AI agents are quickly becoming embedded into everyday business operations. They route tickets, update CRM records, sync data across platforms, summarize content, and trigger workflows without waiting for human input.
They are no longer confined to experimental tools, but are now being built directly inside business-critical SaaS platforms like Microsoft 365 through Copilot Studio, Google Workspace with Gemini, Salesforce via Agentforce, as well as through dedicated AI platforms such as OpenAI and ChatGPT and workflow automation tools like Workato and n8n.
And yet, most organizations are still thinking about AI agents the wrong way.
They are treated as features. As productivity enhancements. As smarter automations layered into existing tools.
From a security and governance perspective, that framing is dangerously incomplete.
AI Agents are not Features
AI agents are fundamentally different from traditional AI capabilities.
They do not wait for a user prompt. They do not operate in a single application. And they do not require real-time human approval to act.
AI agents are autonomous operators that:
- Act continuously, not per request
- Hold credentials, tokens, or delegated access
- Execute workflows across multiple SaaS systems
- Make decisions without real-time human approval
This combination is what makes AI agents powerful. It is also what makes them risky.
Once deployed, agents run in the background, performing actions on behalf of users or teams for extended periods of time. They are persistent actors inside the environment, not momentary interactions.
A New Class of Identity Hiding in Plain Sight
When viewed through a security lens, AI agents are best understood as a new class of identity.
They are closer to service accounts or integrations than to users or large language models (LLMs).
Like traditional service accounts, AI agents operate with long-lived, non-interactive access that can touch multiple SaaS systems and often goes unreviewed.
But unlike traditional service accounts, AI agents make decisions. They adapt behavior. And they are frequently created outside of security-owned processes.
That distinction matters.
Most SaaS security programs were never designed for autonomous, non-human identities that can act, change, and expand their reach over time.
How AI Agents Quietly Expand SaaS Risk
AI agents introduce a set of security challenges that rarely appear all at once. Instead, risk accumulates gradually and often invisibly.
Autonomous Action Across SaaS Systems
AI agents rarely operate in isolation. A single agent may interact with email, collaboration tools, CRM platforms, ticketing systems, and file storage at the same time.
This creates cross-SaaS risk, where one agent has influence far beyond a single application. If that agent is misconfigured or compromised, the blast radius is broad by default.
Created by Users, not Security Teams
Most AI agents are created by business users, citizen developers, or operations teams looking to move faster.
As a result, agents are often deployed without security team awareness and go undiscovered. Without basic inventory and ownership in place, access is not reviewed and risk is never assessed.
This is shadow IT, but with autonomy and decision-making built in.
Broad, Persistent Permissions
To function properly, AI agents are often granted wide permissions upfront.
Those permissions tend to persist over time without expiration, go unreviewed, and expand farther as workflows evolve.
What starts as convenience quickly becomes standing privilege.
No Real-Time Policy Enforcement
Once an agent is running, actions execute automatically. There is no human in the loop. No approval prompt. No pause for policy validation.
If an agent is allowed to perform an action, it will continue to do so until access is explicitly changed or removed.
Invisible Data Movement
AI agents frequently move data between SaaS systems without visibility. Sensitive information may be copied, summarized, enriched, or forwarded automatically, sometimes into systems with weaker controls or external AI services.
Without clear visibility into these data flows, organizations lose control over where data lives and how it is used.
Undefined Ownership and Accountability
When something goes wrong, security teams often struggle to answer basic questions:
- Who owns this agent?
- Why does it exist?
- What business process does it support?
Without clear ownership, remediation slows down and accountability breaks down.
Behavioral Drift Over Time
AI agents are not static. Prompts change. Models evolve. Integrations expand. Business needs shift.
Over time, agent behavior drifts, increasing access scope and risk exposure without any explicit trigger. This drift is silent, gradual, and easy to miss.
Why Existing SaaS Security Controls Fall Short
Most SaaS security controls are built for people. They assume interactive login, intentional actions, and periodic review cycles.
AI agents break those assumptions. They do not log in like users. They do not request access in predictable ways. And they do not wait for permission once deployed.
This is why controls like SSO, MFA, and user-centric access reviews are necessary but insufficient.
AI agents require agent-centric security thinking.
Securing AI Agents Starts with Treating Them as Identities
To secure AI agents effectively, organizations must first acknowledge what they are: autonomous, non-human identities with persistent access. That shift enables a more practical and scalable security approach.
Discover AI Agents and Shadow AI
Security teams need continuous visibility into where AI agents operate across SaaS platforms, how AI-driven integrations and automated workflows are created, and which agents exist outside formal security processes.
Discovery must also be ongoing, not point-in-time.
Understand Agent Access and Scope
Every AI agent should be evaluated based on the permissions it holds, the SaaS systems it can access, the data it can touch, and how that access is (and was) delegated.
This forms the foundation for meaningful risk assessment.
Monitor Behavior, Not Intent
AI agents should be evaluated based on what they do, not what they were designed to do.
Understanding agent activity across SaaS environments helps surface risky behavior early, before it becomes an incident.
Enforce Least Privilege
Security teams need the ability to safely reduce agent permissions and adjust access as workflows evolve, remediating risk without stalling or breaking business-critical operations.
As AI agents expand across SaaS systems, least-privilege enforcement must account for changing access paths and growing cross-application dependencies.
Establish Clear Ownership
Every AI agent should have a clearly defined owner responsible for its business justification, ongoing access review, and lifecycle management.
Governance only works when accountability exists.
Frequently Asked Questions
What is an AI agent in an enterprise environment?
An AI agent is an autonomous system that can act independently across SaaS applications without real-time human input. Unlike traditional AI features, AI agents hold credentials, execute workflows, and make decisions continuously, often across multiple systems.
Why are AI agents considered an identity risk?
AI agents function as non-human identities with persistent access to SaaS systems. They hold tokens, API keys, or delegated permissions, operate without interactive login, and are rarely reviewed, making them similar to service accounts but with decision-making capabilities.
How are AI agents different from traditional service accounts or automations?
Traditional service accounts execute predefined actions and follow static logic. AI agents adapt behavior, interact with multiple systems, and expand workflows over time. This autonomy introduces new risk that traditional identity and access controls were not designed to manage.
Can AI agents create data exposure or compliance risk?
Yes. AI agents often move, summarize, or transform data across SaaS platforms automatically. Without visibility into these data flows, sensitive or regulated information can be copied into unintended systems, increasing exposure and compliance risk.
Why don’t existing SaaS security controls fully protect against AI agent risk?
Most SaaS security controls are built for human users and assume interactive login, intentional actions, and periodic access reviews. AI agents act continuously, without approval prompts, and often outside traditional identity governance workflows.
How can organizations secure AI agents effectively?
Organizations can secure AI agents by discovering where agents exist, treating them as identities, monitoring their behavior across SaaS systems, enforcing least-privilege access, and assigning clear ownership for ongoing governance and lifecycle management.
Final Thoughts: AI Agent Security is a SaaS Security Problem
AI agent security is not just an AI governance issue. It is a SaaS security issue, an identity issue, and a data exposure issue, all at once.
As SaaS and AI adoption accelerate together, security teams need a way to find and fix SaaS and AI risks across applications, identities, and automated workflows.
At Valence, we help organizations gain unified visibility into SaaS environments, AI-driven integrations, and both human and non-human identities, enabling security teams to understand access, reduce risk, and enforce governance without slowing the business.
Because autonomous systems demand identity-driven security. And AI agents are only getting started.
Schedule a demo to discover and secure AI agents across your SaaS environment.
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