AI Security Controls to Require Before Expanding Use

Before expanding AI use, an organization should require documented controls for data protection, identity and access, vendor and model risk, secure integration, monitoring, and incident response. Treat each AI use case according to the sensitivity of its data and the consequences of its output, and approve expansion only when safeguards are tested and accountable owners are in place.

Why AI changes the security picture

AI systems can introduce familiar risks—such as account compromise, data leakage, vulnerable software, and supplier exposure—in less familiar ways. A user may paste confidential material into a public service; a connected assistant may retrieve documents its user should not see; or a model may produce a convincing but incorrect answer that influences an important decision.

The word “AI” covers very different deployments. A public chatbot, an internally hosted model, an AI feature embedded in a business application, and an agent that can send messages or change records do not have the same exposure. Controls should therefore follow the use case, the data handled, the people affected, and the system’s ability to take action—not merely whether a product is labeled AI.

Use an established risk-management approach rather than inventing a separate, disconnected security program. Frameworks such as the NIST AI Risk Management Framework can help organize governance, measurement, and risk treatment; conventional security practices remain essential. A framework is a guide, not proof that a particular deployment is safe.

Start with an inventory and approval process

An organization cannot protect systems it does not know are in use. Maintain an inventory of approved AI products, models, integrations, and material use cases. Include employee use of general-purpose tools where feasible, not only centrally purchased software. Define a clear route for requesting new use, and explain which services or data uses are prohibited.

For each proposed use, record information that supports a proportionate review:

• Purpose, business owner, users, and affected parties.
• Data entered, retrieved, generated, or retained, including whether it is personal, confidential, regulated, or publicly available.
• Model and service provider, hosting arrangement, connected tools, and data flows.
• Whether outputs are advisory or can trigger actions, and how a person reviews them.
• Potential impact of inaccurate, biased, unavailable, or exposed outputs.

Require security, privacy, legal, and relevant operational review when the use case warrants it. Define approval conditions and a reassessment date; significant changes to a model, data source, integration, or purpose should trigger another review.

Protect information and access

Apply existing data classification and handling rules to AI inputs and outputs. Employees should know what they may submit, where it may be processed, and whether the provider can retain it or use it to improve services. Do not assume that a “business” or “enterprise” label automatically establishes suitable data protections; check the actual service terms, settings, and contract.

• Minimize data sent to a model. Remove unnecessary identifiers and confidential details, or use synthetic or de-identified data when appropriate.
• Use approved services and centrally managed accounts. Set retention, sharing, and training options deliberately and verify them.
• Apply least privilege to users, service accounts, connectors, and retrieval sources. An AI assistant should not gain access to more information than its users and task require.
• Use strong authentication, role-based access, and prompt removal of access when roles change or employment ends.
• Protect logs and outputs too: they can contain sensitive prompts, retrieved records, or generated personal information.

Redaction is not a universal guarantee: context can still identify a person, and a provider may process data in ways that depend on its configuration and contract. Document what protections are actually in effect.

Review suppliers, models, and integrations

Assess AI providers as suppliers with access to data and business processes. Ask where data is processed, how long it is retained, who can access it, whether it is used for model training, how deletion works, and how the provider reports security incidents and material service changes. Review independent assurance and security documentation where available, while recognizing that a certification or audit report covers a defined scope and period.

Map the full chain of dependencies: the model provider, hosting environment, plugins, retrieval system, and any external tools. Secure APIs with scoped credentials, secret storage, rate limits where appropriate, and monitoring. Avoid giving an agent broad permissions merely because a demonstration works better that way. For actions such as issuing refunds, changing access, or sending external communications, require human confirmation or other narrowly designed safeguards proportionate to the impact.

Contracts and technical controls should align. A promise about deletion is of limited value if the organization has no way to identify its data or verify account settings. Conversely, a technically secure platform does not settle questions about permitted data use or supplier obligations.

Test behavior, monitor use, and prepare for incidents

Test the system in conditions resembling actual use before launch and after meaningful changes. Evaluate ordinary performance as well as security-relevant behavior: whether users can retrieve unauthorized information, whether untrusted content can steer a connected system, and whether the model exposes sensitive details in responses. Prompt injection—malicious instructions embedded in user input or retrieved content—is one reason to treat model instructions and retrieved text as untrusted, particularly when tools are connected.

Testing reduces uncertainty but cannot prove that every possible input is safe. Combine it with operational controls:

• Log access, configuration changes, tool calls, and relevant security events, while limiting sensitive content in logs and controlling log access.
• Monitor for unusual usage, unexpected data movement, excessive privileges, and changes in provider or model behavior.
• Provide a way for users to report unsafe outputs, suspected disclosure, or unexpected actions.
• Define how to disable an integration, revoke credentials, preserve evidence, notify the right teams, and assess affected data.

Include AI systems in incident-response exercises. A response may involve the provider, internal IT and security teams, privacy personnel, and the business owner. Maintain a fallback process so critical work can continue if an AI service is unavailable or suspended.

Set human oversight and expansion gates

Human review should be meaningful, not a box-ticking step. Reviewers need sufficient context, training, and authority to reject or correct an output. They should not treat fluent text as evidence of accuracy. For consequential decisions, establish which decisions cannot be delegated, what must be independently verified, and how users can challenge or escalate an outcome.

Expand in stages: a limited pilot, a review of observed problems, then a broader rollout with explicit approval. Before each stage, verify that access controls, data rules, testing, monitoring, incident procedures, and user training are working. Set measurable criteria tied to the use case, such as acceptable error handling or successful access-control tests; do not rely on a single generic “AI accuracy” score.

Keep a record of owners, approvals, known limitations, and changes. Pause or roll back a deployment when controls fail, the purpose changes materially, or an incident reveals risks that were not addressed. Expansion should be earned by evidence, not driven solely by adoption targets.

Frequently Asked Questions

Can employees use public AI tools with company information?
Only if the organization has approved the service and its data handling for that information. Until then, employees should not submit confidential, personal, or otherwise restricted data; a public interface may process or retain inputs under settings and terms that differ from internal systems.

Does a private or enterprise AI service prevent data leaks?
No. It may offer stronger administrative or data protections, but configuration errors, excessive permissions, insecure integrations, compromised accounts, and unsafe user practices can still expose information.

Is a human reviewer enough to make AI output safe?
No. Review helps only when people can understand the task, verify important claims, and stop an action. Combine it with access restrictions, testing, monitoring, and clear accountability.

How often should AI security reviews be repeated?
Set a review schedule based on risk and repeat the assessment after significant changes—for example, a new data source, expanded permissions, a different model, or a shift from advice to automated action. Also review after relevant incidents.

What is the most important control for an AI agent?
There is no single control that fits every agent. Start by limiting its permissions and available tools to what the task requires, then add approval for consequential actions, monitoring, testing, and a reliable way to revoke access.

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