The Invisible Insider: How Generative AI Is Creating a New Class of Cyber Threats

When the tools meant to boost productivity quietly open new doors for exploitation

🔍 Introduction: A New Threat Hiding in Plain Sight

Threats no longer always come from the outside. While traditional insider threats involve disgruntled employees stealing sensitive data or misusing privileges, a new class of insider has emerged — one powered not by malice, but by misuse of generative AI tools.

From ChatGPT to GitHub Copilot to AI-based slide builders and marketing platforms, employees are now using generative AI to speed up tasks, code faster, and even write reports. But in the process, they may be unknowingly uploading confidential information, violating data privacy laws, or introducing biased, unverifiable outputs into your business decisions.

This isn’t just an IT risk — it’s a governance blind spot. Welcome to the era of the invisible insider.

🧠 What Makes This Threat Different?

Traditional insider threats rely on intent — someone choosing to leak data or sabotage a system. But generative AI introduces unintentional threat vectors:

• An employee pastes confidential code into ChatGPT for debugging.

• A marketer uploads raw customer data to an AI writing tool to personalize messaging.

• A legal team asks an LLM to “summarize this contract” — and it retains sensitive clauses.

These actions may seem harmless, even helpful. But they can result in:

• Data exfiltration to public model logs

• Regulatory non-compliance (GDPR, HIPAA, CPRA)

• Intellectual property leakage

• Bias amplification and hallucinations from unverifiable sources

🧠 Real-World Examples of Generative AI Becoming the Invisible Insider

🔍 1. Samsung Engineers Leak Sensitive Data via ChatGPT (2023)

In early 2023, engineers at Samsung Semiconductor used ChatGPT to help debug and summarize internal code. Without realizing it, they submitted sensitive source code and meeting transcripts containing proprietary chip designs. This data was absorbed by the model during their sessions, creating a potential data leakage incident.

📎 Source: The Economist Tech Quarterly, 2023

🔍 2. ChatGPT Used to Plan Las Vegas Bombing (January 2025)

A former U.S. Army Green Beret was arrested after using ChatGPT to gather information on building explosives and firearms. He allegedly planned an attack outside a Las Vegas hotel on New Year's Day 2025. This case marks one of the first publicized uses of generative AI to support a domestic terror attempt in the U.S.

📎 Source: The Times UK, Jan 2025

🔍 3. DeepSeek AI Breach Exposes Prompt History and API Keys (2025)

In January 2025, Chinese AI platform DeepSeek suffered a data breach due to a misconfigured cloud instance. The breach exposed user prompts, logs, and API credentials, raising concerns about how generative AI platforms store and manage sensitive query data.

📎 Source: Wikipedia – DeepSeek

🔍 4. Hackers Use Google's Gemini AI for Research and Social Engineering (2025)

In a 2025 investigation, WIRED reported that threat groups from China, Iran, North Korea, and Russia were using publicly accessible generative AI models like Google Gemini to support their cyber campaigns. These groups reportedly used AI to generate phishing content, write malicious code, and conduct reconnaissance.

📎 Source: WIRED, April 2025

🔍 5. Prompt Injection Discovered in AI Models with Long-Term Memory (2024–2025)

Security researchers uncovered critical vulnerabilities in large language models, including Google’s Gemini, showing that prompt injection can hijack the model’s memory and steer future outputs. This means even well-intentioned users could unknowingly influence or compromise shared model behavior.

📎 Source: Wikipedia – Prompt Injection

🔍 6. AI-Powered Phishing Becomes Main Entry Vector in 2025

According to a 2025 report from Palo Alto Networks’ Unit 42, AI-generated phishing emails are now the most common method for initial access in cyberattacks. Using generative models, attackers craft highly personalized emails that mimic employee language and formatting.

📎 Source: Palo Alto Networks – Unit 42 IR Report, 2025

🛑 The Emerging Risks from Generative AI Use

1. Shadow AI

Employees use unauthorized AI tools outside corporate control — sometimes unknowingly. These "shadow tools" aren’t logged or governed.

2. Prompt Leakage

Data entered into AI tools can persist in memory, especially in non-private deployments. This creates risk of long-term data exposure.

3. Bias & Hallucinations

Generative models reflect training data biases and invent false facts (hallucinations). When used for research, decision support, or customer-facing content, the damage is reputational and operational.

4. Intellectual Property Drift

When internal R&D, product strategies, or legal language are passed to external AIs, companies risk losing ownership control and innovation edge.

🔐 How Organizations Can Respond

✅ 1. Develop an Internal AI Use Policy

Define what AI tools can be used, for what purpose, and what data is off-limits. Share this policy across departments, not just IT.

✅ 2. Implement AI DLP (Data Loss Prevention)

Deploy solutions that monitor for sensitive data input into AI interfaces. Some newer endpoint tools can detect and block unauthorized data submissions to known AI URLs.

✅ 3. Offer Safe, Internal AI Alternatives

Create approved, sandboxed LLM tools using secure enterprise platforms (e.g., Microsoft Copilot, Google Cloud Vertex AI, private GPTs). Provide productivity benefits without the privacy risk.

✅ 4. Educate on Risks Beyond Productivity

Conduct AI security awareness training that covers:

• What generative AI tools actually do with submitted data

• The risk of inaccurate or legally risky AI-generated content

• The limitations of relying on LLMs for truth or expertise

  
  

📊 Supporting Statistics

• 52% of employees have used generative AI tools for work without notifying IT.

  → Source: Cisco AI Trust Report 2024

  
  

• 33% of companies discovered confidential data had been submitted to public AI tools in the past year.

  → Source: Gartner Cyber Risk Survey, 2024

  
  

• 80% of C-level executives believe their organization lacks visibility into employee AI usage.

  → Source: Deloitte Tech Trends, 2025

🧭 Conclusion: You Can’t Control What You Can’t See

Generative AI tools are here to stay — and they’re redefining what an “insider” looks like. Today, any employee with a browser and good intentions can still pose a security, compliance, and reputational risk if the right guardrails aren’t in place.

The answer isn’t fear or overregulation — it’s transparency, policy, and education. Organizations must balance innovation with oversight, productivity with privacy, and curiosity with control.

Because the next major data breach might not come from a hacker.It might come from your intern… using ChatGPT to finish their onboarding presentation.

 

✅ Generative AI Acceptable Use Policy Checklist

🔐 General Use

• Only approved AI platforms may be used for work-related tasks.

• AI must never be used to process or store confidential, personal, or proprietary data unless explicitly authorized.

• Employees must not use AI-generated content in public-facing materials without review and citation.

  
  

📄 Data Handling

• No customer data, internal strategy documents, or source code may be entered into public AI tools.

• Prompt content entered into AI tools is treated as company data and subject to DLP policy.

  
  

📢 Output & Accuracy

• AI outputs used for decisions (e.g., legal, financial, hiring) must be reviewed by a qualified human.

• Employees must label AI-generated content if used in deliverables or communications.

  
  

🛠️ Security & Compliance

• Log all AI tool usage through company systems or VPN.

• Regularly audit AI tool use across departments (shadow AI detection).

  
  

🧠 Training & Awareness

• All employees must complete AI risk and data protection training annually.

• Teams should nominate an “AI risk liaison” to stay current on policy and support adoption.