The State of AI 2026: Security Insights CISOs Need to Know

TL;DR

AvePoint’s State of AI 2026 Report found that 89.5% of organizations suffered a generative AI (GenAI) breach and 88.4% suffered an AI agent breach in the past year, even though confidence in preventing unauthorized access also rose. Closing this gap requires governance frameworks and AI agent management platforms (AMP).

Key Takeaways

• Breach rates are climbing faster than confidence. 89.5% of organizations experienced a GenAI-related breach in the past 12 months (up from 75.1% in 2025), and 88.4% reported at least one AI agent-related breach.
• Confidence does not equal control. More than 80% of organizations say they are “very” or “extremely” confident in preventing unauthorized data access, yet 72% of “very confident” respondents and 62% of “extremely confident” respondents were breached anyway.
• Shadow AI is expanding faster than visibility. 21.1% of organizations do not know whether unsanctioned tools are being used to build AI agents, while both sanctioned and unsanctioned access to agent-creation tools grew year over year (YOY).
• Deployment delays are the norm, not the exception. 86% of organizations delayed AI agent rollouts and 86.9% delayed generative AI rollouts due to data security and data management issues, each by nearly six months on average.
• Governance, not more models, closes the gap. An AMP, paired with an assess-clean-maintain data governance framework, gives CISOs the control layer needed to match rising AI adoption with rising AI trust.

AI adoption has outpaced AI governance in almost every enterprise. As GenAI tools and autonomous agents move from pilot projects into daily workflows, security leaders face a widening gap between how confident they feel and how often incidents occur. AvePoint’s The State of AI 2026: Scaling Trust, Control, and Readiness in the Agentic Era report quantifies that gap. This article walks chief information security officers (CISOs) through the data and what to do about it.

What Is the Confidence-Incident Paradox, and How Many Organizations Have Been Breached?

The confidence-incident paradox describes a widening disconnect between how secure organizations believe their AI environment is and how often that environment is compromised. According to The State of AI 2026 report, GenAI-related breaches rose to 89.5% of organizations in the past 12 months, up from 75.1% in 2025. AI agent-related breaches were measured as a standalone metric for the first time in 2026, and 88.4% of organizations reported at least one such incident.

Table 1: AI Breach Rates, 2025 vs. 2026

What makes this paradoxical is that confidence rose alongside breach rates rather than falling. More than 80% of organizations now say they are “very” or “extremely” confident in their ability to prevent unauthorized data access, up from 75.5% in 2025. Yet among the “very confident” group, 72% still experienced an AI-related unauthorized access incident in the past year. Among the highest-confidence group, those who described themselves as “extremely confident,” 62% still had an incident.

That combination, rising confidence and rising breach rates in the same population, tells us confidence is currently based on intent and policy rather than verified control. A governance framework that only measures whether a policy exists, without measuring whether AI systems are actually monitored, audited, and constrained in practice, will keep producing this exact pattern. Closing this gap is the central AI-ready data governance framework challenge for CISOs heading into 2026.

What Are the Most Common Types of AI Agent Security Breaches CISOs Should Watch For?

The most common AI agent security breach in 2026 involved sensitive or confidential data being improperly exposed or retained by an agent, affecting 50.1% of organizations, closely followed by agents manipulated through malicious or untrusted inputs at 49.6%. These two categories account for roughly half of all reported AI agent incidents, but they are far from the only risks CISOs need to track.

Table 2: AI Agent Breach Types (% of Organizations Experiencing, Past 12 Months)

How Often Is Sensitive Data Exposed by AI Agents?

Data exposure is the single most common AI agent breach type, at 50.1% of organizations. Agents often have broader read access than the people who built them realize, and once an agent can retrieve a file, it can also summarize, forward, or act on its contents without the access controls a human user would normally trigger. This is precisely the exposure surface that AvePoint’s data security and governance capabilities are built to close, by mapping sensitivity against real-time permissions before an agent is ever granted access

How Common Is Prompt Injection Against AI Agents?

Manipulation through malicious or untrusted inputs, including prompt injection, malicious documents or emails, unverified external data, and retrieval-augmented generation (RAG) leakage, affected 49.6% of organizations. This category is as prevalent as direct data exposure, and it is particularly dangerous because it does not require an attacker to breach a network perimeter, only a manipulated input placed in front of an agent that already has legitimate access. GenAI tools showed a related pattern: 39.6% of organizations reported a prompt injection attack that bypassed security guardrails on a generative AI assistant, and 38.3% reported an AI chatbot exposing data to another AI chatbot.

How Widespread Is Shadow Agent Risk?

Unauthorized or shadow AI identities, agents created or misused outside of sanctioned processes, were reported by 30.1% of organizations. Combined with 34.1% experiencing unauthorized autonomous actions and 21.9% experiencing upstream supply chain compromise, it is clear that agent identity and provenance have become as important to secure as the data an agent touches. Insufficient logging and auditability, while the least common breach type at 7.1%, is arguably the most consequential, because it determines whether any of the other six categories can even be investigated after the fact (refer to Table 2: AI Agent Breach Types).

GenAI assistants carried their own integrity risks in parallel: 

• 40.1% of organizations reported an AI assistant generating content with integrity risks (inaccurate, untrustworthy, or altered).
• 33.6% reported insecure code generated by an AI coding assistant.
• 30.5% reported an AI assistant providing harmful or concerning advice.
• 20.4% detected non-corporate-approved GenAI assistants in their environment.
• 18.5% reported an AI assistant leaking sensitive data it should not have disclosed.
• 4.1% reported an unauthorized party gaining access to GenAI tools outright.

Why Is Data Security and Privacy the Top AI Security Concern in 2026?

Data security and privacy is the top AI security concern in 2026 because it is the one risk category that scores highest for both GenAI and AI agents, at 76.0% for each, and because it is rising, not falling, year over year (YOY). For GenAI, concern about data security and privacy climbed from 70.5% in 2025 to 76.0% in 2026, even as organizations gained a full additional year of hands-on deployment experience.

Table 3: Top AI Security Concerns, Generative AI vs. AI Agents (2026)

Every single concern in both lists rose YOY, which tells us this is not a temporary adjustment period but a structural shift in how security leaders view AI risk. Notably, ensuring sufficient human-in-the-loop (HITL) controls ranks third among AI agent concerns at 70.3%, ahead of regulatory compliance, reflecting that CISOs increasingly see oversight mechanisms, not just policy documents, as the actual control point.

This concern profile lines up closely with independent research: The World Economic Forum’s Global Cybersecurity Outlook 2026 found that 87% of cybersecurity leaders identified AI as the fastest-growing cyber risk in 2025, and that the share of organizations formally assessing the security of AI tools before deployment grew from 37% to 64% in the same period.

What Is Shadow AI, and Why Is Observability Collapsing?

Shadow AI refers to AI tools, assistants, or agents operating in an enterprise environment without security, IT, or compliance oversight, and observability into it is collapsing because organizations increasingly do not know it exists at all. According to The State of AI 2026 Report, 21.1% of organizations say they do not know whether unsanctioned tools are being used to create AI agents, a higher blind spot than the equivalent GenAI figure. For GenAI specifically, the share of organizations that admitted they do not know if employees are using unsanctioned tools rose from 6.3% in 2025 to 17.6% in 2026, nearly tripling in a single year.

This is not simply a case of usage staying flat while awareness declines. Both sanctioned and unsanctioned access are growing simultaneously. Access to sanctioned AI agent creation tools grew from 26.8% to 37.8% YOY, while access to unsanctioned agent creation tools grew from 26.3% to 33.8% over the same period.

Generative AI shows the same pattern: Sanctioned access grew from 29.0% to 40.3%, and unsanctioned access grew from 27.6% to 34.2%. Organizations are extending official access to AI tools while unofficial access grows at the same rate right alongside it, and their ability to tell the difference is deteriorating.

This trend is consistent with what the wider industry is seeing. The ISC2 2025 Cybersecurity Workforce Study found that 70% of security professionals at smaller organizations had experienced an AI-related security event, underscoring that shrinking visibility into AI usage is not confined to large, complex enterprises. Closing this blind spot requires continuous discovery of AI agents and assistants across every sanctioned and unmanaged platform, because a governance policy cannot protect data it does not know is being accessed.

Why Are Enterprises Delaying AI Deployments?

Enterprises are delaying AI deployments primarily because of data security and data management issues, which caused 86% of organizations to delay AI agent rollouts and 86.9% to delay GenAI rollouts. These are not brief pauses: the average AI agent deployment delay was 5.92 months, and the average generative AI delay was 5.88 months, up slightly from 5.76 months in 2025.

Table 4: Top Reasons for AI Agent Deployment Delays (Highly + Extremely Impactful, Combined)

Data security concerns rank as the single most impactful delay reason, ahead of hallucination risk, cost, and change management combined, which tells us CISOs and data leaders are correctly treating security readiness as a prerequisite rather than a parallel workstream.

Deploying an AI agent before its data access is scoped and its actions are auditable simply moves the risk downstream rather than removing it. This is also reflected in the pressure organizations feel to show results quickly once deployment does happen: 86.3% of organizations want to see AI ROI within 12 months, up from 81.9% in 2025, and only 13.7% are willing to wait longer, down from 18.1%. Delayed deployment paired with compressed ROI expectations puts real pressure on CISOs to secure AI environments faster without cutting corners on governance.

How Is AI-Generated Data Reshaping Enterprise Data Governance and Risk?

AI-generated data is reshaping enterprise governance because it is growing faster than most organizations’ ability to classify, label, or clean it. AvePoint’s The State of AI 2026 report found that 35.5% of enterprise data is currently AI-generated, a figure projected to reach 42.1% within the next 12 months. That growth is layered on top of already substantial data volumes: 84.1% of organizations manage at least 1PB of data, with average data growth of 31.8% over the past 12 months, expected to rise to 39.1% over the next year12 months.

Compounding the problem, that data is aging in place rather than being cleaned up. According to the report, 78.1% of organizations say at least half of their data is five years old or older, up from 70.7% in 2025. Old, unclassified, and increasingly AI-generated data creates exactly the conditions where redundant, outdated, or trivial (ROT) content ends up feeding both GenAI outputs and the agents that act on them.

As AI becomes a major producer of enterprise content, data readiness becomes AI readiness. Dana Simberkoff, Chief Risk, Privacy & Information Security Officer at AvePoint, says: 

“Training and reasoning on ROT content increases the probability of irrelevant output and poor decision-making. When AI agents begin acting autonomously on that output, governance failures can propagate operationally across systems, workflows, and decisions at machine speed.”

A stale file sitting in an unmonitored SharePoint site is a static risk. The same file, summarized, acted on, and forwarded by an autonomous agent, is a dynamic one that compounds with every workflow it touches. This is why AvePoint’s information lifecycle management solutions, including defensible deletion of ROT content and automated classification at the point of creation, have become foundational AI security infrastructure rather than a back-office compliance task.

What Is an AI Agent Management Platform, and Why Do CISOs Need One?

An AI agent management platform (AMP) is a governance layer that discovers, monitors, and controls AI agents across an enterprise’s cloud and SaaS environments. CISOs need AMP because agent adoption is accelerating far faster than manual oversight can scale. Employees already rely on AI agents daily or weekly for work tasks at a rate of 46.9%, and the share of work processes incorporating AI agents has jumped from 26.6% a year ago to 39.1% today, with 54.8% anticipated within the next 12 months. AI agents are now rated the most important emerging technology by respondents, at 84.7% importance, but they also carry the widest importance-maturity gap of any technology category, with maturity lagging at just 72.3%.

Analyst research highlights the urgency behind this shift. Gartner projects that by 2027, 75% of enterprises will consider their AI agent monitoring methodology their most important governance tool, up from just 1% today. Enterprise investment in AMP technologies will exceed $15 billion by 2029, and the average Fortune 500 enterprise will manage 150,000 AI agents by 2028. At that scale, agent governance cannot be a manual, spreadsheet-driven process. It has to be a platform capability.

As John Peluso, Chief Technology Officer at AvePoint, puts it: 

"This is why trust in AI is increasingly a control problem rather than a model problem. As AI becomes embedded in workflows, the key question is not only whether users are trained or whether a policy exists, but whether organizations can reliably control what AI can access, audit what AI did, and remediate outcomes when something goes wrong.”

Key Capabilities of an AI Agent Management Platform

AvePoint AgentPulse addresses this control problem through a five-step lifecycle:

This lifecycle approach directly closes the observability gap described earlier: an agent cannot become “shadow” if it is discovered and classified the moment it is created, and it cannot linger indefinitely if renewal and retirement are built into its lifecycle by default.

Where Should CISOs Prioritize AI Security Investment in 2026?

CISOs should prioritize governance tooling first, because it is the single highest-ranked investment category for both GenAI and AI agents heading into 2026. According to The State of AI 2026 report, 62.4% of organizations plan to increase investment in governance tools that monitor agent actions for policy alignment, and 60.8% plan to increase investment in governance tools that assess GenAI output accuracy and alignment.

Table 5: Where CISOs Plan to Increase AI Security Investment (Next 12 Months)

Investment intent lines up with the mitigation steps organizations are already taking. For AI agents:

• 54.8% of organizations have added HITL controls
• 51.6% have provided employee training
• 40.4% have deployed third-party governance tools
• 15.5% have deployed third-party data protection tools.

Only 4.5% have taken no action at all, meaning 95.5% have implemented at least one mitigation step. Some organizations have gone further: 37.9% have canceled an AI agent rollout entirely, and 29.3% have delayed one.

GenAI mitigation shows a similar, and in some cases sharper, trend, with 66.9% of organizations providing employee training and 55.6% deploying third-party governance tools.

Cancellations rose from 31.7% to 40.7% YOY, a 28.1% relative increase, and delays rose from 22.1% to 34.5%, a 56.5% relative increase. Meanwhile, the share of organizations doing nothing in response to generative AI risk fell from 8.3% to 2.5%. Taken together, this is a market that is not slowing AI adoption so much as front-loading governance investment before scaling further, which is exactly the sequencing a mature AI governance framework should follow.

What Should a CISO’s AI-Ready Data Governance Framework Include in 2026?

A CISO’s AI-ready data governance framework in 2026 should include three continuous phases: assess, clean, and maintain, applied to data before, during, and after AI and agent deployment. This structure keeps pace with data growing 31.8% annually and AI-generated content already representing 35.5% of the enterprise data estate, conditions that point-in-time audits cannot match.

Assess: Discover and Analyze Risk

Assessment starts with knowing what data exists and where the risk actually sits. That means discovering sensitive data across collaboration platforms, overlaying sensitivity against real-time permissions to identify high-exposure content, identifying “shadow users” who have file-level access without formal workspace membership, and inventorying every sharing link, anonymous, external, or organization-wide, that could expose that data to an AI assistant or agent. This is also where confidence should be tested against outcomes rather than assumed.

The State of AI 2026 Report finds that confidence in:

• preventing unauthorized access reaches 82.6% (very or extremely confident combined).
• enforcing consistent guardrails across multiple clouds reaches 78.0.
• detecting shadow AI agents reaches 76.5%. 
• recognizing and mitigating bias declined to 71.0% YOY.
• stopping hallucinations dropped to 70.7% YOY.

Clean: Remediate Exposure

Once risk is mapped, remediation has to be automated rather than manual to keep pace with data growth. That includes automated policy enforcement that detects and corrects oversharing on a recurring basis, expiring sharing links after a set retention window, applying sensitivity labels and encryption based on the content found inside a file, and restricting sensitive content from being surfaced by AI search or Copilot without changing the underlying site permissions. This phase is where the 50.1% of organizations experiencing agent-related data exposure and the 49.6% experiencing input manipulation stand the best chance of seeing those numbers fall.

Maintain: Govern the Lifecycle

Maintenance is what prevents yesterday’s clean environment from becoming tomorrow’s breach statistic. That means managing the shelf life of workspaces and agents to prevent sprawl, requiring business owners to attest to the purpose of the workspaces and agents they own, scheduling recurring reviews of membership and access, and using a governed service catalog so that new AI agents, Teams, or groups are checked against policy before they are created rather than after. 

As Dr. Tianyi Jiang (TJ), CEO and Co-Founder of AvePoint, explains:

“AI agents completely rewrite the enterprise risk equation. Because agents can execute autonomously, the risk shifts from flawed outputs to flawed actions... In agent-driven environments, trust depends on control.”

This maturity-first approach is echoed in independent research as well. The Cloud Security Alliance’s State of AI Security and Governance research treats governance maturity itself as a multiplier on security outcomes, meaning organizations with structured, continuous governance processes see materially better security results than those relying on point-in-time controls, regardless of how sophisticated their underlying AI models are.

What Should CISOs Do Next?

CISOs should treat 2026 as the year AI governance shifts from policy to platform. The data is unambiguous: Breach rates are rising in lockstep with confidence, shadow AI is expanding faster than visibility into it, and deployment delays are already costing organizations nearly six months per rollout on data security grounds alone. None of that improves by waiting for AI models to become inherently safer. It improves when organizations can reliably answer three questions for every agent and every generative AI tool in their environment: what can it access, what did it do, and can we remediate it when something goes wrong.

That is the sequencing we recommend: Assess your current data and agent exposure, clean up the ROT content and oversharing that AI will otherwise amplify, and maintain that posture continuously through an AMP rather than periodic audits. The AvePoint Confidence Platform and AgentPulse are built around exactly this sequence, unifying security, governance, and resilience into a single control plane rather than a collection of disconnected point tools. For a deeper look at building this out step by step, see our guide to enterprise AI governance frameworks.

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FAQ

What is the State of AI 2026 Report?

AvePoint’s annual global study of 750 respondents, conducted by Osterman Research, tracks enterprise AI adoption, security, and governance trends, following prior editions in 2025 and 2024.

How many organizations experienced an AI security breach in 2026?

More than 89% of organizations experienced a generative AI-related breach, and 88.4% experienced an AI agent-related breach, both within the past 12 months.

What is an AI agent management platform (AMP)?

An AMP is a governance platform that discovers, classifies, monitors, and retires AI agents across an enterprise’s cloud environments, giving CISOs continuous control instead of one-time audits.

What is shadow AI?

Shadow AI refers to AI tools, assistants, or agents used without security or IT oversight. In 2026, 21.1% of organizations did not know if unsanctioned agent-creation tools were in use.

How can CISOs close the AI confidence-incident gap?

By replacing assumed confidence with measured control: assessing data and agent exposure, cleaning up oversharing and stale data, and maintaining governance continuously through an AMP.