In one sentence
Your employees use ChatGPT, Claude and Copilot daily, with or without your approval. Your data may be going to OpenAI without your knowledge. Your CRM, emails, product specs are potentially exposed. In 2026, securing a company against AI is no longer optional: it's a discipline in its own right with its frameworks, tools and processes.
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The analogy that works
Securing a company against AI in 2026 is like protecting a bank that suddenly has 50 new secret doors. Your employees, out of enthusiasm, open these doors to move faster. Your job as CISO: identify all the doors, choose which ones to close, which ones to monitor, and train the guards so they know who's going through where.
🎣 Want to understand AI phishing attacks in detail?
Vishing, smishing, perfect emails: we dissect AI phishing with professional diagrams.
The 2026 context: why it's urgent
The figures are alarming:
AI incidents in enterprises (2024-2026)
Average cost of AI incident in healthcare12 300 000€
Average cost of AI incident4 900 000€
202667%
202551%
202432%
Today23%
202314%
% companies having suffered an AI incident0
% companies with mature AI governance0
Reading: 67% of companies suffered an AI incident in 2026, but only 23% have mature governance. The gap is massive. Average cost: €4.9M.
Mapping AI risks in the enterprise
Google's SAIF framework: the 2026 foundation
Google published SAIF (Secure AI Framework) in 2024, which has become the de facto standard. 6 pillars:
OWASP LLM Top 10: the 10 vulnerabilities to know
OWASP (the reference organisation in web security) published its Top 10 LLM vulnerabilities in 2024, updated 2025. This is THE reference for developers.
OWASP LLM Top 10 (2025 version)
| 🔓Vulnerability | 💥Impact / Example | |
|---|---|---|
| LLM01 - Prompt Injection | Manipulate the LLM via malicious prompts | Bot says nonsense or exfiltrates data |
| LLM02 - Sensitive Info Disclosure | LLM reveals secrets in responses | API keys, passwords in training data |
| LLM03 - Supply Chain | Corrupted model/dataset upstream | Malicious HuggingFace model |
| LLM04 - Data & Model Poisoning | Training poisoning | Activatable latent backdoors |
| LLM05 - Improper Output Handling | LLM generates dangerous code that's executed | XSS, SQL injection via output |
| LLM06 - Excessive Agency | AI agent with too many permissions | Agent can delete files |
| LLM07 - System Prompt Leakage | Hidden system prompt leak | Competitor steals your business logic |
| LLM08 - Vector & Embedding Weakness | RAG flaw | Vector database poisoning |
| LLM09 - Misinformation | LLM generates false info at scale | False legal/medical responses |
| LLM10 - Unbounded Consumption | No limit on usage = DoS & costs | Bot consumes €100K of API in 1 hour |
AI maturity: the 5 levels
Action plan in 90 days
📚Concrete compliance roadmap
📅 Month 1 : Audit & foundations
Week 1-2: Inventory
- Anonymous survey: who uses which AI and for what?
- Network logs: which AI domains are visited? (ChatGPT, Claude, Gemini, etc.)
- Systems inventory: which chatbots/AI agents are deployed internally?
- Map sensitive data (customers, financial, IP)
Week 3-4: AI Policy
- Draft the company AI policy (1-2 pages, readable)
- Which uses are allowed (ChatGPT Enterprise OK, personal ChatGPT NO) - What data can be shared with an AI - Output validation rules
- Get exec team approval (not just an IT doc)
📅 Month 2 : Tools & training
Week 5-6: Tech
- Deploy ChatGPT Team / Enterprise or Claude for Work (alternative to personal accounts)
- 2FA everywhere on AI tools
- DLP (Data Loss Prevention): tools that detect data leaks to AI
- List of authorised usernames per AI use case
Week 7-8: Training
- 1-hour flash training for all (video + quiz)
- 4-hour in-depth training for techs (devs, ops, CISO)
- "VIP" training for exec team (90 min, focus business risks)
- Internal communication kit (intranet, posters)
📅 Month 3 : Tests & governance
Week 9-10: Tests
- Simulated AI phishing: send phishing emails/SMS to measure click-through rate
- AI pentest: test deployed chatbots (prompt injection, leaks)
- Shadow AI audit: verify unauthorised uses have decreased
Week 11-12: Sustainability
- Establish monthly AI committee (CISO, DPO, IT, Business)
- Define AI security KPIs (incidents, reporting rate, training rate)
- 6-month plan ahead (red team, ISO 42001, etc.)
🎯 Expected deliverables
At the end of 90 days, you must have:
- ✅ AI policy validated and published
- ✅ Complete inventory of AI uses (authorised + Shadow)
- ✅ ChatGPT Enterprise (or equivalent) deployed
- ✅ 80%+ employees trained
- ✅ AI helpline in place
- ✅ AI incident response plan
- ✅ Active monthly AI committee
The CISO's tools in 2026
Essential AI governance tools
| 🛠️Category | ✨Solutions | |
|---|---|---|
| ChatGPT Enterprise / Claude for Work | Corporate versions of LLMs with data control | €20-60/user/month |
| Lakera Guard | Real-time prompt injection detection | For securing your own bots |
| Microsoft Purview AI Hub | Visibility of AI usage in organisation | Included in M365 E5 |
| Robust Intelligence | AI adversarial testing | For those deploying their own models |
| Calypso AI / Glean | AI-specialised DLP | Prevents data leaks to public ChatGPT |
| Zscaler AI Security | Traffic filtering to AI services | Global visibility + blocking |
| ISO 42001 / NIST AI RMF | Official frameworks | Reference standards |
The European AI Act: what you MUST do
Legal obligations in France
The AI Act came into force in 2025-2026, with obligations depending on the risk level of your AI systems:
🔴 UNACCEPTABLE risk (prohibited):
- Social scoring, cognitive manipulation, biometric surveillance in public places
🟠 HIGH risk (heavily constrained):
- AI in HR (recruitment, evaluation), education, justice, critical healthcare
- → Obligation: conformity assessment, register, documentation, human oversight
🟡 LIMITED risk (transparency):
- Chatbots, deepfakes, consumer AI
- → Obligation: clearly inform the user they're interacting with an AI
🟢 MINIMAL risk:
- Classic AI (spam filters, recommendations)
- → No specific obligation
Sanctions: up to €35M or 7% global turnover depending on infringement. More severe than GDPR.
Action: AI compliance audit for AI Act = 2026 priority.
The metaphor that sums it all up
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Building a skyscraper vs adding floors
Doing AI security in 2026 is like renovating a building under construction. You can't stop everything ("we ban AI"), but you can't let everyone go up without a plan either.
The good CISO in 2026:
- 🏗️ Maps what exists (Shadow AI, declared uses, systems)
- 📐 Establishes plans (AI policy, SAIF framework)
- 👷 Trains the workers (employees, devs)
- 🔍 Inspects regularly (red team, pentest)
- 🚨 Plans for firefighters (incident response)
→ No impenetrable wall (impossible with AI). But a solid architecture that withstands shocks.
Key takeaways
- ✅ 67% of companies suffered an AI incident in 2026
- ✅ 4 vectors: Shadow AI, prompt injection, poisoning, agents
- ✅ Frameworks: SAIF (Google), OWASP LLM Top 10, NIST, ISO 42001
- ✅ EU AI Act: sanctions up to €35M, compliance priority
- ✅ 90-day plan: audit → policy → tooling → training → tests
- ✅ Target level: maturity 3 (proactive), the majority are still at level 1-2
AI security is no longer optional. It's become a discipline in its own right with its tools, standards and professions (AI Risk Manager, AI Auditor).
🧠 Quiz
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