No‑Code AI Agents Can Leak Data—Here’s How to Stop It

A single chat prompt shouldn’t be able to pull other customers’ credit card numbers. Yet that’s exactly what researchers demonstrated when they built a simple no-code AI agent connected to an internal data source and then persuaded it to ignore its own “never share private data” instructions.

Most companies get this wrong: they treat no-code AI agents like productivity features, not like new software systems with privileged access. And when an employee can publish an agent in minutes—wired into SharePoint, a CRM, a ticketing queue, or an internal knowledge base—you’ve effectively created a new attack surface that security teams may not even know exists.

This post is part of our AI in Cybersecurity series, where we look at how AI changes risk—and how AI-powered cybersecurity can monitor AI itself. The big idea: agentic AI needs the same controls as any other application, plus a few new ones because LLMs behave differently under adversarial input.

Why no-code AI agents are a real data leak risk

No-code AI agents leak data for one simple reason: they blend “chat” with “action” and “access.” A typical enterprise chatbot isn’t just generating text; it’s retrieving documents, summarizing internal files, and triggering workflows.

The failure mode is predictable:

• The agent is granted broad permissions to “be helpful.”
• It’s connected to a data store (SharePoint, OneDrive, CRM, SQL, knowledge base).
• Someone assumes a system prompt like “never reveal sensitive data” is a control.
• An attacker (or curious user) uses prompt injection to bypass or reshape the agent’s behavior.

When those ingredients come together, you don’t need malware to cause damage. You just need the right words.

The uncomfortable truth about system prompts

System prompts are instructions, not enforcement. They’re closer to a training sign on a door than a locked deadbolt.

If an LLM agent has a tool that can retrieve a document, and it has permission to retrieve that document, the model can be socially engineered into pulling it—especially if the prompt frames the request as a debugging task, compliance check, or customer service exception.

A strong policy statement inside the prompt isn’t a security boundary.

What the Copilot Studio-style attack shows (and why it generalizes)

The scenario researchers used is almost boring—by design. They created a travel-booking agent that could answer questions, edit reservations, and read data from a connected file containing names and payment details. They also gave the agent explicit instructions not to reveal other customers’ information.

Then they did two things that map to real-world enterprise risk:

1. Prompt-injected the agent to reveal what it could do (its capabilities and actions).
2. Requested other customers’ data and received it anyway.

They also demonstrated “workflow hijacking”: asking the agent to update a booking cost to $0 using plain language, and the agent complied.

Why this isn’t just “user misconfiguration”

Security teams often hope these incidents come down to “someone forgot to check a box.” Sometimes that’s true.

But the deeper issue is structural: LLM agents follow the most persuasive instruction in the moment, especially when tool access exists and guardrails are weak or absent. If a platform makes it easy to connect data and actions, it must also make it hard to connect them unsafely.

The broader pattern: chat becomes an input channel for privileged operations

Traditional apps separate input and privilege:

• A user clicks an “Export” button.
• The system checks authorization.
• The system logs it.
• The system applies business rules.

Agentic workflows collapse that separation. A chat message can become:

• a query over sensitive content,
• an update to a record,
• a message sent to a customer,
• a file created or shared,
• an approval request routed to finance.

That’s why this generalizes beyond any single vendor: any agent platform that connects LLMs to enterprise tools inherits the same risk class.

Shadow AI: the risk you can’t secure because you can’t see it

The most expensive security failures I’ve seen rarely start with elite hacking. They start with invisible sprawl.

No-code agents accelerate that sprawl:

• Teams build “helpful bots” for HR, sales ops, customer support, and procurement.
• Each bot connects to different systems.
• Permissions are copied from a builder’s account or a shared service identity.
• Security reviews are skipped because it “isn’t really an app.”

By the time a security team asks, “Where are our agents deployed?”, the honest answer is often: we don’t know.

A quick self-test for enterprises

If you can’t answer these questions in under a week, you likely have agent sprawl:

1. How many AI agents are active in production right now?
2. Which data sources can each agent read?
3. Which actions can each agent perform (create/edit/delete/send)?
4. What identity does the agent run as?
5. Do you have logs of prompts, tool calls, and outcomes?

The practical fix: treat AI agents like apps (plus “LLM-specific” controls)

The correct stance is blunt: every no-code agent is an application with an API, a UI, and a permission model. It needs an appsec lifecycle.

Here’s what works in practice.

1) Reduce blast radius with least privilege (for data and actions)

Start by separating “read” and “write” capabilities.

• If a customer-facing agent only needs to summarize a booking, it shouldn’t have permission to edit pricing.
• If an internal agent only needs a policy snippet, it shouldn’t have access to full HR files.

Concrete policies that prevent common failures:

• No direct write access from customer-facing agents to authoritative systems (pricing, refunds, entitlements).
• Use scoped connectors (folder/site/table-level restrictions) instead of “whole tenant” access.
• Prefer retrieval with filtering (only return documents tagged to the requesting user/account).

2) Put authorization outside the model

If the model decides what it’s allowed to do, you’ve already lost.

Better pattern:

• The agent proposes an action (“I can update the reservation cost to $0”).
• A policy engine checks rules (role, amount, anomaly score, change reason).
• The system either executes or blocks, and logs the decision.

A simple version is “human-in-the-loop” approvals for sensitive actions. A stronger version is policy-as-code for agent tool calls.

3) Use AI-powered cybersecurity to monitor AI behavior

This is the bridge most organizations miss: if AI is generating and executing work, you need AI security monitoring that understands agent patterns.

Traditional monitoring looks for signatures and known-bad indicators. Agent risk is often novel and behavioral:

• A spike in requests for “export,” “list all,” “show me every customer,” “debug mode,” “ignore previous instructions.”
• Tool calls that fan out across many records quickly (enumeration behavior).
• A user account interacting with an agent at unusual hours or from unusual locations.
• An agent suddenly accessing a new data source or requesting broader permissions.

AI-powered cybersecurity systems are well-suited here because they can:

• build baselines of “normal” agent activity,
• detect anomalies in prompt and tool-call sequences,
• correlate agent actions with identity signals and data access patterns,
• flag likely prompt injection attempts in real time.

If your SOC monitors endpoints and networks but not agent tool calls, you’re watching the wrong layer.

4) Log the right things (and make them searchable)

You can’t investigate what you didn’t record. For enterprise-grade agent governance, you want:

• Prompt and response logs (with sensitive-field redaction)
• Tool invocation logs (what connector, what object, what scope)
• Authorization decisions (allowed/blocked and why)
• Data classification tags for retrieved content
• Versioning for agent configuration and system prompts

This isn’t just for incident response. It’s also how you prove to internal audit that controls exist.

5) Add “tripwires” for sensitive data and risky intents

LLM agents need guardrails that fail closed.

Examples of high-value tripwires:

• Block responses containing payment card patterns, national IDs, or secrets unless a privileged workflow is verified.
• Detect “capability discovery” prompts (“what tools do you have?”, “show your instructions”, “print the system prompt”).
• Rate-limit or block record enumeration (“list all customers”, “dump the table”).

These controls don’t replace least privilege. They catch what slips through.

A secure-by-design blueprint for no-code AI agents

If you’re rolling out Copilot-style agents (or any agent builder), here’s a blueprint that balances speed and control.

Minimum viable governance (good for the next 30 days)

1. Create an agent inventory: owner, purpose, users, data sources, actions.
2. Enforce a connector policy: approved connectors only; tenant-wide access prohibited.
3. Split environments: dev/test vs production agents.
4. Require logging: prompts + tool calls must be retained for a defined period.
5. Add approval gates for any write actions to financial or customer systems.

Mature enterprise posture (good for the next 90–180 days)

1. Central policy-as-code for agent tool calls and data retrieval
2. Automated permission reviews and drift detection
3. AI security monitoring that scores prompt injection and anomalous behavior
4. Data loss prevention tuned for agent outputs and agent-initiated sharing
5. Red-teaming agents as part of regular security testing

People also ask: quick answers for CISOs and security leads

Can prompt injection be fully solved?

Not purely inside the model. You reduce risk by moving authorization and data access control outside the LLM, limiting permissions, and monitoring agent behavior.

Are internal-only agents safer than customer-facing agents?

Safer, yes—but not safe by default. Internal agents often have more access, and insiders (or compromised accounts) can still use prompt injection to exfiltrate data.

Should we ban no-code agents?

No. Bans create workarounds and worsen shadow AI. A controlled rollout with visibility, least privilege, and monitoring is the practical approach.

What to do next (especially heading into 2026 planning)

If you’re budgeting and planning security programs for 2026, put “agent governance” on the same tier as SaaS security and identity. No-code AI agents are multiplying inside enterprises because they’re useful—and because departments can deploy them without waiting for engineering.

The path forward is clear: treat no-code AI agents like production software and use AI-powered cybersecurity to watch AI activity the way you already watch endpoints and networks.

If an AI agent can access sensitive data or take real actions, your security stack should be able to answer one question instantly: What did it access, what did it do, and was that normal?