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Choose the platform that can prove, in your own configuration, who can access customer data, what the AI can retrieve or change, and which evidence is retained when something goes wrong. Start with a control-and-investigation checklist, then test every finalist with realistic roles, AI workflows, log queries, exports, retention requirements, and the exact plan quoted to you. An “audit log” label alone does not establish that every AI action or customer-data view is recorded.
Start with the questions your platform must answer
Security and compliance teams usually need two different kinds of evidence. A configuration audit trail answers, “Who changed a setting, permission, workflow, or integration?” An access log answers, “Who viewed or searched a customer record?” Those records may live in separate products, have different retention periods, and require different subscriptions.
Zendesk’s documentation illustrates the distinction: its account audit log tracks changes to account settings or fields, while its separate access log covers access to tickets, user profiles, and searches. The account audit log does not capture end-user activity. Treat those as examples of scope, not as a universal product model.
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| Question | Control or evidence to verify | Why it matters |
|---|---|---|
| Who can administer the service? | Role definitions, delegated administration, approval for privilege changes, and an event showing the change | Prevents broad administrative access and supports investigations |
| Who can read customer records? | Object-, record-, field-, and search-level permissions, including API and integration identities | Limits exposure of personal and confidential data |
| What can the AI do? | Allowed data sources, actions, approval gates, refusal behavior, and an event trail for prompts, retrieval, responses, actions, overrides, and errors where required | AI activity can cross the boundaries of a normal support-console session unless it is explicitly constrained |
| Can an investigator reconstruct an incident? | Actor identity, timestamp and timezone, object and action, before/after details, searchability, retention, and export | A log that cannot be queried or preserved is weak evidence |
| Is the feature actually purchased? | SKU, edition, add-on, API entitlement, data-region terms, and contract language | Demonstration environments often include capabilities absent from the quoted plan |
1. Define the control boundary before comparing vendors
Inventory data and connected systems
List every customer record the service can reach: tickets, profiles, attachments, knowledge bases, messaging histories, billing data, and any connected CRM or data warehouse. For each source, record whether the platform can read, create, update, delete, export, or search it. Include test and production environments; a connector that appears harmless in a demo may expose a much broader production dataset.
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Inventory identities as well. Your list should include support agents, supervisors, administrators, developers, automation workers, webhooks, service accounts, and third-party integrations. A human’s console permissions do not automatically describe an integration’s permissions.
Write down permitted AI actions
Separate low-risk assistance, such as drafting a reply, from consequential actions, such as changing a refund status, closing a case, editing a customer profile, or sending a message externally. For every action, specify whether the AI may perform it autonomously, must request approval, or must be blocked. Define what happens when the user lacks permission, a field is masked, a connector is unavailable, or the model produces an uncertain result.
Apply least privilege and test removal
Give each role only the access required for its duties. Create a test user, grant a permission, confirm the intended operation, then remove or change the permission and verify that both direct access and AI-assisted access stop working. Zendesk’s security guidance recommends limiting administrator access and using role controls; use the same principle for every finalist, regardless of its terminology.
2. Build an event matrix instead of accepting a generic “audit log”
Give each finalist the same event list and require a written answer for every row. Ask whether the event is recorded, which fields are present, when it becomes visible, who can query it, how it is exported, and how long it remains available. “Supported” should mean demonstrated in your configured trial, not merely shown in a feature list.
| Event family | Examples to test | Evidence to request |
|---|---|---|
| Configuration and permissions | Role creation, permission changes, workflow edits, policy changes, connector configuration, and deletion | Actor, time, IP or source, object, action, and before/after values |
| Identity and authentication | Invitations, deactivation, password or authentication changes, failed sign-ins, session events, and service-account changes | Stable identity, timestamp and timezone, source address or device where available, and outcome |
| Record access | Opening a ticket or profile, searching, viewing a sensitive field, downloading an attachment, and bulk access | User or service identity, record or query scope, time, and whether the event covers API and integration access |
| AI processing | Prompt, retrieved records, generated response, tool call, write action, approval, override, refusal, and error | Correlation ID tying the user, model run, data sources, action, and result together |
| Exports and integrations | CSV or API exports, webhook delivery, synchronisation changes, and system-initiated updates | Initiator, destination, scope, volume, result, and retry or failure details |
This matrix is a buyer requirement, not an assertion that every platform records all of these events. If an event is not captured, document the compensating control, such as an identity-provider log, database audit stream, approval record, or network monitoring system.
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3. Test whether AI follows the signed-in user’s permissions
Create realistic test personas
At minimum, create a least-privilege agent, a standard agent, a supervisor, an administrator, and a service identity used by an integration. Give each persona deliberately different access to records, fields, queues, and actions. Include a sensitive field that ordinary agents cannot see and a record belonging to a different team.
Run direct and AI-assisted tests
- Sign in as the least-privilege agent and attempt to open a restricted record directly.
- Ask the AI a question whose answer would require that restricted record. Confirm that it refuses, returns only permitted information, or clearly identifies the missing authorization.
- Ask the AI to perform a restricted update. Verify that it cannot bypass the user’s permission through a tool, connector, or background workflow.
- Repeat the tests through the API, mobile experience, automation, and any service account used in production.
- Remove one permission and rerun the tests. Check that cached context, conversation history, search indexes, and generated summaries do not continue to expose the data.
Salesforce describes controls intended to limit an agent to data the current user is authorized to see and to restrict datasets or fields used in AI workflows. It also describes deterministic controls for sensitive actions and an audit trail. These are vendor-described capabilities, so require a demonstration using your own objects, fields, roles, and actions rather than treating the statement as a guarantee for every configuration.
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Require identifiable, complete records
A useful entry identifies a person or service identity, the exact time and timezone, the action, the affected object, the result, and the origin. For changes, capture before-and-after values or a precise change description. For AI workflows, require a way to connect the user request, retrieved data, model run, tool call, approval, and final write action. Ask how the system handles shared accounts, renamed users, deleted users, and rotating service credentials.
Zendesk’s account audit-log documentation describes fields such as time, actor, IP address, and activity details. Its developer documentation identifies actor and change-description fields and states that its audit-log endpoints are Enterprise-only. Confirm the current field set and entitlement in the version you are buying.
Run a tabletop investigation
- Change a sensitive permission with a test administrator.
- View a disputed customer record as two different roles.
- Run an AI query that retrieves permitted data and attempt one that should be blocked.
- Approve an AI-generated action, then deliberately cause a tool or connector error.
- Ask an investigator to identify the actors, times, records, decisions, and outcomes using only the product’s log interface and export.
Measure the time required, the number of systems involved, and any ambiguity. Verify that filters, pagination, sorting, saved searches, and correlation identifiers work at the volume you expect. A log that contains the right fields but cannot be searched during an incident is not an adequate control.
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5. Verify retention, export, and monitoring integration
Retention is event-specific
Ask for the retention period for configuration changes, record access, AI activity, exports, and authentication events separately. A platform may retain one class for years and another for weeks. Zendesk documentation describes a 90-day window for the access events captured by its access log. That is a product specification for the documented capability, not an industry norm; confirm the current SKU, region, and configuration before relying on it.
Test the export path
Determine whether investigators can export filtered results through the interface, an API, a scheduled job, or a streaming integration. Test large result sets, pagination, rate limits, timestamps, deleted identities, and attachments. Confirm that exports preserve the original event identifiers and cannot be altered without detection. Zendesk documentation describes API and export options; test the exact endpoint, permissions, and history available in your proposed plan.
Connect monitoring systems deliberately
Ask whether events can be delivered to your SIEM, data lake, ticketing system, or webhook receiver in near real time. Verify delivery guarantees, retries, ordering, duplicate handling, encryption, and the identity used to deliver events. If the platform cannot stream a required event, define a compensating control and document the delay an investigation may experience.
6. Map every requirement to the quoted plan and contract
Build a one-page entitlement map that names the exact edition, add-on, API level, environment, region, user or event limits, and contract term for each required control. Have the vendor sign or otherwise confirm the map. Do not infer entitlement from a sales demonstration or a help article describing a higher tier.
| Zendesk-documented example | What to verify before purchase |
|---|---|
| Account audit logs are documented as an Enterprise-and-above capability. | Whether your proposed edition includes the required events, fields, interfaces, and retention. |
| The access log is described as part of the Advanced Data Privacy and Protection add-on. | Whether the add-on is available in your region, its price, captured events, and the retention period in your contract. |
| Audit-log API endpoints are described as Enterprise-only. | Whether API access, rate limits, pagination, and export volume meet incident-response needs. |
| The documented access log covers the previous 90 days. | Whether longer retention or an external archive is required and technically available. |
Repeat this exercise for every finalist. Include implementation costs, additional environments, connector charges, event-volume limits, and any professional-services dependency that affects the controls. A lower subscription price is not lower total cost if required evidence must be recreated elsewhere.
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7. Compare finalists on common, testable axes
| Axis | Questions for the same proof-of-concept |
|---|---|
| Least privilege | Can access be limited by role, team, record, field, queue, action, dataset, and integration identity? Can administration be delegated safely? |
| AI authorization | Does retrieval use the signed-in user’s permissions? Can sensitive fields and datasets be excluded? Are high-impact actions gated by deterministic approval rules? |
| Event coverage | Which configuration, access, AI, export, and system events exist, and which are explicitly out of scope? |
| Evidence quality | Are actors stable, timestamps unambiguous, changes reconstructable, and AI steps correlated? |
| Retention and export | How long is each event class retained? Can investigators search, export, archive, and feed a monitoring system? |
| Data handling | Which AI providers process data, where is it hosted, what regional terms apply, and what contractual commitments govern use and deletion? |
| Commercial fit | Which SKU and add-ons provide each control, and what limits or renewals could remove it? |
Use identical scenarios, role definitions, event requests, and evidence formats for every finalist. The available vendor materials are not an independent, like-for-like benchmark; they show useful dimensions to verify, not proof that two products provide equivalent coverage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the documented vendor examples show
Zendesk
Zendesk’s official security guidance recommends limited administrator access, audit-log review, and role controls. Its account audit log records account changes and is documented for Enterprise and higher editions. The help documentation describes time, actor, IP address, and other entry information, and states that end-user activity is not included in that account log. A separate access log, described as part of the Advanced Data Privacy and Protection add-on, concerns access to tickets, user profiles, and searches and is documented as covering the previous 90 days. Developer documentation identifies actor and change-description fields and describes the audit-log endpoints as Enterprise-only.
Zendesk’s AI trust material describes governance modeled on the NIST AI Risk Management Framework and explains the company’s stated data-handling approach. Treat those statements as vendor information. Validate the providers, regional processing, retention, and contractual commitments that apply to your deployment.
Salesforce
Salesforce’s trusted-AI material describes limiting an agent to data authorized for the current user, restricting datasets or fields in AI workflows, applying deterministic controls to sensitive actions, and providing an audit trail. Ask for a demonstration with your own roles, objects, fields, connectors, and approval paths. The existence of a described control does not prove that a particular configuration enforces it correctly.
Proof-of-concept acceptance checklist
- Every production data source and integration is inventoried.
- Each human and service identity has a documented role and least-privilege rationale.
- Direct record access and AI-assisted retrieval produce the same authorization result.
- Restricted fields cannot be exposed through prompts, summaries, search, exports, or conversation history.
- Sensitive AI actions require the specified approval or are blocked.
- Configuration, identity, access, AI, export, and integration events are mapped to concrete log records.
- Each required event includes the actor, time and timezone, object, action, result, and correlation data appropriate to the event.
- Investigators can search and export a realistic incident without vendor assistance.
- Retention meets policy, or an tested archive receives events before expiry.
- The proposal names every required edition, add-on, API entitlement, limit, region, and contract commitment.
- Changes to roles, connectors, AI policies, and data sources generate evidence and trigger the intended monitoring alerts.
Common selection mistakes and how to correct them
Assuming one audit log covers everything
Correction: separate configuration changes, record access, AI activity, authentication, and exports in the event matrix. Require an explicit “not recorded” answer where coverage is absent.
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Testing only the support console
Correction: repeat tests through APIs, automations, connectors, mobile clients, and service identities. AI tools often operate through paths that a console-only test misses.
Trusting a vendor’s governance statement as a control
Correction: use trust material to form questions, then validate your roles, data sources, providers, regions, retention, and contract language in a configured environment.
Ignoring plan and add-on boundaries
Correction: attach every requirement to a quoted SKU and entitlement. Recheck it when user counts, regions, or contract terms change.
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Buying retention without testing retrieval
Correction: perform the tabletop investigation, export a large filtered set, test pagination and timestamps, and confirm an archive or monitoring feed receives events before the platform’s retention limit.
Make the decision
Select the finalist that passes your highest-risk authorization and investigation scenarios with the least compensating infrastructure, then confirm that the passing configuration is covered by the signed commercial terms. If no platform records a required AI or data-access event, do not hide that gap behind the phrase “audit log”; either add an independent control or choose a design whose evidence matches your policy and incident-response obligations.
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