Every organization already has a working AI policy, whether it has written one or not. In many cases, the policy is simply whatever employees decide is acceptable when they need a quick answer late in the day.

A contract is pasted into a chatbot for a summary. An AI assistant is connected to an inbox. A software vendor enables a new AI feature inside a product the organization already uses. None of these decisions necessarily looks like an AI program, but together they define one.

Verizon’s 2026 breach report says frequent employee use of AI tools rose from 15% to 45% in one year. The immediate challenge is not to stop useful experimentation. It is to understand where sensitive data, permissions and business decisions are now involved.

Where existing controls start to strain

Shadow AI. Employees can create accounts with a work email and begin using a service before IT or legal knows it exists. Once information is submitted, retention and reuse may be outside the organization’s control.

Data exposure in both directions. Sensitive information can leave through prompts, while AI connected to internal repositories can return information to someone who should not have access to it.

Prompt injection. Instructions embedded in an email, document or web page can influence an AI system in ways the user did not intend. OWASP lists prompt injection among the leading risks for applications built with large language models.

Over-permissioned agents. An agent that can send messages or change records should not automatically receive broad access. The permissions, approvals and rollback options matter as much as the model.

Unchecked decisions. If AI output flows directly into hiring, lending, claims or eligibility decisions, errors can reach customers before anyone reviews them.

Six guardrails worth putting in place now

1. Find the real footprint. Inventory standalone tools, AI features built into existing platforms and projects being developed internally. The first version will not be perfect, but it gives the organization something concrete to govern.
2. Publish a short rule that people can use. Name the approved tools, the data that must stay out and the place to ask questions. A practical one-page guide is more useful than a policy employees cannot apply.
3. Classify sensitive information. AI cannot respect handling expectations that have never been defined.
4. Limit access. Give AI systems and agents only the permissions needed for the specific task. Require human approval before consequential actions.
5. Choose the review points. Customer, financial, safety and compliance decisions need a named human reviewer.
6. Keep records. Log access and actions so that incidents can be investigated and controls can be demonstrated.

Use the governance structure you already have

Most organizations do not need a separate AI bureaucracy. NIST AI RMF organizes the work around Govern, Map, Measure and Manage. ISO/IEC 42001 can sit alongside an ISO 27001 management system. The harder part is assigning responsibility across security, privacy, legal, technology and the business, especially when adoption is already underway.

Where to start

SVAM builds AI solutions and works with organizations to secure their use. Our AI risk assessment identifies where AI is in use, where sensitive data or excessive permissions may be involved and which guardrails deserve attention first. The Cyber Check includes an AI question, and a review can be scheduled at svam.com/cybersecurity-awareness-month/.