AI and Cybersecurity: How to train your organization to use…

Generative artificial intelligence (AI) and large language models (LLM) such as ChatGPT, Claude or Gemini have radically transformed the productivity of organizations. However, this massive and accelerated adoption has occurred in many cases without minimum cybersecurity guidelines, opening a new and invisible avenue for the leak of corporate information and technical vulnerabilities.

Successful integration of AI into the office requires a rigorous balance between innovation and Cybersecurity Governance.

Prompts Data Leak: The Silent Enemy

The most widespread security risk when using public AI tools lies not in malicious code, but in the information that employees voluntarily enter into text boxes.

• The Scenario: A financial analyst copies a confidential internal report or proprietary code into ChatGPT to write an executive summary.
• The Problem: If the free or regular commercial versions are used without training opt-out policies, the company that owns the AI ​​stores that information and uses it to refine future models, which can be revealed to external users through mining attacks.

New Technical Threats: Prompt Injection

For companies that develop their own systems by connecting AI models through APIs to their internal databases (RAG systems), highly advanced attack vectors emerge:

1. Prompt Injection: An attacker introduces hidden instructions into the data that the AI reads (such as a resume, a support email, or a website) to force the model to ignore its original security guidelines, allowing sensitive data to be extracted from the local system.
2. Data Poisoning: Manipulation of training data with incorrect or malicious information to alter the behavior and final predictions of the model.

Strategies for the Safe Use of AI in the Company

• Establish Clear Policies: Define what type of information (public, internal, confidential) can be uploaded to external AI platforms.
• Adoption of Enterprise Environments: Use corporate subscription versions of AI that contractually guarantee absolute data privacy and the non-training of models with your information.
• Team Training: Teach development and innovation teams to validate AI outputs and detect and mitigate language model-specific exploits.

Train your teams to adopt artificial intelligence without compromising your sensitive data. Learn about our AI and Cybersecurity Training service.

Summary of Key Security Takeaways and Actionable Guidelines

To maintain highest standards of operational resilience and cybersecurity compliance across corporate systems, organizations must adopt a proactive security stance. Continuous security testing, strict threat modeling, automated auditing pipelines, and adherence to established international frameworks (such as NIST FIPS PUB 180-4, OWASP recommendations, and CISA advisories) form the cornerstone of modern digital protection.

By systematically applying least-privilege principles, cryptographically verifying data assets, and isolating high-risk compute workloads within zero-trust boundaries, security teams can effectively mitigate emergent threats while sustaining long-term technological innovation.