The shadow of the IA the invisible threat that breaks your security and compliance controls

Every time a worker uses an artificial intelligence tool to answer a mail, review a contract or purify a code fragment without passing through official channels, an invisible barrier is being raised between the organization and its security controls. This phenomenon, commonly called Shadow AI, is not simply the modern version of the well-known Shadow IT: it incorporates systems that process, generate and in many cases can store sensitive information outside the perimeter that security teams think they manage.

The adoption of these tools is understandable: many IA solutions require little or no deployment, are intuitive and offer an immediate benefit in productivity. However, this ease of use is also its main risk. When an employee uses a conversational assistant to get formulas or paste fragments of documents, the information can leave the company without traceability. Depending on the supplier and the type of account, such data could even be incorporated into the model training processes, thus further dilute control over its destination. In order to understand the magnitude of legal and regulatory implications, it should be recalled that unchecked data transfers may conflict with frameworks such as the GDPR or the US regulations on health privacy, explained by the Department of Health and Human Services on its website on HIPAA.

The problem takes on several faces. First, visibility: many IA platforms communicate by HTTPS, which prevents traditional controls from inspecting content unless there is a TLS / SSL inspection infrastructure, a measure not all organizations have deployed. Second, the attack surface: improvised integration with external APIs or unaudited plugins can open up exploitable gaps by attackers. And third, identity and access: employees create personal accounts, developers stick keys in public chats or link service accounts to IA agents, generating what can be called non-human identities that do not go through the usual governance processes.

These challenges are not theoretical. Regulatory and cybersecurity organisations have begun to warn about the risks of generative IA and the need for specific controls, for example, the EU has already launched the IA Act to regulate higher-risk uses, and agencies such as CISA provide guidance on how to incorporate safety practices against IA. In addition, the principles of identity recommended by the NIST remain relevant in facing multiple identities distributed between human users and automatic agents.

Against this background, the temptation to adopt a policy of total prohibition of external tools is great, but it is often ineffective. When the rules are too rigid or safe alternatives are not available, employees simply seek shortcuts. Instead of trying to completely close the phenomenon, organizations that better manage the risk accept that some adoption of IA will be inevitable and guide their efforts to recover visibility, control data flow and govern identities.

This change of approach involves a number of working lines: establishing clear and practical use policies that indicate what type of data can be shared with external tools; providing internal or approved solutions that meet the actual needs of the equipment; improving the monitoring of traffic and activity in APIs to detect abnormal patterns; and training the staff in specific hazards - such as not giving credentials, not uploading customer lists or avoiding sharing financial information - to ensure that daily decisions are made with judgement. Education is decisive: many leaks are accidental, they are born from the illusion that IA is a simple "glue" for recurring tasks and not from malicious intent.

Manage Shadow AI also requires adaptation of identity governance. When tools are integrated into workflows through service accounts, it is essential that these non-human identities go through the same cycles of creation, review and revocation as human accounts. Apply the principle of minimum privilege, audit access and keep an unaltered record of who, when and with which tool interacted with a critical resource significantly reduces the exposure window.

The advantages of taking these measures are clear: greater control over which tools are in use and what data they handle, lower risk of incidents requiring regulatory notification, and faster and safer adoption of approved technologies. In addition, when the security team provides useful alternatives and simple processes, the willingness of employees to use unmanaged solutions is reduced.

It is not just about technology: it is a cultural and organizational exercise. The companies that make up the IA safely do so through a continuous dialogue between business teams, developers and security, offering clear policies, practical training and tools that facilitate work without compromising data protection. In this sense, there are products on the market that help to control privileged access and to chart the activity of identities, humans and machines alike; to know these options and to align them with regulatory and operational requirements is part of the response.

The work landscape has changed: the IA is integrated into many daily tasks and will continue to expand. Assuming that its unapproved use can be eradicated is unrealistic. The effective alternative is to accept reality, understand blind points and deploy controls that allow responsible and auditable use of the IA. Only in this way will organizations be able to benefit without sacrificing security or exposing themselves to unexpected legal problems.

In order to deepen regulations and recommendations on privacy and security related to the processing of data and new technologies, official resources such as the data protection in the EU, the page of HHS on HIPAA the text and follow-up to the EU AI Act, the identity guide of the NIST and cybersecurity resources of the CISA. If you are looking for specific solutions for the management of privileged access and identity control that help mitigate risks associated with IA agents, suppliers such as Keeper Security They offer tools aimed at auditing and restricting access in hybrid environments where humans and machines live together.