What criteria should I use when evaluating and shortlisting AI usage control vendors?

Start with coverage, then test the depth of that coverage. The platform should show AI use in browsers, desktop apps, IDEs, command-line tools, local models, and agent workflows. For each surface, ask whether it can see only the application or domain, or whether it can also understand prompts, files, account type, model provider, tool calls, and actions. Next, test the control model. Useful options include monitoring, coaching, requesting justification, redaction, and blocking. Policies should account for user, data, tool, account, and action rather than treating every visit to an AI domain the same. Review classification accuracy, latency, deployment method, retention, role-based access, masking, and employee privacy. Finally, ask the vendor to label every capability as discovery, evidence, or inline control. That distinction exposes broad claims that rely on an API or log source that cannot stop a risky action.

How do I evaluate whether our current security stack can show how employees use AI and control the riskiest activity?

It depends on the question you need the stack to answer. SASE and secure web gateways can identify traffic to known AI domains. Endpoint tools can show which applications and processes run. Traditional DLP can find known patterns, and GRC platforms can store policies and risk decisions. Those controls remain useful. The gap appears when the team needs to understand what work an employee or agent is trying to complete, what sensitive information is present in context, whether the account is personal or corporate, or what an agent is about to do. Test the stack against real workflows in ChatGPT, Claude, Copilot, desktop apps, IDEs, CLIs, embedded AI features, local models, and MCP. If the evidence stops at domain, process, or file label, add an AI-specific control layer rather than trying to replace the existing stack. Related: How Harmonic compares with adjacent security categories

Can AI usage control work without routing employee traffic through a network proxy?

Yes. A network proxy is one control point, but it is not the only way to see or govern workforce AI. Browser extensions can inspect interactions and account context in web tools. Endpoint agents can discover native applications, IDEs, CLIs, local models, and inference calls. OpenTelemetry integrations can add evidence from supported applications. A local MCP gateway can observe and control supported tool calls. Harmonic Connectors, currently in early access, can add visibility and controls for supported cloud connector paths. No single collection method covers every surface or provides the same level of control. A log or API may provide useful visibility after an event but no inline enforcement. Device controls can act before data leaves the endpoint, but they need managed deployment. The evaluation should map each important AI workflow to its collection method, evidence depth, and available action. The right architecture may combine device, application, and network controls.

Agent-based vs proxy-based AI data security: which approach has lower operational overhead at scale?

A proxy provides a centralized control point for traffic that passes through it. It can be a natural fit when an organization already routes managed web traffic through a SASE platform. Its limits are the workflows that never cross that point, including local models, some native applications, local MCP servers, and activity on unmanaged devices. A device-based approach can combine a browser extension with an endpoint agent, extending coverage across browser, desktop, developer, and local agent activity. It requires deployment and lifecycle management through tools such as Intune, Jamf, or Kandji. Teams should test CPU and memory use, update behavior, browser support, and the depth of visibility for each application. Neither architecture should be judged from a diagram. Run representative workflows and compare what each approach records, what it can stop, and what the employee experiences when a policy fires.

What is the difference between AI governance, AI usage control, and AI data security?

AI governance defines who may use AI, for which purposes, with what data, and under whose oversight. It includes policy, risk acceptance, vendor approval, accountability, and evidence for audit or regulation. AI usage control applies those decisions while people and agents work. It can monitor, coach, request justification, redact, or block an interaction based on its context. AI data security focuses on preventing sensitive information from entering an inappropriate model, account, connector, or action. The three functions overlap but are not interchangeable. A governance platform may record an approved use case without seeing daily activity. A data control may stop a secret without showing whether the tool creates business value. A complete program connects the policy record to real usage, enforcement, and outcomes. Buyers should ask which layer each product covers and how evidence moves between them.

How should a CISO build an AI usage control program from scratch?

Begin with discovery. Build an inventory of applications, accounts, teams, use cases, sensitive data, and agent connections. Separate approved, monitored, and prohibited use. Review actual workflows before selecting controls, because an org chart or license list will not show how employees complete the work. Assign decision rights across security, IT, privacy, legal, procurement, and the AI committee. Write a short acceptable-use policy that employees can apply at the moment of work. Start controls in monitor mode, validate detections, then introduce coaching and blocking for high-confidence cases. Give employees an approved path and a way to explain or challenge an intervention. Review metrics monthly: tool adoption, risky data sharing, policy actions, repeat behavior, high-value use cases, and gaps in coverage. Expand the program as new desktop tools, agents, connectors, and local models appear. Related: AI usage governance

What reporting and audit trails should an AI usage control platform provide?

The platform should record the application, account type, user or agent, time, policy, data category, action, and outcome for each material event. For agents, include the server, tool, requested action, and whether the action was allowed, coached, redacted, or blocked. The record should distinguish observed activity from an enforced control. Teams also need evidence of the program, not only incident logs. Useful reports include the AI application inventory, approved and unapproved use, policy coverage, intervention volumes, employee responses, repeat behavior, exceptions, retention settings, and access history. Role-based access, masking, employee exclusions, regional hosting, and configurable retention should support privacy and compliance obligations. Export and API access matter when evidence must move into a SIEM, case system, GRC platform, or regulator response. Ask to see a sample audit package during evaluation.

How can IT teams enforce AI data policies without creating friction that drives employees to workarounds?

Use controls that match the risk and explain what the employee can do next. Monitoring may be enough for an approved tool and low-risk data. A warning can help when someone selects a personal account, uses an unreviewed tool, or shares information that needs a safer path. Ask for justification when context matters. Reserve blocking for clear policy violations or high-impact data. The intervention should identify the problem in plain language and point to an approved account, tool, or workflow. Track whether employees accept, override, or repeat the behavior. Repeated workarounds often signal that the sanctioned tool does not support a real use case. Security can use that evidence to adjust policy, improve enablement, or approve a better tool. Blanket blocking removes visibility when employees move the work elsewhere, so it should be the exception rather than the program design.

What new data security risks does agentic AI introduce compared to standard AI tool usage?

A standard chat interaction usually returns text to a person. An agent can read files, call tools, query systems, change records, send messages, run code, or trigger another service. A mistake or malicious instruction can therefore move from a bad answer to an operational action. The risk depends on the data the agent can reach, the permissions attached to its identity, the tools it can invoke, and whether a person reviews the result. Agents also create longer chains of evidence. A prompt may lead to a model decision, an MCP tool call, a connector request, and a write action. Security teams should inventory agents, servers, tools, permissions, data paths, and actions. Apply least privilege, separate read from write operations, require approval for high-impact actions, inspect sensitive data in tool-call payloads, and log the full chain for investigation. Related: AI agent security

How can security teams discover which AI agents employees are using and what data and actions those agents can access?

Use more than an agent registry. Start with device discovery for desktop clients, IDEs, CLIs, local models, and MCP servers. Add supported application telemetry and, where available, early-access Harmonic Connectors for supported cloud workflows. For MCP workflows, record the client, server, tool, user, data passed into the call, requested action, and result. Tie each event to the underlying model provider where possible. The inventory should answer both posture and runtime questions. Posture shows which agents and tools exist, how they are configured, and what they may access. Runtime evidence shows what they actually did. A platform that reports only installed software or registered identities cannot explain a sensitive tool call. A platform that sees only prompts may miss the resulting action. Test discovery with a representative local agent, a cloud agent, a read action, a write action, and a blocked policy case.

How do you enforce data policies when AI agents are taking actions autonomously rather than on user prompts?

Place controls at the points where an agent receives data and attempts an action. Policies can evaluate the user, agent, server, tool, action, data, destination, and business context. A read of a public knowledge base may be low risk. Deleting a repository, sending customer data to a third party, or changing a financial record may require approval or a block. Separate discovery from enforcement in the architecture. Logs and traces can show what happened, but they do not automatically prevent it. Inline control needs a supported gateway, connector, or application control point. Begin with observe-only policies, verify the action taxonomy, then add human approval or blocking to high-impact cases. Preserve the reason for each decision and allow the safe parts of a workflow to continue when the control point supports it. Review policies as agents gain new tools and permissions.

How should security teams approach protecting data in agentic AI workflows powered by tools like MCP?

Inventory MCP clients, servers, and tools first. Record which user and process initiated each call, what data entered the payload, which destination received it, and what action the tool attempted. Apply allowlists to approved servers and tools, validate server provenance, limit credentials, and separate read permissions from write permissions. Inspect the content of tool calls for sensitive data and policy context. A tool name alone does not reveal whether the payload contains source code, credentials, customer records, or confidential business information. Use a local gateway or another supported inline control when the organization needs to stop a call before it executes. Retain enough evidence for investigation without keeping unnecessary employee content. Test controls against prompt injection, excessive permissions, unapproved servers, data exfiltration, and destructive actions. MCP is a protocol, so security still depends on the client, server, permissions, and control point around it.

AI Security Answers Library | Harmonic Security

02Data

Data protection

Protecting sensitive data in AI

AI interactions are short, unstructured, and often context-dependent. A useful control must understand what the employee is doing, not only match a string.

Which sensitive data types should an AI usage control platform detect in employee prompts, uploads, and AI agent workflows?

The starting set should include personal data, health information, payment data, credentials, secrets, source code, customer information, confidential deal terms, contracts, financial plans, security findings, internal strategy, and regulated records. The list should reflect the organization’s actual work rather than a generic taxonomy.

Detection also needs context. A customer name may be harmless in an approved enterprise workspace and risky when combined with pricing, renewal terms, and a personal AI account. Source code may be acceptable in an approved coding assistant but not in an unreviewed model or agent connector. During evaluation, use realistic prompts, pasted text, and uploaded files. Include paraphrased and unstructured content, because the riskiest material rarely arrives as a clean identifier that a regular expression can catch. The policy should combine data meaning with tool, account, user, destination, and intended action.

Why is traditional DLP failing to protect data shared with generative AI tools?

Traditional DLP is strongest when sensitive data has a recognizable pattern, label, or fingerprint. AI prompts often contain short excerpts, summaries, code, or business context that is sensitive because of its meaning. A phrase such as “the customer will accept 18 months at this price” may reveal a live negotiation without containing a card number or other fixed identifier.

Static rules can therefore miss risky interactions or trigger on harmless ones. AI-specific controls should classify the intent and meaning of the interaction, then combine that result with the application, account, user, and action. Existing DLP still matters for structured identifiers and established data policy. The practical approach is to keep those controls and add contextual inspection where employees and agents interact with AI. Test this with your own difficult examples, including source code, deal information, internal plans, and mixed documents.

How can we prevent developers from pasting source code or credentials into AI coding assistants?

Cover the developer workflow before writing a policy. That means the browser, desktop clients, IDEs, plugins, command-line tools, local models, direct inference APIs, and agent or MCP calls. Identify approved tools and accounts, then inspect prompts, pasted text, file uploads, and tool-call payloads for secrets, credentials, proprietary code, and sensitive repository content.

Use the least disruptive response that fits the risk. A warning can explain why a personal account is inappropriate and direct the developer to an approved workspace. Redaction may work for a small secret. A confirmed credential, protected repository, or high-risk destination may require a block. Record the policy decision and employee response so the team can improve controls. Do not rely on training alone: fast-moving developer tools and agents create new paths that a policy document cannot observe.

How do employees typically expose sensitive data when using AI tools: what does this look like in practice?

Common paths include typing data into a prompt, pasting a document excerpt, uploading a file, using a personal account for work, invoking AI inside an approved application, or asking an agent to read from a connected system. Developers add other paths through IDE assistants, CLIs, local models, plugins, direct model APIs, and MCP servers. Some exposures are deliberate shortcuts, but many happen because the employee does not realize the tool, account, or connector is outside policy.

A useful investigation should answer five questions: who initiated the interaction, which tool and account were used, what data was involved, what the person or agent intended to do, and whether the data or action left the approved boundary. Domain logs alone cannot provide all five. Combine tool discovery with prompt, file, account, and action context, then preserve only the evidence the security and compliance teams genuinely need.

How do we protect sensitive data across both managed and unmanaged devices used to access AI tools?

Managed devices allow the deepest control because the organization can deploy a browser extension, endpoint agent, local gateway, or approved application configuration. Those controls can discover AI use, inspect interactions, distinguish accounts, and intervene before sensitive data is submitted.

Unmanaged devices have different limits. Identity policy, enterprise application settings, provider logs, and supported cloud connectors may provide partial visibility or restrict access, but they do not recreate device-level inspection everywhere. Define which workflows are permitted on unmanaged devices, require managed accounts for sensitive work, and prevent high-risk data from being accessed outside managed environments where possible. Vendor evaluations should state the difference clearly. Ask what is visible on each device type, whether the control is preventive or retrospective, and what evidence is unavailable. Treat “coverage” as a matrix rather than a single percentage.

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05Measurement

Program outcomes

Measuring AI adoption, risk, and value

A useful scorecard connects AI use to business tasks, policy outcomes, and data risk. Raw prompt counts and license totals do not provide that view.

What measurable outcomes should an enterprise expect from an AI usage control program?

The first outcome is an accurate inventory: which tools, accounts, teams, use cases, models, and agents are active. The next outcome to measure is lower exposure. Track sensitive-data events per 1,000 AI interactions, high-risk events by data type and destination, repeat behavior after an intervention, and the share of risky activity that receives an inline control.

The program should also improve safe adoption. Measure movement from personal or unsanctioned accounts to approved services, use of sanctioned tools by team, the business tasks AI supports, policy friction, and where employees abandon or override controls. For agents, track discovered clients and servers, risky tool calls, high-impact actions, and coverage by policy. Do not promise a universal reduction percentage. Establish a baseline, validate the data, and set targets for the organization’s own risk profile, deployment coverage, and adoption goals.

How can companies measure the business value of employee AI adoption by team and use case?

Group AI interactions into the work employees are trying to complete, then review adoption by team, application, account, and time. Useful use cases might include research, drafting customer communications, code assistance, document review, analysis, or internal support. The categories should come from observed work rather than a fixed generic list.

Combine usage evidence with outcome data the business already trusts. Examples include cycle time, throughput, quality, employee feedback, support resolution, content production, or engineering delivery. AI telemetry can show where adoption is concentrated and which tools support the work, but it should not claim productivity from prompt volume alone. Review sanctioned and unsanctioned use together. An unapproved tool with high repeat usage may reveal a valuable capability that the approved stack lacks. That evidence can guide enablement, licensing, and vendor decisions.

How do I quantify the risk reduction from deploying AI usage controls?

Use a before-and-after baseline that accounts for total AI activity. Count confirmed sensitive-data events, high-risk destinations, personal-account use, risky agent actions, and repeat behavior, then normalize those figures by interactions or active users. Track what happened after each policy action: the employee removed the data, switched to an approved account, justified the action, overrode the warning, or was blocked.

Separate better detection from lower risk. Event counts may rise after deployment because the organization can finally see activity that was already happening. Over time, look for lower event rates, fewer repeat events, greater use of approved tools, faster investigations, and wider policy coverage. For high-impact scenarios, estimate exposure using the data type, destination, affected system, and possible consequence rather than applying one average breach cost. Document assumptions so leadership can see what the estimate includes.

How do I build a business case for AI usage control investment to present to the board?

Frame the decision around safe AI adoption, measurable exposure, and operating cost. Show where employees already use AI, which business tasks depend on it, where sensitive data or high-impact agent actions appear, and which parts of that activity the current stack cannot see or control. Use a few verified examples rather than a long list of hypothetical threats.

Compare the proposed control with the cost of the current approach: manual tool reviews, incident investigation, blanket blocking, duplicated licenses, and unmeasured shadow AI. Define the outcomes the investment should produce, such as coverage across priority workflows, fewer repeat data-sharing events, movement to approved accounts, faster evidence collection, and clearer adoption data. State known limits and dependencies. A credible case shows how security enables useful AI work while reducing specific exposures; it does not rely on a single speculative breach number.

Usage Intelligence AI Usage Index AI governance

06Shadow AI

Discovery and response

Managing shadow AI without losing the work

Shadow AI is a visibility and workflow problem. The response should identify the tool, the account, the data, and the reason employees chose it.

What is shadow AI and why should enterprise security and IT teams care about it?

Shadow AI is the use of AI tools, accounts, models, agents, or features that the organization has not approved or cannot adequately see. It includes obvious examples such as a public chatbot on a personal account and less obvious ones such as AI embedded inside a sanctioned application, a desktop assistant, an IDE plugin, a local model, a custom script, or an MCP server.

The risk is not the application name alone. Security needs to know what work the employee or agent performed, what data was shared, which account and model were involved, and what action followed. An unapproved tool may support a legitimate use case that approved tools do not cover. Discovery should therefore lead to a decision: approve the workflow, move it to a safer service, add a guardrail, or prohibit it. A domain block without that context can push the same work to a less visible device or account.

What is the risk difference between blocking shadow AI entirely vs monitoring and controlling it?

Use a graduated response. Monitor low-risk activity while the team learns which tools and use cases matter. Coach employees when an approved alternative exists, when they use a personal account, or when a safer workflow would solve the same need. Apply a block when the destination, data, or action creates a clear and unacceptable risk.

The decision should combine tool risk, account type, data sensitivity, user role, business purpose, and action. A public prompt with no sensitive data is different from uploading customer records to a personal account or letting an agent change a production system. Measure the effect of each control. If employees repeatedly encounter the same warning or move to another tool, investigate the missing capability or confusing policy. The goal is to keep useful work visible and within an approved path while stopping the interactions that create real exposure.

Do in-product warnings and coaching reduce risky employee AI behavior over time?

They can, but the effect should be measured in each environment. The strongest warnings arrive before submission, describe the specific issue, and give the employee a workable alternative. A generic policy banner is easy to ignore. A useful intervention might explain that the current session uses a personal account, identify the sensitive information in the prompt, and link to the approved enterprise workspace.

Measure whether the employee changes the interaction, switches accounts or tools, submits a justification, overrides the warning, or repeats the behavior later. Compare outcomes by policy, team, tool, and data type. Coaching is appropriate when the employee can correct the action safely. Blocking is appropriate for high-confidence, high-impact events. Start in monitor mode, validate detections with real workflows, then introduce controls gradually. Poorly tuned warnings create fatigue and workarounds, so false positives and employee feedback should be part of the operating review.

How can I understand what my team is doing with AI across tools, accounts, and use cases?

Combine application discovery with identity and task context. The inventory should include browser tools, embedded AI features, desktop applications, IDEs, CLIs, local models, agents, and direct model connections. For each supported interaction, identify the user, application, model provider, account context, approval status, and relevant data risk.

Raw events are not enough for planning. Group related interactions into tasks and use cases, then report by department, employee, application, approval status, and time. This lets security find risky exposure while IT sees which tools support valuable work, where licenses overlap, and why employees choose unsanctioned services. Apply role-based access, masking, exclusions, retention limits, and personal-conversation filtering so the analysis supports the program without becoming broad employee surveillance.

How do I detect when employees are using personal AI accounts instead of corporate ones on company devices?

Account-aware controls use the account and application context that an AI service exposes. In supported browser workflows, that can distinguish personal use from a managed enterprise workspace and apply a different policy to each. Identity and provider logs can add evidence for supported managed services.

Test this by using the same application with both account types. Confirm whether the platform can identify the session, apply different rules, and record the decision. The control should avoid treating every use of a popular service as equally risky. An organization may allow general work in the corporate instance, warn when a personal account is used, and block sensitive data only when it crosses an unapproved boundary. Some applications and surfaces expose less account detail than others, so document coverage by workflow rather than assuming universal detection.

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A practical reference for workforce AI security

Start with the decision you need to make

Evaluating AI usage control platforms