Most AI CRM security reviews fail because they stop at “Do you have SOC 2?” In 2026, SOC 2 is still necessary, but it is not sufficient. Buyers now run “security gates for AI” that focus on governance: what data the model can see, what it can remember, what it can do, and how you prove it later in an audit.
TL;DR: Use this AI CRM security checklist to pass modern AI security gates: verify SOC 2 scope and subprocessors, lock down data retention and deletion, confirm model training policies, minimize PII, enforce role-based and field-level access, require audit logs, encryption, SSO/SAML and IP allowlists, separate sandboxes, log prompts and outputs safely, add agent action approvals and rate limits, and design safe-fail behavior. Then run an internal rollout plan (security, legal, pilot, monitoring). Governance is the differentiator.
What “AI CRM security” means in 2026 (and why SOC 2 is table stakes)
AI CRM security is the set of technical controls and governance rules that prevent an AI-enabled CRM from:
- leaking sensitive CRM data (PII, deal terms, emails, notes),
- taking unsafe actions (sending emails, changing pipeline stages, deleting records),
- becoming an untraceable “black box” (no audit trail),
- expanding risk through hidden vendors (subprocessors, model providers, plugins).
SOC 2 is still important because it validates that controls exist and operate over time across security, availability, confidentiality, processing integrity, and privacy, depending on scope. But SOC 2 does not automatically answer AI-specific buyer questions like:
- “Is our data used to train your models?”
- “Can we delete all prompts and outputs?”
- “Can your AI agent send emails without approval?”
- “Can we restrict the AI from seeing certain fields?”
- “Can we prove who saw what and when?”
When breaches happen, they are expensive and disruptive. IBM’s annual breach research continues to show multi-million dollar average breach costs and a growing governance gap tied to AI and shadow AI. (Use this as your internal justification to invest in governance, not just compliance.)
- IBM Cost of a Data Breach report (2025 landing page): https://www.ibm.com/reports/data-breach
- IBM “What’s new” 2024 report recap (global average breach cost and disruption stats): https://www.ibm.com/think/insights/whats-new-2024-cost-of-a-data-breach-report
For AI risk governance language that security teams recognize, align your program to:
- NIST AI RMF 1.0 (govern, map, measure, manage): https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10
And for LLM-specific app risks, reference: - OWASP Top 10 for LLM Applications (prompt injection, sensitive data disclosure, excessive agency): https://genai.owasp.org/llm-top-10/
How to use this AI CRM security checklist (the “security gates for AI” buying cycle)
Most B2B security reviews follow a predictable sequence. Use the checklist in the same order so you do not get stalled late by a missing control.
Security gates sequence (recommended):
- Compliance proof (SOC 2, pen tests, policies)
- Vendor transparency (subprocessors, data flow diagrams)
- Data governance (retention, deletion, training, PII minimization)
- Identity and access (RBAC, field-level access, SSO, IP restrictions)
- Monitoring and auditability (logs, alerts, exports)
- AI-specific controls (prompt/output logging, agent guardrails, approvals, safe-fail)
- Rollout plan (pilot, training, ongoing monitoring)
AI CRM security checklist: Step-by-step gates (copy/paste for your vendor review)
Step 1: Confirm SOC 2, scope, and what “covered systems” actually include
Goal: Avoid the classic trap where a vendor “has SOC 2” but the AI features or data pipelines are out of scope.
Checklist:
- Ask for a SOC 2 Type II report and confirm the report period.
- Confirm the in-scope system includes:
- the AI features (email writer, agent, scoring),
- the data ingestion and enrichment pipelines,
- the prompt and output storage layer (if any),
- the infrastructure environment(s) you will use (regions, clouds).
- Confirm the Trust Services Criteria categories included (security is required, others may be optional). For a CRM handling PII, you typically want privacy and confidentiality addressed, at minimum.
Reference explainer for TSC categories: https://cloudsecurityalliance.org/articles/the-5-soc-2-trust-services-criteria-explained
Procurement tip: If the SOC 2 report excludes the AI agent or “beta AI features,” treat that as a separate vendor, because it is.
Step 2: Subprocessor transparency and model vendor accountability
Goal: Know every party that can touch your CRM data, including AI model providers, enrichment data sources, email infrastructure, observability tools, and support platforms.
Checklist:
- Require a current subprocessor list that includes:
- AI/LLM providers,
- cloud infrastructure,
- data enrichment providers,
- email sending infrastructure (if embedded),
- customer support tooling that might receive logs or attachments.
- Ask for:
- how subprocessors are vetted,
- how customers are notified of changes,
- which subprocessors are optional (feature-gated),
- data residency implications.
Red flag: “We do not maintain a subprocessor list” or “We can’t share it.” That is usually a deal blocker for enterprise buyers.
Step 3: Data retention, deletion, and right-to-be-forgotten operations
Goal: Ensure you can actually remove customer data, including AI artifacts like prompts, outputs, embeddings, and agent action traces.
Checklist:
- Get written answers on retention for each data type:
- CRM records,
- emails and email content,
- attachments,
- activity logs,
- prompts and AI outputs,
- embeddings or vector indexes,
- enriched data.
- Validate deletion capabilities:
- record-level deletion,
- account-wide deletion on contract termination,
- deletion propagation to backups (or documented backup retention windows),
- deletion propagation to vector stores and caches.
- Ask for deletion SLAs (example: “completed within 30 days”).
Implementation note for Chronic Digital teams: Treat retention as a product surface area. Your AI features should respect deletion requests automatically, including derived artifacts.
Step 4: Model training policies (your data, their models, and the default settings)
Goal: Prevent your CRM data from becoming training data unless you explicitly opt in.
Checklist:
- Require a clear, contractual statement on:
- whether customer data is used for training,
- whether it is used for fine-tuning, evaluation, or human review,
- whether prompts and outputs are stored and for how long,
- how data is isolated by tenant.
- Require opt-in controls and customer-level settings:
- “Do not train on my data” default for business tiers,
- ability to disable prompt storage (or reduce retention),
- ability to disable human review.
Security language you can use internally: This maps directly to NIST AI RMF governance expectations (policies, oversight, accountability). https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10
Step 5: PII minimization (and preventing sensitive data disclosure)
Goal: Reduce what the AI can see, and reduce the chance it reveals sensitive info in outputs.
Checklist:
- Data minimization:
- Only sync fields needed for your workflows.
- Avoid syncing full email bodies and attachments unless required.
- Mask or tokenize high-risk fields (SSNs, payment data, health data).
- Output controls:
- Configure the AI to avoid generating or reprinting sensitive fields.
- Add “never include” rules for internal notes, legal terms, or credentials.
- Test against common OWASP LLM risks:
- Sensitive information disclosure (LLM06),
- prompt injection (LLM01),
- excessive agency (LLM08).
OWASP LLM Top 10 reference: https://genai.owasp.org/llm-top-10/
Practical example: If your CRM stores contract redlines in notes, your AI email writer should not have access to that notes field by default. Make it an explicit, role-gated permission.
Step 6: Role-based permissions (RBAC) and least privilege by default
Goal: Ensure users, teams, and AI agents only access what they need.
Checklist:
- RBAC requirements:
- roles by team (SDR, AE, CS, RevOps, Admin),
- permission sets for AI features (who can use agent actions, who can approve),
- ability to disable exports per role.
- Least privilege defaults:
- new users start with minimal permissions,
- admin privileges require explicit assignment,
- scoped API keys (read-only vs read-write).
Tie-in to Chronic Digital: Permissions are not just an admin feature. They are an AI safety boundary. Your AI lead scoring, enrichment, and agent actions should respect RBAC at inference time, not only in the UI.
Step 7: Field-level access control (FLAC) for sensitive fields
Goal: Separate “can access the record” from “can access the sensitive fields inside the record.”
Checklist:
- Field-level controls for:
- personal phone numbers,
- personal emails,
- deal amount and margin,
- renewal dates,
- notes and call transcripts,
- custom fields tagged “confidential.”
- AI-aware field access:
- the AI should not be able to reference fields a user cannot view.
- the AI should not be able to use hidden fields in reasoning and then reveal them in an output.
Buyer proof point: Ask the vendor to demonstrate a user with restricted fields running an AI summary and confirm the restricted fields do not appear.
Step 8: Audit logs that actually answer “who did what” (including AI)
Goal: Make AI actions and access auditable for security investigations, compliance, and internal trust.
Checklist:
- Audit logging must cover:
- logins (success, failure),
- permission changes,
- data exports,
- record views (if available) and record edits,
- API access,
- AI actions: prompt submitted, tool invoked, action proposed, action approved, action executed, action failed.
- Log quality requirements:
- immutable or tamper-evident storage,
- timestamps with timezone consistency,
- actor identity (user, admin, service account, AI agent),
- correlation IDs for workflows.
- Operational requirements:
- log export to SIEM,
- retention configuration,
- alerting hooks (webhooks) for high-risk events.
Tie-in to Chronic Digital: This is where “controlled automation” becomes a competitive advantage. If you can show AI actions as first-class audit events, you reduce perceived risk dramatically.
Step 9: Encryption standards (in transit, at rest, and key management)
Goal: Meet baseline security expectations and reduce blast radius.
Checklist:
- Encryption in transit:
- TLS everywhere (browser to app, app to database, app to model provider).
- Encryption at rest:
- databases, object storage, backups, logs.
- Key management:
- rotation policies,
- KMS-backed keys,
- customer-managed keys (CMK/BYOK) if you sell to regulated industries.
Procurement tip: Ask for a diagram: where encryption starts, where it terminates, and which systems ever see plaintext.
Step 10: SSO/SAML, SCIM provisioning, and MFA enforcement
Goal: Prevent account takeover and reduce access sprawl.
Checklist:
- Required:
- SSO (SAML or OIDC),
- MFA enforcement (preferably via IdP),
- SCIM for lifecycle management (create, disable, deprovision).
- Operational controls:
- session timeouts,
- device and location policies (via IdP),
- break-glass admin accounts with strong controls.
Step 11: IP allowlists and network access restrictions (where it makes sense)
Goal: Restrict access to known networks for high-risk teams.
Checklist:
- Admin portal IP restrictions.
- API IP allowlisting.
- Separate policy for remote teams and VPN usage.
Reality check: IP allowlists are not always practical for modern distributed teams, but security buyers still ask for them. Be ready with compensating controls (SSO, device posture, conditional access).
Step 12: Sandbox environments and safe test data
Goal: Prevent “testing in production” and stop accidental leaks during evaluation.
Checklist:
- Provide a sandbox or dev workspace that:
- isolates data from production,
- supports fake domains for email testing,
- disables real sending by default.
- Require a test data policy:
- never upload real customer lists into pilots,
- use anonymized datasets,
- watermark pilot outputs.
Internal link (recommended read): If you are building clean data practices before you automate anything, use this: Minimum Viable CRM Data for AI: The 20 Fields You Need for Scoring, Enrichment, and Personalization
AI-specific governance gates (this is where deals are won or lost)
Step 13: Prompt and output logging (what to store, what to redact)
Goal: Make the AI observable without creating a new sensitive data repository.
Checklist:
- Decide what you log:
- prompt text (full vs redacted),
- retrieved context (what records were pulled),
- model output,
- tool calls and parameters,
- evaluation scores (toxicity, PII detection).
- Redaction requirements:
- automatically redact emails, phone numbers, addresses, and secrets in logs where feasible,
- never log credentials or tokens.
- Access requirements:
- only admins and security roles can view raw prompts/outputs,
- export controls and watermarking for audit exports.
Tie-in to Chronic Digital: Prompt and output logs should be part of your audit trail, but governed by the same permissions model as CRM data.
Step 14: Agent action approvals (human-in-the-loop by default)
Goal: Prevent autonomous AI from making irreversible or risky changes without review.
Checklist:
- Require an “approval mode” for:
- sending emails,
- bulk updates (stages, owners, amounts),
- deletions,
- enrichment writes to key fields,
- creating sequences/campaign steps.
- Approval workflow requirements:
- show proposed action,
- show data used to decide,
- allow edits before execution,
- record approval decision in audit logs.
Internal link: If you are thinking about where agents belong, this helps structure it: Salesforce State of Sales 2026: The 5 CRM Workflows to Automate First With AI Agents (and the 5 to Keep Human)
Step 15: Rate limits, quotas, and spend controls (security and cost)
Goal: Stop abuse, prompt injection loops, and runaway automation.
Checklist:
- Per-user and per-workspace limits for:
- AI requests per minute/day,
- agent actions per hour/day,
- enrichment jobs and exports.
- Circuit breakers:
- auto-disable agent actions after repeated failures,
- auto-pause campaigns on anomaly detection (bounce spikes, complaint spikes).
- Budget controls:
- caps by team,
- alerts at thresholds.
Internal link: If AI touches outbound, deliverability is a security-adjacent risk (domain reputation and data leakage). Use: Cold Email Deliverability Checklist for 2026: Inbox Placement Tests, Auto-Pause Rules, and Ramp Plans
Step 16: Safe-fail behavior (design for “when the AI is wrong”)
Goal: Make failures predictable, reversible, and non-destructive.
Checklist:
- Define safe-fail defaults:
- if permissions cannot be verified, deny action,
- if model confidence is low, ask for approval or clarification,
- if required context is missing, do not guess, request input.
- Reversibility:
- undo for bulk edits,
- version history for key fields,
- rollback for agent workflows.
- “No silent actions” rule:
- every agent action must create a visible activity record.
OWASP mapping: This reduces the impact of excessive agency and insecure output handling. https://genai.owasp.org/llm-top-10/
Red flags section: what should stop an AI CRM purchase
Use this as your quick decision filter.
Red flag 1: Shadow AI usage inside your own org
Symptoms:
- reps paste deal notes into random AI tools,
- unknown browser extensions “summarize” LinkedIn and write emails,
- no retention controls, no admin visibility.
Fix:
- standardize on an AI CRM workflow and block unmanaged tools where feasible.
- give reps a sanctioned AI email writer and AI agent with guardrails.
Red flag 2: Unclear data use or vague training language
Symptoms:
- “We may use data to improve our services” with no opt-out details.
- no separation between prompts, outputs, and training pipelines.
Fix:
- demand explicit contract language about training, retention, and deletion.
- require product controls, not just policy promises.
Red flag 3: No audit trail for AI actions
Symptoms:
- you cannot answer “who approved this email?” or “why did the agent change ownership?”
- logs exist but cannot be exported, filtered, or retained.
Fix:
- require AI actions to be first-class audit events.
- require prompt/tool/action traces with permissioned access.
Red flag 4: Agent can take action without approvals or limits
Symptoms:
- “fully autonomous SDR” with no gating.
- no rate limits, no circuit breakers.
Fix:
- require human-in-the-loop approvals for high-risk actions.
- require safe-fail rules and caps.
Red flag 5: Subprocessors are hidden or constantly changing without notice
Symptoms:
- vendor will not disclose model providers or enrichment sources.
- no customer notification for new subprocessors.
Fix:
- treat this as a supply chain risk and escalate to procurement/security.
Internal rollout plan: implement AI CRM securely (30 to 90 days)
This is the part most teams skip, then they blame the tool. Use this plan to ship safely.
1) Security review (Week 1-2)
Deliverables:
- Completed AI CRM security checklist (this article) with evidence links.
- Data flow diagram: sources, sinks, subprocessors, logs, model calls.
- Risk register:
- top 10 risks,
- mitigations and owners,
- accept/avoid decisions.
Operating model:
- Name an AI System Owner (RevOps or Sales Ops).
- Name an AI Security Owner (Security or GRC).
- Define an exception process (how teams request more access).
2) Legal and privacy review (Week 2-3)
Deliverables:
- DPA review, SCCs if needed, subprocessor terms.
- Model training and retention contract language finalized.
- Policy updates:
- acceptable use (no secrets in prompts),
- data classification guidance for CRM fields.
3) Pilot scope (Weeks 3-6)
Scope it tightly:
- 10 to 25 users max (one SDR pod, one AE pod).
- One or two workflows only:
- AI email writer with approvals,
- lead enrichment with write-back limited to non-sensitive fields,
- AI lead scoring visible but not auto-routing yet.
Controls to enforce in pilot:
- SSO only.
- RBAC + field-level access configured.
- Prompt/output logging on, with redaction and limited viewer roles.
- Agent actions require approvals and have daily caps.
Internal link: Build clean data before you scale: Lead Enrichment Workflow: How to Keep Your CRM Accurate in 2026 (Rules, Refresh Cadence, and Confidence Scores)
4) Ongoing monitoring (Weeks 6+)
What to monitor weekly:
- permission changes,
- export events,
- AI usage spikes,
- agent action approvals vs rejections,
- anomalies (bulk edits, unusual login patterns),
- deliverability and complaint metrics for AI-assisted outbound.
What to review monthly:
- subprocessor changes,
- retention and deletion checks,
- sampling of prompt/output logs for policy violations,
- incident tabletop exercise (prompt injection scenario + runaway agent scenario).
Governance cadence: Quarterly “AI controls review” meeting with Security, Legal, and RevOps.
Turn governance into a competitive advantage (how Chronic Digital should position it)
If you sell an AI CRM in 2026, your security story should not stop at SOC 2. The winning message is:
- Controlled automation: AI can draft, recommend, and queue actions, but high-risk actions require approval.
- Permissions as a safety boundary: RBAC and field-level access apply to both humans and AI.
- Auditability: Every AI step is traceable: prompt, context pulled, output, tool call, approval, execution.
- Data minimization by design: only the minimum fields needed for a workflow are available to AI.
- Subprocessor transparency: buyers can see and approve the AI supply chain.
Internal link (positioning and roadmap): OpenAI’s New Enterprise Agent Platform: What It Means for Sales Teams (and Why Your CRM Becomes the Control Plane)
FAQ
What is an AI CRM security checklist?
An AI CRM security checklist is a structured list of controls that verifies an AI-enabled CRM is safe to deploy. It covers baseline compliance (like SOC 2), classic security (SSO, encryption, logs), and AI-specific governance (model training policies, prompt/output logging, agent approvals, and safe-fail behavior). It is designed to match how security and procurement teams evaluate AI tools.
Is SOC 2 enough to approve an AI CRM?
Usually not. SOC 2 is a baseline that signals a vendor has a control environment and has been audited over time, but it may not cover AI-specific risks like prompt injection, sensitive output leakage, or autonomous agent actions. Security teams increasingly require governance evidence that maps to frameworks like NIST AI RMF and OWASP LLM Top 10.
References: https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10 and https://genai.owasp.org/llm-top-10/
What should I ask about “model training” before connecting my CRM?
Ask whether your CRM data, prompts, and outputs are used for training, fine-tuning, evaluation, or human review, and whether you can opt out. Also ask how long prompts and outputs are retained, and whether derived artifacts like embeddings are deleted when you delete records. If the answer is vague, treat it as a risk.
What are the biggest AI-specific risks in a CRM?
The most common high-impact risks are:
- prompt injection leading to unauthorized data access,
- sensitive information disclosure in generated outputs,
- excessive agency where an agent takes unintended actions,
- lack of auditability for AI-driven changes.
OWASP provides a widely used risk list for LLM applications: https://genai.owasp.org/llm-top-10/
How do you secure AI agents that can take actions in a CRM?
Use layered controls:
- least-privilege permissions for agent tools,
- human approvals for high-risk actions (send email, bulk edits, deletions),
- rate limits and circuit breakers,
- safe-fail defaults (deny when uncertain),
- full audit logging of proposals, approvals, and executions.
What are deal-breaking red flags when buying an AI CRM?
Common deal breakers include unclear data use or training policies, missing subprocessor transparency, no audit trail for AI actions, and autonomous actions with no approvals or rate limits. These issues create governance risk that SOC 2 alone does not mitigate.
Put this checklist into your vendor scorecard this week
- Copy this AI CRM security checklist into your procurement template.
- Run a 60-minute call with Security, RevOps, and the vendor to fill gaps live.
- Require a short demo of: RBAC, field-level access, audit logs, prompt/output logs, and agent approvals.
- Scope a pilot with strict permissions, approval-only actions, and monitoring from day one.
- Schedule the first monthly AI controls review before you roll out to the full sales org.