AI Security: Real Risks and Critical Questions for Organizations

Where Do Organizations Use Artificial Intelligence?

1) Public AI Tools

Question: How are employees using these tools?

Answer: Often in an uncontrolled way, by entering sensitive data into prompts.

Risks:

• Sensitive data leakage (customer data, source code, internal documents)

• Data exfiltration via prompt injection

• Shadow AI usage (outside IT control)

2) AI Embedded in SaaS Applications

Question: Why is AI in SaaS risky?

Answer: Because data is processed without the user fully realizing it, leading to loss of control.

Risks:

• Lack of visibility into where data is processed

• Potential inclusion of data in model training

• Authorization controls being bypassed via AI

3) Third-Party Productivity Platforms

Question: Why are these platforms critical?

Answer: Because they contain the organization’s most valuable data.

Risks:

• Full data indexing by AI

• Excessive access due to misconfigured permissions

• Unnoticed data access via APIs

4) Custom AI Applications (In-House)

Question: Is building your own AI system safer?

Answer: Partially yes—but misconfiguration can create even greater risks.

Critical Components:

• RAG (Retrieval-Augmented Generation)

• Vector databases

• Model Context Protocol (MCP) clients

Risks:

• Internal data leakage via prompt injection

• Data reconstruction from embeddings

• Lack of proper access control

5) AI Agents

Question: Why are AI agents among the most critical risks?

Answer: Because they don’t just suggest—they take action.

Risks:

• Incorrect decisions turning into real system actions

• Chained access across systems via APIs

• Loss of control in multi-step processes

  
  

What Are the Top 5 AI Security Risks?

• Data Leakage

• Prompt Injection

• Privilege Escalation

• Model Manipulation (Model Abuse)

• Supply Chain Risks

Why Is “We Are Already Secure” Misleading?

Question: Aren’t existing security solutions sufficient?

Answer: No, in most cases they are not.

Because:

• DLP solutions do not analyze data inside prompts

• SIEM systems do not monitor AI decisions

• IAM systems cannot control model behavior

AI introduces a new risk layer outside traditional security controls.

What Should Organizations Do?

1) Make AI usage visible

• Detect Shadow AI

• Create an AI usage inventory

2) Control data flow

• Establish prompt → output → logging chain

• Apply sensitive data masking

3) Implement a dedicated AI security layer

• AI firewall and guardrails

• Prompt filtering mechanisms

4) Add control mechanisms for agents

• Human-in-the-loop processes

• Action approval mechanisms

Quick Reality Check

If you cannot clearly answer these questions, you are at risk:

• Which AI tools are employees using?

• What data is being sent to AI systems?

• What systems can AI access?

• What actions can AI agents perform?

Conclusion

AI risk is not a future problem—it is today’s active attack surface.

If:

• AI usage is uncontrolled

• Data flows are not monitored

• Agents are not governed

  
  

your organization has already created a new attack surface—likely without realizing it.