Cybersecurity Leadership in the AI Era

Start with an inventory of actual AI use

Ask teams where they use AI tools, including browser assistants, meeting transcription, coding support, and features embedded in existing software. Record the business purpose, data involved, provider, and owner. A policy cannot protect a workflow the organization has not identified.

Distinguish experimentation from production use. A tool connected to customer records or able to trigger business actions needs a different review from an isolated exercise with non-sensitive data.

Protect the data boundary

Review what information leaves your environment, where it is processed, how long it is retained, and whether the provider may use it to improve its services. Verify these points in the agreement and the actual account settings; do not assume an enterprise label answers them.

Restrict access to connected documents and applications. An assistant should not reveal information that the person requesting it could not otherwise access. Retrieval and integration layers need permission checks, logging, and testing.

Plan for untrusted inputs and outputs

AI systems can treat malicious instructions hidden in documents, webpages, or messages as directions. This is commonly called prompt injection. A warning in the prompt is not a complete security boundary. Limit tool permissions, isolate untrusted content, and require approval before sensitive actions.

Generated code, summaries, and recommendations should be checked before they influence production systems or important decisions. Test for inaccurate outputs, disclosure of restricted information, and inappropriate use of connected tools.

• Limit integration permissions to the task.
• Require approval for consequential actions.
• Log access and actions without unnecessarily storing sensitive prompts.
• Test failure cases and document the fallback process.

Keep incident response and leadership connected

Extend incident procedures to cover AI-assisted data exposure, unauthorized actions, and vendor outages. Identify who can disable the tool, revoke its access, preserve relevant evidence, and notify the appropriate internal stakeholders.

For executive reporting, connect AI risks to familiar business outcomes: confidentiality, service continuity, financial loss, and decision quality. Report whether controls work, not merely how many AI tools have been reviewed.