI systems, and 72% said they do not maintain a software bill of materials for AI models.
• Limited monitoring and accountability: 60% of respondents said they lack anomaly detection for AI activity, while 33% reported fragmented or missing audit trails for AI systems.
• Cross-border visibility gaps: 29% cited cross-border AI data transfers as a risk, yet only 36% reported visibility into where AI data is processed, trained, or inferred.

The 2026 Data Security and Compliance Risk Forecast Report by Kiteworks finds that while every organization surveyed intends to deploy agentic artificial intelligence (AI) systems by 2026, a majority lack the governance and containment controls needed to secure those systems and the sensitive data they access.

The report states that although AI adoption is universal among respondents, most organizations cannot enforce basic controls such as purpose limitations on AI agents, rapid shutdown of misbehaving agents, or isolation of AI systems from broader networks. Specifically, 63% cannot enforce purpose limitations, 60% cannot terminate misbehaving agents quickly, and 55% cannot isolate AI systems from wider network access.

According to Kiteworks, 2026 will mark the transition of AI data security from an “emerging concern” to an “operational reality,” driven by gaps between monitoring capability and the ability to contain risky AI behavior. The report identifies a “governance-containment gap” as a central challenge for enterprise data security ahead of the coming year.

The forecast outlines 15 predictions for how AI and data security risks will evolve. These include the rise of centralized AI gateways as control planes, the dominance of data security posture management as a baseline security requirement, and the increasing regulatory focus on training-data controls and audit trails.

The findings show that key governance capabilities are widely missing: 61% of organizations cannot enforce tagging of data used by AI, 72% have no software bill of materials (SBOM) for AI models, 60% lack anomaly detection for AI activity, and 33% have fragmented or absent audit trails.

The report also highlights industry-specific vulnerabilities. Government organizations, despite handling sensitive citizen data, showed high rates of missing purpose-binding and kill-switch controls. Healthcare respondents demonstrated significant gaps in incident response and limited AI anomaly detection. Manufacturing and other sectors reported visibility blind spots in complex supply chains.

Tim Freestone, chief strategy officer at Kiteworks, said the survey results show that AI governance is lagging behind deployment plans: “Every organization surveyed has agentic AI on their roadmap, yet most lack the controls to govern it.”