Agentic AI Data Security Risks: Why Non-Human Identities Are the New Threat in 2026

Agentic AI (AI systems that can take actions, not just generate text) is one of the most significant security risks that has emerged within the last year.

Because these systems can interact with SaaS platforms, tools, browser extensions, APIs, databases, and internal systems, they introduce data security risks that traditional cybersecurity models were never designed to handle (and don’t know how to).

Critically, agentic AI systems behave as non-human identities (NHIs) - digital actors with access, permissions, and autonomy. When they’re compromised, misconfigured, or manipulated, the impact is immediate and wide-ranging.

This article breaks down the key data security risks, why agentic AI changes the threat landscape, and what to do to secure this new attack surface with a modern data security program.

What Makes Agentic AI a Data Security Threat?

Let's start off by talking about the biggest problem or ‘controversy’ when it comes to securing and protecting against AI agents.

In 2026 and beyond, non-human identities MUST be governed with the same rigor, oversight, and guardrails as human identities. 

Insider risk has been a well-understood threat vector for years - security teams know how to (or at least try to) monitor, contain, and respond to risky user behavior.

So, why is it so difficult to apply this same discipline to agentic AI systems?

Because most organizations still treat AI agents as tools, not as identities.

The reality is simple:

AI agents ARE identities. They operate, access data, and make decisions just like people - and sometimes faster.

To secure them effectively, organizations must start treating them as full-fledged actors within the enterprise: with permissions, privileges, monitoring, policies, and consequences.

Now, let’s get into WHY exactly they are so risky.

Why Are AI Agents a Data Security Threat?

‘Traditional’ generative AI (traditional is in quotes, because nothing is really traditional about it!) is passive: you ask a question, it answers.

Agentic AI is active:

• It reads data
  
  
• Interprets context
  
  
• Makes decisions
  
  
• Executes actions
  
  
• Interacts with systems

This capacity to act makes agentic AI a data-handling and data-moving entity, similar to a service account, automation script, or bot - but significantly more dynamic and unpredictable.

In short:

Agentic AI = a powerful non-human identity with superuser capabilities.

And that’s where the risks begin!

What are the Biggest Agentic AI Security Risks?

1. Over-Permissioned AI Agents Expose Sensitive Data

Most organizations deploy AI agents with far more access than needed because it’s difficult to predict up-front all the tasks an agent might need to perform.

Examples include:

• AI agents connected to internal CRMs with full read/write permissions
  
  
• Agents granted access to entire document repositories
  
  
• Tools allowing agents unrestricted API access

When an agent is over-privileged:

• Sensitive data can leak
  
  
• Data can be moved or corrupted
  
  
• Systems can be modified without visibility
  
  
• Attackers have a massive blast radius if they compromise the agent

This over-permissioning is currently the #1 agentic AI data security flaw.

2. Prompt Injection Turns Agentic AI Into an Insider Threat

Agentic AI is vulnerable to prompt injection, where malicious content embedded in emails, documents, webpages, tickets, logs, or databases instructs an agent to take unintended actions.

For example:

• An attacker includes hidden instructions inside an email that the agent processes.
  
  
• A malicious webpage includes invisible text commanding the agent.
  
  
• A shared file includes metadata that triggers an agentic tool workflow.

Because agents can act, prompt injection escalates into:

• Data exfiltration
  
  
• Unauthorized file access
  
  
• Corruption of records
  
  
• Sending confidential data outside the org
  
  
• Triggering other systems or other agents

Think this is hypothetical? Unfortunately, it's already possible. Earlier this year at Black Hat, a demonstration done by  three researchers demonstrated a real-world prompt injection attack targeting Google’s Gemini AI via a poisoned Google Calendar invite.

The attack embedded hidden instructions in the titles of calendar events - readable by Gemini, but invisible to users - instructing the AI to take malicious actions when later prompted.

When the researchers casually asked Gemini to summarize their weekly calendar, it executed the hidden prompts, triggering smart home devices like internet-connected lights, shutters, and a boiler - showing how LLMs can be manipulated to cause real-world impact.

This attack didn’t require access to source code or vulnerabilities - just clever use of language inside Google Workspace! 

Organizations use Gemini and Google Calendar every single day. As LLMs become further embedded in tools like Google Workspace & Slack, organizations need to rethink how data exposure and SaaS security can be weaponized via AI.

3. Agentic AI Creates New Attack Surfaces Through Non-Human Identities

Every agent is effectively a non-human identity - like an API key, service account, or automation bot. Today, machine identities already outnumber humans by 10–50x in most enterprises.

Agentic AI accelerates this problem:

• Every AI agent creates a new identity
  
  
• Every tool the agent can use generates more credentials
  
  
• Every workflow spawns temporary access objects
  
  
• Every integration increases the attack surface

The result:

Identity sprawl + agent autonomy = security blind spots that attackers love.

If you don’t monitor, classify, and govern these identities, it becomes impossible to answer basic questions like:

• “What can this agent access?”
  
  
• “What data can it read or move?”
  
  
• “Is this action normal for this identity?”
  
  
• “Did this agent leak anything?”

4. Data Exfiltration Becomes Automated and Scalable

A compromised agent can move data faster than any human.

A few high-risk patterns:

• Copying entire datasets to and sharing them externally
  
  
• Sharing sensitive files to unauthorized recipients or personal accounts
  
  
• Inserting customer data or PII into third-party tools
  
  
• Uploading private documents to the wrong Drive, Slack channel, or database
  
  
• Making API calls that leak tokens or credentials

The agent’s speed and autonomy make exfiltration:

• Harder to detect
  
  
• Faster to execute
  
  
• More damaging

This is a huge reason why AI agents must be treated and monitored like human identities that are tracked through a broader ITDR strategy.

5. Lack of Auditability and Oversight

Many agentic AI platforms still lack:

• Clear audit logs
  
  
• Explainability of decisions
  
  
• Traceable action histories
  
  
• Per-task permission scoping
  
  
• Tool usage logs
  
  
• Access guardrails

Without visibility, you can’t:

• Investigate incidents
  
  
• Prove compliance
  
  
• Remediate/undo agent-caused changes
  
  
• Detect anomalous behavior

Auditability is the missing pillar of agentic AI safety today.

How to Secure Agentic AI and Non-Human Identities

While it's still being explored and vetted, agentic AI can be incredibly useful and amazing to add productivity, growth, innovation to different aspects of an organization. 

The question isn’t ‘should we be adopting agentic AI?’ It’s: ‘how can we adopt agentic AI safely and securely from a security perspective?’

Below are actionable steps security leaders can take TODAY that map directly to today’s highest-impact risk reduction patterns.

1. Treat AI Agents as Privileged Non-Human Identities 

Obviously! But how?

The moment an AI agent can read, write, share, or move data inside your SaaS environment, it becomes a privileged non-human identity - and it needs to be managed as such.

Here’s what that actually means in practice:

• Assign every agent a unique identity → No shared accounts. No inherited permissions. No ambiguity about “who” (or what…) took an action.
  
  
• Enforce strict least-privilege access → AI agents should only touch the data, apps, and workflows required for the specific tasks they’re designed for, nothing more.
  
  
• Remove human-based permission inheritance → Agents should never just inherit the access patterns of the person who deployed them. That’s how you end up with silent privilege creep and massive data overexposure.
  
  
• Rotate and scope credentials continuously → API keys, tokens, and IP tied to AI agents must be short-lived and tightly scoped to minimize blast radius.
  
  
• Track AI agents with the same rigor as insiders → Every action should be logged, monitored, and correlated to an identity - just like any human user operating in SaaS.
  
  
• Fold agent monitoring into your broader ITDR strategy → AI agents behave like identities, so they must be part of your identity threat detection and response (ITDR) strategy. If they act weird or go rogue, sec teams need to know!
  
  
• Automate remediation for sharing, exfiltration, and misuse → When an agent starts over-sharing, mass-downloading, or touching data it shouldn’t, you need automated workflows in place to:
  
  
  • restrict access
    
    
  • revoke permissions
    
    
  • quarantine files
    
    
  • stop the spread

… the exact same way you’d respond to risky human behavior!

Bottom line? If an AI agent can access data, it is an identity - and an extremely powerful one. Treating it with anything less than full identity-level governance and SaaS-native controls leaves your data exposed.

This is the overarching way to secure and protect your SaaS environment and data from AI agents, but there are smaller substeps you can take to make sure that you're checking all the boxes too:

2. Restrict Access to Only the Data That’s Required

AI agents should only reach the specific datasets and tools they need. Use tight controls like:

• Granular access controls for sharing permissions
• Read vs. write separation so agents can’t modify data when they're not supposed to
• Context on each agent (aka identity) to decipher whether they should have access or not based on role, scope, activity, etc.

Basically, if an agent doesn’t absolutely need access, it shouldn’t have it.

3. Require Human-in-the-Loop for High-Risk Actions

For sensitive operations, AI shouldn’t act alone. Always require human approval for:

• Data sharing (both internal AND external)
  
  
• Record edits or deletions
  
  
• Document changes when the file contains PII
  
  
• System configuration updates

Guardrails prevent automated mistakes from becoming data incidents.

4. Log Every Action the Agent Takes

Agentic AI moves quickly - you need full auditability. Security teams need to map:

• Tool and API calls
  
  
• File access
  
  
• Data movement within Drives
• Data sharing (both internal AND external)
  
  
• Decision steps

In a perfect world, this SHOULD be automated, that way - it can be scalable, accurate, and precise. After all, if you can’t trace it, you can’t secure it.

5. Continuously Monitor for Abnormal Behavior

Treat AI agents like continuously active identities. Watch for:

• Unusual activity patterns
• Anomalous geo locations
  
  
• Unexpected data access
  
  
• Irregular movement or sharing
  
  
• Sudden permission misuse

Real-time detection is the difference between a contained event and a data breach.

How DoControl Solves Agentic AI Data Security Risks

Agentic AI blends the worlds of:

• SaaS security
  
  
• Data security
  
  
• Identity security
  
  
• Insider risk management
  
  
• Data access governance
  
  
• Data loss prevention 

DoControl is one of the only platforms built from the ground up to unify data context, identity context, and access context. This creates a security model that is designed for the new world where AI agents:

• behave like autonomous identities
  
  
• access sensitive SaaS data continuously
  
  
• operate across multiple applications
  
  
• generate enormous amounts of activity

DoControl gives you the full picture - identity, action, access, data, permissions, and anomalies - in one place.

DoControl Identity Threat Detection & Response (ITDR) capabilities directly mitigate the data security risks introduced by rogue + suspicious identities within your SaaS environment.

DoControl’s IDTR module gives you the identity-level context needed to distinguish:

A legitimate employee action

VS.

A non-human identity taking over & behaving abnormally or maliciously

DoControl delivers the controls, context, and automation needed to keep data safe when identities act suspiciously inside your SaaS environment. 

Key Takeaways

Agentic AI introduces real, material data security risks - not because it is malicious, but because it:

• Acts autonomously
  
  
• Has access to sensitive systems
  
  
• Behaves as a non-human identity
  
  
• Can be influenced by untrusted input
  
  
• Moves data quickly and at scale

To secure this new class of automation, organizations must shift mindset from:

“AI agents are a tool” → “AI agents are identities.”

Managing non-human identities and limiting agent autonomy when it’s not needed will be the core of AI security moving forward.

As AI shifts from passive tools to active participants in our systems, the real risk isn’t just what these agents can create - it’s what data they can access, move, manipulate, and expose.