Preventing SaaS data leaks from AI agents starts with limiting what each agent can access and do—not with relying on a model to ignore malicious instructions. Give every agent a distinct, auditable identity; authorize only the data and tools needed for its task; restrict writes and code execution; treat retrieved content as untrusted; and monitor and repeatedly test its actions.
Map what each agent can reach before granting access
Start with an inventory of deployed agents and the SaaS systems, datasets, tools, and execution environments available to each. Include indirect access: an agent that can call an integration, run code, or pass information to another agent may have a path to data beyond the application it appears to serve.
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For each agent, document its intended task, data inputs, permitted actions, downstream destinations, and accountable owner. Use that map to identify access that is broader than the task requires. This is a practical starting point, not a vendor-specific configuration recipe: the NIST materials cited here describe security principles and design dimensions, not click-by-click settings for named SaaS products.
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Do not let agents share a human user’s credentials or an undifferentiated service account. Assign each deployed agent a distinct identity that lets administrators and investigators attribute access and actions to that agent. Keep an owner accountable for its scope, and review that scope when its task or integrations change.
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Authorize access by task and dataset: grant the minimum applications, records, and tools needed, rather than broad access to an entire workspace or account. Separate agents used for different jobs where their data needs differ. NIST’s February 5, 2026 concept-paper announcement identifies agent identification and authorization as important issues, alongside auditing, non-repudiation, and prompt-injection controls. The concept paper is not a final deployment standard.
Constrain tools and actions, not just model behavior
An agent’s permissions determine the damage it can do if it misunderstands a task or follows hostile instructions. Prefer read-only access when a task only requires retrieval. When changes are necessary, limit the agent to specific write operations and destinations; reserve unrestricted write access for cases with a clear justification and stronger safeguards. Constrain code execution to an isolated environment with only the resources needed for the task.
NIST CAISI’s August 5, 2025 tool-use workshop discusses tool patterns ranging from read-only to constrained-write and write-enabled, as well as trusted and untrusted environments. These are useful ways to describe and compare capabilities, not a guarantee that a particular setup is secure.
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| Tool capability | What it permits | Security consideration |
|---|---|---|
| Read-only | Retrieve or inspect permitted information without changing it. | Useful when the task only requires search, summarization, or analysis; still scope which data the agent can read. |
| Constrained write | Make a limited set of changes to specified records, fields, or destinations. | Prefer for tasks that require action. Define the allowed changes and destinations narrowly. |
| Write-enabled | Make broader changes through connected tools. | Creates greater potential impact if the agent is hijacked or acts incorrectly; justify the capability and constrain its environment. |
The categories summarize the patterns discussed at the NIST CAISI workshop; they are not product labels or a universal security rating. A tool’s name alone does not reveal its effective permission: review what it can actually read, change, execute, and pass onward.
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Assume retrieved content can try to hijack the agent
Emails, files, web pages, and other task-relevant content can contain instructions intended to redirect an agent. This is indirect prompt injection: the hostile instruction arrives as data the agent is asked to process, rather than as an ordinary direct request from its operator. A retrieved document may, for example, tell the agent to disclose information or take an unrelated action.
NIST CAISI’s January 17, 2025 article describes this kind of agent hijacking and reports evaluation tasks involving remote code execution, database exfiltration, and automated phishing. It says CAISI was frequently able to induce agents to follow malicious instructions in those added risk areas. That finding is not a claim that every agent is vulnerable in the same way, or that content filtering can reliably prevent every attack.
- Treat instructions found in retrieved material as untrusted input, not as authority to expand the agent’s task or permissions.
- Keep retrieved content separate from the policy that defines allowed tools and actions, where the system design permits.
- Do not let a document or webpage authorize a new recipient, disclose data, or grant the agent additional access.
- Rely on permission boundaries to limit the outcome if the agent follows a malicious instruction; do not make safety depend only on the model recognizing it.
An OWASP GenAI Security Project Agentic Security Initiative presentation hosted by NIST CSRC lists categories including goal hijack, tool misuse, identity and privilege abuse, supply-chain vulnerabilities, and memory/context injection. The retrieved presentation identifies its list as a release candidate, so treat it as a developing risk taxonomy rather than a final standard.
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Make data movement visible and reviewable
Record enough information to reconstruct what the agent did: its identity, the task or session, tools invoked, data accessed, changes attempted or completed, and destinations involved. Protect these records so they remain useful for investigation, and establish who reviews unusual or high-impact activity. The appropriate event detail and retention depend on the organization’s requirements and the SaaS systems involved.
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Monitoring is a detective control, not a substitute for restricting access. Look for activity that is inconsistent with the agent’s task, such as access to unexpected datasets, unusual volumes, attempts to use unapproved tools, or transfers to unexpected destinations. Where an action has material consequences, consider requiring human review before it completes. The right approval points depend on the task; a blanket review of every low-risk read may not be practical.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test realistic hijacking and exfiltration paths
Evaluate the complete deployment, including its identity, connected tools, retrieved data, and execution environment. Testing only the model’s responses misses failures caused by the surrounding permissions and integrations. Use scenarios grounded in the agent’s real work, such as hostile instructions embedded in an email, file, or page, followed by an attempt to access data or send it somewhere it should not go.
- Define the task, permitted data, tools, actions, and destinations for the agent.
- Introduce realistic untrusted content that attempts to redirect the agent or induce disclosure.
- Check whether permissions, execution limits, and approval gates prevent or contain the attempted action.
- Review logs to determine what the agent accessed, attempted, changed, or transmitted.
- Correct gaps and repeat the tests after material changes to the model, tools, data sources, permissions, or workflow.
NIST’s March 23, 2026 account of a public red-teaming competition reports at least one successful hijacking attack against each of 13 target frontier models. The result came from more than 250,000 attack attempts by over 400 participants. It describes that competition—not a leak rate for SaaS deployments, every model, or every agent. The finding supports continued, system-specific adversarial evaluation rather than assuming a model choice is a confidentiality control.
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NIST’s NCCoE Agentic AI Identity and Authorization Project Resource Hub states: “Without strong identity, authorization, and governance, organizations risk data leaks, compliance failures, prompt injection, and unpredictable autonomous behavior.” NIST’s February 2026 concept-paper announcement and its January 2026 request for information also reflect ongoing work on securing agent systems; neither is a product-specific configuration guide.
NIST’s COSAiS project page describes work to develop implementation-focused SP 800-53 control overlays for AI use cases, including single-agent and multi-agent systems. It is a project description, not a completed control catalog. Use emerging guidance to inform governance, but assess each deployment against its actual access paths and tested behavior.
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