Professional uses
What AI can support, what should remain under human responsibility and how to validate outputs.
AI training
Understand AI tools, their risks and the conditions for safe professional use.
AI needs a security framework
AI tools are useful, but they can also expose data, produce unreliable answers or amplify manipulation techniques.
The training helps teams use AI with clear rules, human validation and awareness of cyber risks instead of either banning everything or trusting tools blindly.
It connects practical use cases with security constraints: confidentiality, intellectual property, hallucinations, generated code, deepfakes and decision-making responsibility.
The aim is to help teams adopt useful tools without losing control over data, validation chains, accountability and the security expectations already applied to other digital practices.
Topics covered
Data and confidentiality
Sensitive information, obfuscation, intellectual property, internal rules and public tools.
Abuse cases
Deepfakes, cloned voice, CEO fraud, phishing, generated code and manipulation.
Reliability limits
Hallucinations, outdated answers, prompt sensitivity and the need for verification.
Practical guidelines
- Review before publication or operational use
- Do not delegate binding decisions to a probabilistic tool
- Protect sensitive data and define what may be shared
- Frame internal use cases with clear validation and escalation rules
Expected result
Clear usage rules
Teams know what is acceptable, what is risky and what requires approval.
Better validation
Outputs are checked before action, publication or integration into code and procedures.
Cyber awareness
AI-enabled threats become easier to recognize and report.
Useful adoption
The organization can benefit from AI tools without ignoring confidentiality and security constraints.
Who should attend
Business teams
Understand safe everyday uses and avoid common data-handling mistakes.
Managers
Frame practices, responsibilities and validation chains.
Technical teams
Use AI assistants with caution for code, documentation, scripts and troubleshooting.
From AI awareness to safe adoption
Use-case framing
Teams identify which AI uses are helpful, which are sensitive and which require approval, anonymization or a private environment.
Data protection reflexes
Participants learn to distinguish public information, internal context, personal data, client data, secrets and intellectual property before prompting a tool.
Output verification
The training insists on human validation, source checking and the limits of generated text, code, images or strategic recommendations.
AI-enabled attacks
Examples cover phishing, voice cloning, deepfakes, fake documents, generated malware snippets and manipulation at scale without sensationalism.
Internal rules
The session can help turn principles into practical rules for teams: what to use, what to avoid and when to escalate.
Technical follow-up
For technical teams, the topic can be extended toward secure use of coding assistants, prompt hygiene and review of AI-generated code.