Shadow AI: The new gray cybersecurity area

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 to their use strengthen this dynamic. According to several internal studies carried out by cloud players, more than 60 % of professional users already use generative AI tools without formal supervision.

A systemic risk by accumulation

This fragmentation of uses creates a blind spot in the safety strategy of companies. The risks are not only technical (data exfiltration, persistence in models, attacks by prompt injection). They are 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 a Cumulative attack surface. Each non -framed use weakens the organization’s digital trust base.

Weak signals with strong consequences

Unlike Shadow itwhich focused on identifiable tools (Dropbox, Trello, Slack, etc.), the Shadow ai is often 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 diffuse: content generated without stylistic coherence, non -documented automated decisions, suspicious files in collaborative tools. These indices must now be the subject of active and structured monitoring.

Regain control: a three -part strategy

Faced with this silent proliferation, organizations must build a three -level response:

1. Visibility : Setting up IA use detection tools (logs analysis, API flow inspection, content audits), in connection with business teams.
2. Management : Establish a clear policy for the use of IA tools, including white lists, validation procedures, and internal training.
3. Accompaniement : Offer secure internal alternatives (owner agents, validated co -pilotes, partitioned environments) to meet the demand for innovation without exposing the company.

Expanded responsibility

THE Shadow ai does not only be cybersecurity. It must be treated as a Global governance subjectinvolving 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 challenge is not to slow down the adoption of AI, but to channel its uses in a framework of confidence. Ignore it Shadow aiit is taking the risk of seeing a technological and regulatory debt emerge as invisible as exposed.

Shadow it vs shadow ai: what differences?