The acceleration of generative artificial intelligence uses in companies brings out a critical phenomenon, the Shadow ai. After the Shadow itthis gray area designates AI tools used outside of any governance or technical supervision. If it reflects an appetite for innovation, it also exposes organizations to considerable security, conformity and reputation risks.
Invisible, but massive AI initiatives
THE Shadow ai Takes various forms, it can be a collaborator who uses Chatgpt to generate a customer deliverable, a marketing department that incorporates an AI co -pilot in his CRM, or a manager who relies on an autonomous agent to automate his processes. These uses often develop without validation of the CIO, without compliance review, and sometimes without even informing the hierarchy.
The trivialization of IA interfaces in SaaS, and the absence of technical barriers reinforce the adoption of AI solutions. According to several internal studies carried out by cloud players, more than 60 % of professional users already use generative AI tools without formal supervision. Certain estimates report more than 10,000 IA tools launched in 2024, illustrating an explosion of the offer far beyond traditional control capacities.
A systemic risk by accumulation
This fragmentation of uses creates a blind spot in the safety strategy of companies. Thus the risks are not only technical (data exfiltration, persistence in models, attacks by prompt injection) but also legal (non-compliance with the GDPR), ethical (use of tools trained on opaque corpora) and strategic (loss of control over intellectual property).
As these tools integrate into daily work flows, the Shadow ai becomes an attack surface. Each non -framed use weakens the organization’s digital trust base, and as for the Shadow IT, the belief that “our teams do not do this” is precisely what lets the breaches multiply.
Weak signals with strong consequences
Unlike Shadow itwhich focused on identifiable tools (Dropbox, Trello, Slack, etc.), the Shadow ai Most of the time is invisible by conventional supervision tools. It leaves no lasting trace on internal networks, because it is based on API calls to external services or on plugins in standard office follow -up.
The alert signals are therefore multiple, like content generated without stylistic coherence, un -documented automated decisions, suspicious files in collaborative tools, and it is precisely these anomalies which must be the subject of active monitoring.
From symptom to strategy: rethink adoption
Instead of reacting by blocking, companies have the opportunity to transform this disorder into revealing real needs, because behind each non -framed use is often hidden the need to automate repetitive tasks, to accelerate deliverables, to test new methods. THE Shadow ai then acts as a “prototype engine”, and unwittingly identifies the internal ineffective areas. Finally, the challenge is therefore not to eradicate it, but to channel it.
Regain control: a three -part strategy
Faced with this silent proliferation, organizations must build a progressive and coherent response. Three levers must be activated simultaneously:
1. Visibility
Set in place IA use detection tools (log analysis, API flow inspection, content audits generated), in connection with business teams. Solutions like Cyberhaven, Microsoft Defender or Netskope already allow these flows to be mapped.
2.
Establish a clear use policy, based on real cases rather than theoretical prohibitions. It is a question of integrating:
-
- White lists of authorized tools
- Explicit Red Lines (no confidential data in public AI)
- Principles of prompt hygiene and concrete examples
- Short training formats (videos, interactive guides)
3. Support
Offer secure internal alternatives, such as owner co-pilotes, tailor-made LLM agents, AI assistants embedded on controlled architectures (Snowflake Cortex, Openai Enterprise, LLM Open-Source + RAG). The objective is to offer power without the risk.
Expanded responsibility
THE Shadow ai does not only be a matter of cybersecurity and must be dealt with as a subject of global governance, involving business directions, compliance, human resources and the CIO. At a time when regulators strengthen the requirements on the transparency of models and the traceability of uses, tolerating these gray areas would amount to building the digital future of the company on an unstable basis.
The real risk is not the use of AI but its invisibility.
Shadow it vs shadow ai: what differences?
| Criteria | Shadow it | Shadow ai |
|---|---|---|
| Definition | Use of digital tools without validation of the DSI | Use of IA tools or models without supervision |
| Typical examples | Dropbox, Trello, Google Docs, Slack | Chatgpt, AI concept, unauthorized co -pilotes |
| Visibility for the DSI | Average (networks) | Weak (external API, invisible content) |
| Main risks | Data leaks, GDPR | Uncontrolled learning, hallucinations, drifts |
| Business reaction | White lists, blockages | In the emergence phase, need for supervision |
| Cybersecurity impact | Modeled and controlled | Quickly expanding, poorly anticipated |
| Remediation tools | Casb, SSO, software inventory | IA policy, traceability tools, internal LLM |
From taboo to transformation
Behind the Shadow ai There is a tension between control and innovation. To refuse to understand this phenomenon is to confirm a silent risk, when it intelligently structures it opens the way to a secure, pragmatic and above all creator of value.
Each clandestine use is a signal to hear and can become an improvement track.
Rather than repressing, it is better to understand, channel, etse -buy, because basically, Shadow ai is not a threat, but the draft of the future information system, still you have to get down to it.
