AI Sprawl: The Next Enterprise Security Challenge

Executive Summary

For more than a decade, security teams have battled SaaS sprawl—the unchecked growth of cloud applications across the enterprise. The challenge was straightforward: discover applications, understand who was using them, and reduce unnecessary risk.

That challenge has fundamentally changed.

Today, organizations are no longer managing only SaaS applications. They are managing AI embedded inside SaaS platforms, browser-based AI assistants, AI copilots, autonomous AI agents, OAuth-connected AI services, and an ever-growing network of machine identities and trusted integrations.

This evolution has created a new governance problem: AI Sprawl.

Unlike SaaS sprawl, AI Sprawl is driven by identities and access relationships rather than applications alone. Every AI feature, agent, and integration introduces another pathway to sensitive data, another identity requiring governance, and another trusted relationship that expands the enterprise attack surface.

Grip Security believes AI Sprawl will become the defining enterprise security challenge of this decade, just as SaaS sprawl defined the previous one.

Organizations that continue governing AI as if it were simply another application category will struggle to maintain visibility, control, and risk management as AI adoption accelerates.

Key Takeaways

• AI Sprawl is the evolution of traditional SaaS sprawl.
• Modern enterprises are managing AI agents, embedded AI, browser AI, OAuth-connected services, and non-human identities—not just applications.
• AI dramatically expands identity relationships and trusted access across SaaS environments.
• Traditional governance approaches cannot keep pace with continuously changing AI ecosystems.
• AI security increasingly depends on identity visibility, continuous governance, and automated remediation rather than periodic application reviews.

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What Is AI Sprawl?

AI Sprawl is the rapid, decentralized expansion of AI capabilities, AI agents, embedded AI features, browser-based AI tools, and connected AI services across an organization, creating an increasingly complex web of identities, permissions, and trust relationships that traditional governance cannot effectively manage.

Unlike traditional SaaS sprawl—which focused on discovering unauthorized applications—AI Sprawl centers on understanding:

• Which AI capabilities exist
• Which identities they operate under
• What data they can access
• Which applications they connect to
• What actions they are authorized to perform

As AI becomes embedded into nearly every business application, organizations often cannot distinguish where SaaS ends and AI begins.

That distinction matters less than understanding how access is expanding.

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Why SaaS Sprawl Became AI Sprawl

Ten years ago, the challenge was simple:

Employees signed up for cloud applications faster than IT could manage them.

Today, AI has fundamentally changed that equation.

Applications no longer exist as isolated software platforms. Instead, they continuously gain new AI capabilities that extend functionality, automate workflows, and connect previously independent systems.

Several trends are driving this shift.

Embedded AI Everywhere

According to Grip's Mid-Year AI Exposure Update, 54% of enterprise applications now contain AI functionality.

Many organizations have not intentionally adopted AI in these applications.

Instead, AI capabilities are arriving automatically through product updates.

The result is an expanding AI footprint without corresponding governance.

Browser-Based AI

Employees increasingly rely on browser-based AI assistants for:

• Writing
• Coding
• Research
• Data analysis
• Document creation
• Customer communications

These tools frequently interact with corporate data despite existing outside traditional application inventories.

AI Copilots

AI copilots are now embedded across productivity suites, collaboration platforms, CRM systems, development environments, customer support platforms, and security tools.

Each copilot inherits permissions from existing identities while introducing new opportunities for unintended data exposure.

AI Agents

Perhaps the largest shift is the emergence of AI agents.

Unlike copilots that assist users, AI agents increasingly perform work autonomously.

They:

• Retrieve data
• Execute workflows
• Connect applications
• Initiate business processes
• Make operational decisions

Every agent effectively becomes another digital worker requiring identity governance.

Grip's research introduced the Rule of 17, finding that organizations now average one AI agent for every 17 identities.

That number is expected to grow rapidly.

SaaS Integrations

Modern AI rarely operates in isolation.

AI systems increasingly connect through OAuth to productivity platforms, collaboration tools, CRM systems, developer environments, IT service management platforms, cloud storage services, and hundreds of additional SaaS applications.

These trusted relationships dramatically expand the enterprise attack surface.

The result is not simply more software.

It is exponentially more interconnected software.

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Why AI Sprawl Creates New Security Risks

AI introduces risks that traditional SaaS governance was never designed to address.

Identity Sprawl

Every AI agent represents another identity.

Every integration introduces another trusted relationship.

Every automation creates another pathway to sensitive information.

As AI deployments multiply, identity inventories become significantly more complex than application inventories.

Organizations must now govern:

• Human identities
• Service accounts
• AI agents
• API identities
• Machine identities
• Automated workflows

Understanding applications alone no longer provides sufficient visibility.

OAuth Permissions

Many AI platforms rely on OAuth authorization.

While OAuth improves user experience, it also creates persistent trust relationships between applications.

These permissions often allow AI systems to:

• Read email
• Access documents
• Search file repositories
• Retrieve CRM data
• Connect collaboration platforms

Without continuous governance, organizations accumulate excessive permissions over time.

Non-Human Identities

AI agents increasingly operate independently from users.

They authenticate.

They retrieve data.

They perform actions.

They maintain persistent access.

These non-human identities require governance equivalent to privileged human users.

Without visibility, organizations cannot accurately answer:

• Which AI agents exist?
• Who deployed them?
• What can they access?
• Are they still needed?

Excessive Permissions

AI systems often inherit broad permissions simply because users already possess them.

The consequence is an expanding set of privileged identities operating continuously rather than occasionally.

Least-privilege principles become significantly harder to enforce.

SaaS-to-SaaS Trust

Traditional security focused primarily on users accessing applications.

AI changes this model.

Applications increasingly communicate directly with one another through trusted integrations.

Compromise of one platform can create cascading exposure across many others.

Recent SaaS breaches have demonstrated how attackers increasingly exploit trusted relationships rather than individual application vulnerabilities.

AI accelerates this trend.

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AI Agents Expand the Attack Surface

The more capable AI becomes, the more authority organizations delegate to software.

Agents now:

• Modify records
• Generate content
• Approve workflows
• Trigger automation
• Access confidential information

Every new capability expands potential attack paths.

Security teams must understand not only where AI exists—but what AI is allowed to do.

Grip's Data Shows the Scale of AI Sprawl

Grip's latest research illustrates how quickly AI exposure is expanding across enterprise environments:

• AI-related attacks increased approximately 490% year over year, according to the Grip Security 2026 SaaS + AI Security Report, based on 2025 enterprise telemetry.
• Approximately 80% of AI-related incidents involved sensitive or regulated data, underscoring the business impact of inadequate AI governance.
• Users are exposed to an average of 33.5 AI-enabled applications.
• The average Grip customer now uses 1,017 AI-enabled applications.

These findings suggest that AI Sprawl is not a future concern—it is already reshaping enterprise risk today.

The evolution is clear.

Organizations are no longer securing software alone.

They are securing ecosystems of identities, AI capabilities, and continuously changing trust relationships.

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Why Traditional Governance Breaks Down

Many governance programs were built around assumptions that no longer hold true.

Point-in-Time Reviews

Annual or quarterly reviews cannot keep pace with AI deployments that change weekly—or even daily.

New AI capabilities appear continuously through software updates.

Application Inventories

Knowing an application exists tells security teams very little about:

• Embedded AI features
• Connected AI services
• Autonomous agents
• OAuth relationships

Application inventories have become necessary—but insufficient.

Manual Governance

Manual reviews cannot realistically evaluate:

• Thousands of SaaS applications
• Millions of permissions
• Hundreds of AI agents
• Constantly evolving integrations

Governance must become continuous.

Otherwise, organizations will always operate behind their actual AI exposure.

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What Security Leaders Should Do Next

AI Sprawl cannot be solved through application discovery alone.

Security leaders should instead focus on governing identities, access, and trust relationships across the AI ecosystem.

Practical priorities include:

Continuously discover AI across SaaS

Identify embedded AI features, AI agents, browser AI usage, and connected AI services—not just standalone AI applications.

Build a complete identity inventory

Include human users, service accounts, machine identities, and AI agents within a single governance model.

Govern OAuth continuously

Review delegated permissions regularly, remove unused integrations, and enforce least-privilege access.

Monitor AI-enabled applications

Recognize that AI functionality increasingly appears through existing SaaS platforms rather than new software purchases.

Automate remediation

As AI adoption scales, manual governance will not keep pace. Organizations should automate remediation wherever possible, including revoking excessive permissions, disabling unused integrations, and enforcing identity-based security policies.

The organizations that successfully manage AI Sprawl will not necessarily deploy less AI.

They will simply govern it more effectively.

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FAQ

What is AI Sprawl?

AI Sprawl is the rapid expansion of AI capabilities, AI agents, embedded AI features, and connected AI services across an organization, resulting in increasingly complex identity, permission, and trust relationships that require continuous governance.

How is AI Sprawl different from SaaS sprawl?

SaaS sprawl focused on managing cloud applications. AI Sprawl focuses on governing AI capabilities, non-human identities, OAuth relationships, embedded AI, and autonomous agents operating across those applications.

Why is AI increasing SaaS risk?

AI introduces additional identities, delegated permissions, and trusted integrations that expand access to sensitive data. As AI becomes embedded across SaaS platforms, organizations inherit more complex security relationships that traditional governance models were not designed to manage.

How can organizations reduce AI Sprawl?

Organizations should continuously discover AI across their SaaS environment, inventory both human and non-human identities, govern OAuth permissions, monitor AI-enabled applications, and automate remediation of excessive access and risky integrations.

Why is AI Sprawl becoming a security problem?

AI Sprawl expands the number of AI agents, identities, OAuth connections, and trusted integrations operating across SaaS environments. Without continuous visibility and governance, organizations may struggle to understand what AI systems exist, what they can access, and how they interact with sensitive data.

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Conclusion

SaaS sprawl changed enterprise IT by proving that applications could grow faster than governance.

AI Sprawl changes the equation again.

The challenge is no longer simply knowing which applications employees use. It is understanding the expanding network of AI capabilities, autonomous agents, identities, permissions, and trust relationships operating across those applications.

As AI adoption accelerates, governance strategies must evolve from application-centric thinking to identity-first security. Organizations that embrace continuous visibility, identity governance, and automated remediation will be better positioned to harness AI's benefits while reducing its risks.

AI Sprawl is not just another security trend—it is the next major evolution of the enterprise attack surface. And the organizations that recognize it early will be the ones best equipped to secure the AI-driven future.

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