Generative AI: revolution or time bomb?
In 2025, 78% of companies use some form of generative AI. But only 23% have implemented a dedicated security policy. This gap creates major risks.
The 5 main risks
1. Sensitive data leaks
The problem: Your employees share confidential data with ChatGPT, Copilot, or other AI tools without realizing the implications.
Real cases:
- Samsung: engineers shared proprietary source code
- American lawyer: use of legal cases invented by ChatGPT
- Companies: sharing customer, financial, strategic data
Data at risk:
- Source code and intellectual property
- Customer data (GDPR)
- Financial information
- Confidential business strategies
2. Prompt Injection
The problem: Applications integrating LLMs are vulnerable to a new class of attacks.
3. Shadow AI
The problem: Employees use unapproved AI tools, creating security blind spots.
Statistics:
- 65% of employees use AI tools not approved by IT
- 40% share sensitive data with them
- 90% of companies don't have complete visibility on AI usage
4. Hallucinations and misinformation
The problem: LLMs generate false content with apparent confidence.
5. AI Supply Chain
The problem: Your suppliers use AI on your data.
The OWASP LLM Top 10 Framework
OWASP published a Top 10 dedicated to LLM applications covering prompt injection, insecure output handling, training data poisoning, and more.
How to secure AI in business?
1. Governance
- Approved AI tools list
- Authorized data types
- Validation process
- Clearly defined responsibilities
2. Technical controls
- DLP on AI access
- Network traffic analysis
- AI-adapted CASB solutions
3. Secure architecture
Gateway, filtering, logging, and alerting.
Conclusion
Generative AI offers immense opportunities but requires a proactive security approach. Companies that anticipate these risks today will avoid costly incidents tomorrow.
Need an AI security audit? RedSentinel offers assessments based on the OWASP LLM Top 10 framework.
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