When AI Agents Leak

How Easy It Is to Build an AI Agent

The modern automation stack makes it almost trivial to connect enterprise tools:

• n8n: A low-code automation platform that lets you drag-and-drop integrations between apps like Slack, Teams, YouTrack, Jira, Salesforce, and more.
  
• LangChain: A developer framework for chaining together AI models and logic to enrich or transform data in those workflows.
  

By combining these, you can create an “AI agent” that reads a message in Slack, uses an LLM to classify or summarize it, and then creates a new ticket in YouTrack—all in minutes.

Example 1: Slack → YouTrack

Imagine a DevOps team using Slack. An engineer drops a message:

Customer ACME’s production database outage is linked to password rotation bug. Temporary fix applied. Tracking root cause.

A simple n8n workflow could watch for new messages in the #devops channel, run them through LangChain to summarize, then call YouTrack’s API to create a ticket.

Sample n8n workflow (simplified):

nodes:

  - name: Slack Trigger

    type: slackTrigger

    credentials: slackApi

    params:

      channel: "devops"

  - name: LangChain Summarizer

    type: langchainLLM

    params:

      prompt: "Summarize this Slack message for a ticket description"

  - name: YouTrack Create Issue

    type: httpRequest

    params:

      method: POST

      url: "https://youtrack.mycompany.com/api/issues"

      body:

        project: "DEVOPS"

        summary: "{{ $json['text'] }}"

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This agent works—but it also unintentionally leaks sensitive data. The Slack message included customer name (“ACME”), operational context (production database outage), and security-related details (password rotation bug). By creating a ticket in YouTrack, the AI agent just passed PII, operational data, and security flaws into a system potentially accessible to different teams, contractors, or even external vendors.

Example 2: Teams → Jira

Now consider a Teams channel for customer support escalation. A support engineer posts:

Escalating: Customer Contoso reported billing failure. Credit card data visible in logs. Please prioritize.

An AI agent built in n8n could capture Teams messages tagged with “Escalation,” enrich them with LangChain for categorization, then create Jira tickets.

n8n workflow snippet:

nodes:

  - name: Teams Trigger

    type: microsoftTeamsTrigger

    credentials: teamsApi

    params:

      channel: "Escalations"

  - name: LangChain Classifier

    type: langchainLLM

    params:

      prompt: "Extract ticket type (Bug, Outage, Billing, Security) from message"

  - name: Jira Create Issue

    type: jira

    credentials: jiraApi

    params:

      project: "SUPPORT"

      summary: "{{ $json['text'] }}"

      issueType: "Bug"

‍

Here the agent just moved credit card data (regulated under PCI DSS) into Jira, where retention and access controls may not be aligned to payment card compliance. What started as a well-meaning automation quietly became a compliance and legal liability.

Real-World Impact of AI Agent Leaks

These aren’t hypothetical risks. Consider:

• Customer identifiers (names, account numbers): Passed from chat to ticketing system without redaction.
  
• Operational details (production outages, exploits): Shared outside engineering, increasing insider risk.
  
• Financial or personal data (credit card info, health records): Copied into systems not designed for compliance (Jira, YouTrack).
  

Incidents like these fall directly under GDPR, CCPA, HIPAA, and PCI DSS, exposing companies to fines and audits. Worse, they often happen invisibly—compliance teams may not even know these AI-driven exchanges exist.

Why This Happens

1. Shadow AI: 62% of CISOs say less than a quarter of tools in use are registered with procurement.
   
2. Ease of building agents: Platforms like n8n and LangChain lower the barrier so anyone can create these automations.‍
3. No data governance: Once built, the agents pass data between APIs with no guardrails—no redaction, no classification, no audit trail.

This is exactly where an AI data firewall becomes essential. Organizations need more than visibility—they need active control over data moving between AI agents and applications.

Riscosity provides:

• AI Vendor Discovery: Automatically identifies which AI tools and services are being used—catching Shadow AI before it becomes a problem.
  
• Data Flow Classification: Monitors what kinds of data (PII, operational, financial) are moving across workflows.
  
• Policy Enforcement: Blocks or redacts unsanctioned data before it leaves Slack, Teams, or any system.‍
• Data Sovereignty Controls: Tracks where in the world data is sent, preventing accidental cross-border violations.

Bringing It Together

The promise of AI agents is speed: faster ticket creation, quicker incident escalation, less manual work. But speed without guardrails creates exposure.

• A Slack message about a customer outage shouldn’t automatically become a YouTrack ticket containing PII.
  
• A Teams message about billing shouldn’t inject card data into Jira.
  

Without safeguards, these are silent leaks waiting to surface as regulatory violations or security incidents.

With Riscosity, enterprises can still harness AI automation—but with the firewalling, redaction, and visibility needed to keep data flows compliant and secure.

Conclusion

AI agents aren’t malicious. They’re just fast, easy, and careless. In the rush to automate, organizations risk creating hidden channels where sensitive data leaks between apps and third parties.

The solution isn’t to stop using AI agents—it’s to govern them. By treating every agent and every integration as a potential sub-processor, and by putting controls in place to monitor and sanitize data flows, enterprises can balance productivity with compliance.

Riscosity enables that balance—making AI adoption responsible, not reckless.