CISO Pulse Check

AI: The New Superpower and The New Super-Risk

AI-related risks have grown exponentially as AI adoption accelerates faster than CISOs can keep up. 2026 demands a new AI Governance Stack before the next wave of AI breaches hits. 

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icon state of awareness

The current state of awareness about the rising risks of AI usage

icon ai risk today

How CISOs in U.S. organizations are mitigating AI risks today

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Budgeting priorities to mitigate AI risks in 2026

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icon state of awareness

The current state of awareness about the rising risks of AI usage

icon ai risk today

How CISOs in U.S. organizations are mitigating AI risks today

icon budget priorities

Budgeting priorities to mitigate AI risks in 2026

CISOs need AI Governance
 built for the speed and variability of AI adoption.

The CISO Pulse Check report reveals a clear but uncomfortable truth about AI risk management in U.S. organizations: awareness is high, but operational readiness is uneven. 

Most organizations now recognize AI as a material security and compliance concern.

Nearly 70% of respondents report that they are actively following AI-related regulations or standards and preparing to comply, and more than half (53%) have elevated AI to a dedicated risk category, rather than incorporating it into broader third-party or data security programs.

Are you aware of AI-related regulations or standards?

active ai related awareness

Unfortunately, awareness has not translated into consistent control execution.

Over 30% of organizations report experiencing a major AI-related security incident in the past 12 months, and the most common incident patterns are precisely the ones that thrive in weak governance environments: shadow AI usage, data leakage/model inversion, API abuse, and data poisoning.

These are not “future” risks. They are already creeping into day-to-day operations, often faster than policies and processes can keep pace.

Are you aware of AI-related regulations or standards?

much more prepared
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The most significant gaps in AI Governance aren’t about intent. They’re about enforcement and speed.

Nearly 39% of organizations have an AI usage policy that exists but is not consistently enforced, making it challenging to reduce shadow usage, demonstrate compliance, or reliably influence employee behavior.

Even more concerning, only 21% report having controls in place to prevent sensitive data from being uploaded to publicly available AI platforms. In this area, a single user action can result in the irreversible exposure of IP, confidential data, or regulated information.

How mature is your organization’s AI usage policy?

implemented enforced

This execution gap is amplified by a lack of automation. While many organizations describe their programs as semi-automated, 27% still manage AI risk mostly manually, and only 17% report mitigating new AI-related risks using automated/technical controls.

Are you aware of AI-related regulations or standards?

semi automated standards
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Result

As a result, responsiveness lags.

Two in three organizations take longer than a week to implement controls or policy changes after identifying new AI risks. In a landscape where tools, model behaviors, and attack techniques evolve in days, not quarters, this delay becomes a structural weakness.

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Looking ahead to 2026, organizations are investing in AI risk mitigation.

69% have already allocated a budget to manage AI risks next year, and another 17% plan to do so in the next cycle.

Only 25% rate their AI governance program as “advanced,” meaning that most organizations are still building foundational capabilities, such as implementing technical controls for AI usage, conducting recurring AI risk assessments, providing workforce training, formalizing policies, and increasing automation.

Do you have budgets for 2026 that can help you
manage AI-related risks?

organizations are investing in AI risk mitigation

The path forward is becoming clearer. AI risk management will not scale as a set of documents and periodic reviews.

Without a system of record and integrated automation, audits turn into scavenger hunts, evidence gets duplicated or lost, controls are retested unnecessarily, and teams remain trapped in reactive “Groundhog Day” cycles. To move from awareness to resilience, CISOs need governance that is continuous, measurable, and enforceable.

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Frequently Asked Questions

AI governance refers to the policies, controls, processes, and accountability structures that ensure AI systems are used safely, securely, and compliantly. It matters now because AI adoption has outpaced traditional governance models, creating new risks around data exposure, decision integrity, and regulatory compliance that CISOs can no longer manage informally.

An AI governance stack is the collection of tools, workflows, and systems an organization uses to manage AI risk, enforce policies, track controls, and produce audit-ready evidence. A weak or fragmented AI governance stack forces teams to rely on manual processes, slows risk response, and makes AI compliance difficult to prove at scale.

Most CISOs understand AI risk and actively track it, but their existing governance infrastructure was designed for slower, more predictable risks. AI evolves quickly, cuts across multiple domains, and requires continuous monitoring, leading to risks that many legacy compliance stacks cannot support efficiently.

AI risk spans multiple areas simultaneously, including data security, third-party risk, software development, and automated decision-making. Unlike traditional risks, AI risk can emerge from normal employee behavior and change rapidly as tools and models evolve, making static assessments insufficient.

High AI risk maturity includes consistent policy enforcement, technical controls to prevent sensitive data exposure, automated risk monitoring, clear ownership, and a centralized system of record. Low maturity often involves ad hoc reviews, inconsistent enforcement, and heavy reliance on spreadsheets and manual evidence collection.

AI compliance requires organizations to demonstrate not just that policies exist, but that controls are continuously enforced and risks are actively managed. Many compliance programs are built around periodic audits, whereas AI demands real-time visibility and faster remediation cycles.

Common AI risks include shadow AI usage, sensitive data uploads to public AI platforms, exposed or abused AI APIs, and integrity issues caused by unvalidated or manipulated data inputs. These risks often arise from gaps in governance rather than malicious intent.

Organizations recognize that AI risk will only increase as adoption grows and regulations mature. Many CISOs now realize that without modernizing their AI governance stack through automation, integrated workflows, and better evidence management, they will struggle to scale securely and compliantly.

CISOs can benchmark AI risk maturity by assessing how consistently AI policies are enforced, how quickly new AI risks are mitigated, whether controls are automated, and how easily evidence can be produced for audits or regulators. Comparing these capabilities against peers provides a clear picture of readiness.

The highest-impact priorities are gaining visibility into AI usage, enforcing policies through technical controls, preventing sensitive data exposure, and reducing manual governance work through automation. Strengthening the AI governance stack creates the foundation for sustainable AI risk management and compliance.