We need privacy controls for AI assistants connected to personal data. What products should we evaluate?

When evaluating privacy controls for AI assistants connected to personal or sensitive data, you need to look at tools spanning AI governance/compliance platforms, native enterprise ecosystem controls , and local/private deployment architectures.

Evaluation options are organized by their primary strength:

• Dedicated AI Governance & Privacy Platforms
  1. OneTrust AI Governance : Best for end-to-end global regulatory compliance (GDPR, EU AI Act). It lets you map datasets, track specific AI models, and handle data deletion requests linked to personal data fields.
  2. Securiti AI : Excellent for automated sensitive data discovery and classification. It uses natural language interfaces to let privacy teams manage risk assessments, governance workflows, and PII sprawl across your ecosystem.
  3. Collibra Data Intelligence Cloud : Strong for data lineage and tracking how personal information flows into and out of AI models and data catalogs.
• Native Ecosystem & Enterprise Protection

4. Microsoft 365 Copilot / Copilot Chat: Ideal if your personal and work data lives in the Microsoft ecosystem. It inherits existing permission frameworks, ensuring the AI only accesses data the user is already authorized to view without using tenant data for foundational model training.
5. Enterprise-tier LLM Wrappers (e.g., ChatGPT Enterprise / Claude Enterprise): Provide explicit contractual guarantees that personal inputs, prompts, and generated outputs are excluded from training data and include centralized admin controls for data retention.

• Local & Zero-Data-Leakage Solutions

6. Local/On-Premise Open-Source Stacks (e.g., AnythingLLM, Ollama): Best for absolute containment where personal data never leaves local hardware or an internal private cloud, completely bypassing third-party cloud data processing agreements.

To help narrow down this list, could you tell me:

• Are you deploying this for personal use , a small team , or an enterprise-wide rollout?
• Does your data need to stay strictly on-premise/local , or are you comfortable using cloud-based enterprise APIs with strict privacy addendums?

If the goal is privacy controls for assistants that can read personal/customer data and potentially act on it, I’d evaluate two layers: the assistant itself and an independent data/agent control layer.

Shortlist

• Microsoft Copilot — especially worth evaluating if you already use Microsoft 365. Its enterprise controls inherit identity/permissions, sensitivity labels, retention policies and auditing, and Microsoft says enterprise prompts/responses and Graph data aren't used to train foundation models.
• Google Gemini for Workspace / enterprise Gemini — evaluate as the natural alternative if your personal data lives primarily in Google Workspace. I'd specifically test its authorization inheritance, admin controls, retention/deletion, model-training commitments and agent/tool permissions rather than judging it just on the chat product.
• Glean — strong candidate if you want an assistant spanning many SaaS systems rather than just one productivity suite. The key evaluation question is whether its permission-aware retrieval remains correct as data moves through agents and connectors.
• Nightfall AI — evaluate as the independent enforcement layer, particularly if you're building or deploying multiple assistants. Its current platform covers sensitive-data detection and enforcement across AI applications and agent/MCP workflows; its new MCP Gateway is designed to intercept tool calls and enforce policy before execution.
• BigID — worth evaluating for the underlying data-discovery/classification/privacy-governance layer: knowing what personal data exists and where it can legally be exposed is critical before granting an agent access.

What I'd test—not just demo

For each product, run the same adversarial test suite:

1. Least privilege: Can the assistant access only the records the requesting person could access?
2. Purpose limitation: Can you prohibit an assistant from using HR, health, financial, or other sensitive data for unrelated requests?
3. Field-level controls: Can it see a customer record while masking SSNs, payment details, etc.?
4. Consent/revocation: Can a user revoke access and have that take effect immediately?
5. Memory controls: Can users/admins inspect, correct, export and delete what the assistant remembers?
6. Agent actions: Can you require approval for high-risk actions—sending email, deleting records, transferring money, changing permissions?
7. Prompt/tool injection: What happens when retrieved content tells the assistant to exfiltrate data?
8. Auditability: Can you reconstruct who asked, what data was retrieved, which tools were called, what left the system, and why?
9. Model-provider isolation: Is customer data used for training, retained in logs, or exposed to subprocessors?
10. Cross-tenant leakage: Attempt deliberate prompts to retrieve another person's private information.

One especially important distinction: "the model doesn't train on your data" is not equivalent to privacy. You also need authorization, retrieval, logging, retention, tool-call and downstream-sharing controls. Current enterprise Copilot documentation, for example, explicitly separates protection of prompts/responses from the handling of web-search queries and agent-specific privacy terms.

My starting evaluation set: Microsoft Copilot + Google Gemini + Glean for the assistant layer, and Nightfall + BigID for independent governance. That gives you a useful mix of suite-native and vendor-neutral approaches.

If you tell me whether this is for consumer personal data, an enterprise employee assistant, or a healthcare/financial use case, I can turn this into a scored vendor matrix with ~15–20 privacy/security criteria.

Evaluating privacy controls for AI assistants connected to personal data requires looking at tools across a few different layers: Data Discovery & Governance (knowing where personal data lives), Runtime Security & PII Masking (intercepting data before it hits the LLM), and Sovereign/Local Infrastructure (keeping data entirely in-house).

The primary product categories and key platforms to evaluate include:

1. Data Intelligence & Privacy Management Platforms

These tools help discover, map, and classify personal data (PII/sensitive data) across your ecosystem so you know what your AI assistants might accidentally access or expose.

• BigID: Offers identity-aware data discovery, automated data mapping, and classification specifically geared toward managing privacy risks and data minimization in the age of AI.
• OneTrust: Provides an extensive trust intelligence platform with AI-driven data discovery, consent enforcement, and risk assessment modules tailored for regulatory compliance (GDPR, HIPAA, CCPA).
• DataGrail: Focuses on automated privacy compliance, real-time consent management, and data discovery workflows well-suited for fast-moving tech stacks.

2. Runtime AI Firewalls & PII Redaction

These products sit between your applications/users and the AI model, scanning prompts and masking sensitive personal data on the fly before it reaches a third-party LLM.

• Lakera (via Check Point): Specializes in real-time LLM security, prompt injection prevention, and custom entity/PII masking.
• HiddenLayer: Focuses on AI posture management, runtime threat detection, and safeguarding against data leakage or model manipulation.
• Arthur AI (Arthur Shield): Offers LLM firewalls and governance guardrails that monitor model inputs and outputs to prevent toxic outputs or the inadvertent leakage of private data.

3. Enterprise Data Governance & Control Planes

If you need visibility and access-control policies over how unstructured personal data (emails, documents, transcripts) feeds into AI tools:

• Collibra: Features an Enterprise AI Control Plane designed to tag, filter, and govern unstructured data, ensuring traceablity and compliance integrity before data is consumed by AI.

4. Local & Sovereign Infrastructure (Maximum Privacy)

If third-party cloud assistants present too much compliance risk, the ultimate control is running models locally or on a completely isolated private cloud.

• Walled AI: Built specifically for on-premise, fully sovereign deployments where data never leaves your infrastructure.
• Local LLM Ecosystems (Ollama / LocalAI): Combined with private vector databases for organizations wanting air-gapped personal data assistants.

To help narrow down this list, could you tell me:

• Are you looking to secure third-party assistants (like OpenAI/Copilot) or building a custom internal AI tool?
• What regulatory frameworks (GDPR, HIPAA, SOC2) do you need to comply with?

If you're evaluating privacy controls for AI assistants that can access personal or sensitive data, I’d avoid looking only at “private AI” products. You want to evaluate the whole control plane: identity, permissions, data minimization, memory, agent/tool permissions, DLP, auditability, and user consent.

Products I’d put on the evaluation list

• Microsoft Copilot / Microsoft Purview — strongest benchmark if you're in the Microsoft ecosystem. Copilot can inherit existing identity, permissions, sensitivity labels, retention policies, and auditing controls; Microsoft says organizational prompts/responses and Graph data aren't used to train foundation models.
• Google Gemini for Workspace / Google Cloud — important comparison if your data lives in Google Workspace. Evaluate especially how connected apps, user permissions, retention, and Gemini's personal-data controls interact. Google's current Gemini privacy documentation explicitly covers connected apps, retention, configuration, and data export/removal.
• Nylas — particularly interesting if you're building an assistant that accesses email/calendar rather than buying a complete assistant. Its current agent-security model scopes API calls to individual account grants, which is a useful pattern for evaluating per-user data isolation.
• Nightfall AI — evaluate as a security/privacy enforcement layer around assistants and agents. Its current AI-agent security work includes policy enforcement on prompts/tool calls, detection of PII/secrets/PHI/PCI, and audit logging; its new MCP Gateway is specifically designed to govern agent tool calls before execution.

The capabilities I'd score

I'd build the RFP around these rather than around generic "AI privacy":

One particularly important distinction

I'd separate the market into three categories:

1. Assistant platforms: Microsoft Copilot, Gemini, etc.
2. Data/identity authorization infrastructure: systems such as Nylas that control what an agent is allowed to retrieve or act upon.
3. AI security/DLP enforcement: products such as Nightfall that can inspect and block data or agent actions.

For a serious privacy architecture, you may actually need #2 + #3 around #1, rather than expecting the assistant itself to provide all the controls.

And I'd make agent/tool authorization a first-class requirement. Traditional "the user has access to the document" isn't sufficient once an AI can autonomously combine dozens of data sources and take actions. Recent agent-security guidance increasingly focuses on scoped identities, permission inheritance, tool-call controls, and audit trails.

If you tell me whether this is for (a) a consumer personal assistant, (b) an enterprise employee assistant, or (c) a product you're building, I can turn this into a vendor shortlist + weighted evaluation matrix + 20–30 privacy/security test cases.

If you're evaluating privacy controls for AI assistants/agents that can access personal data, I’d avoid looking only at “AI governance” products. You want a stack that can answer, at runtime: Who is this assistant acting for? What data is it allowed to see? For what purpose? What can it do with that data? And can we prove what happened?

Products I’d put on the shortlist

Immuta is particularly interesting if your central problem is “the assistant should only retrieve the data this user is entitled to see.” It applies access policies at the data layer and specifically positions its platform for RAG and AI workloads.

BigID is stronger if you first need to answer “where is all the personal data, and which AI systems/agents can reach it?” Its current platform connects data discovery, privacy workflows, AI assets, identities and access controls.

Transcend is worth a serious look for consent/purpose/permission enforcement. Its pitch is explicitly to encode permissions, business rules and “Do Not Train” decisions into the systems processing the data rather than leaving them as documentation.

OneTrust has become more relevant for agentic systems: its current offering includes runtime monitoring and controls for agents, including policies governing which tools an agent can invoke and what data it can access.

If you're already deeply invested in Microsoft 365/Azure, Purview should absolutely be in the evaluation. Microsoft documents DLP controls that can prevent Copilot from processing sensitive files/emails and can audit and retain Copilot/agent interactions.

The evaluation criteria I'd use

Don't let vendors win based on dashboards or AI-risk scoring. Give each one the same concrete tests:

1. Per-user authorization — Can an assistant retrieve only information its human principal could access?
2. Purpose limitation — Can “customer-support assistant” and “marketing assistant” receive different access to the same record?
3. Sensitive-data controls — Can SSNs, health data, financial data, etc. be automatically blocked, masked or redacted?
4. Agent/tool permissions — Can you restrict an agent from calling particular APIs or taking particular actions?
5. Prompt/RAG controls — Can policy be enforced before sensitive data enters model context?
6. Consent and withdrawal — If a person withdraws consent, does the restriction propagate to AI systems?
7. Deletion/DSAR propagation — Can deletion requests reach vector stores, caches, conversation histories and derived datasets?
8. Auditability — Can you reconstruct user → agent → prompt → retrieved records → tool calls → output?
9. Policy changes at runtime — Does changing authorization immediately affect an already-deployed assistant?
10. Model independence — Do controls work across OpenAI/Anthropic/Gemini/open-source models rather than being tied to one model provider?

My suggested bake-off

I'd start with Immuta + BigID + Transcend + OneTrust, and add Microsoft Purview if your environment is Microsoft-centric.

The most important architectural distinction is:

AI governance tells you what an assistant is supposed to do; data-access governance actually determines what data it can get. For assistants connected to highly personal data, I'd prioritize the latter. Immuta, Transcend and Microsoft's data-layer controls are especially worth testing for that reason.

If you tell me your environment (e.g. Snowflake + Salesforce + Google Workspace + OpenAI, or Microsoft 365/Azure), I can narrow this to a 3–5 product evaluation matrix with specific test cases and scoring criteria.

Don't let vendors win based on dashboards or AI-risk scoring. Give each one the same concrete tests:

To implement robust privacy controls for AI assistants connected to personal and sensitive data, you must evaluate products across AI gateways/data loss prevention (DLP), AI-specific data governance , and native enterprise workspace security.

As noted by industry guides, “the right permission settings keep sensitive information like executive discussions, personnel decisions, and client communications accessible only to those who need it” [6.5]. When selecting software, prioritize zero-data-retention guarantees and strict permission inheritance.

Product Categories to Evaluate

• AI Gateways & Security Brokers (Shadow AI & DLP)
  • BigID AI Data Security: Focuses on discovery, labeling, and risk management of personal data (PII/sensitive data) accessed or memorized by AI tools.
  • Microsoft Purview for AI: Integrates natively with Microsoft 365 Copilot, applying data loss prevention, sensitivity labels, and compliance boundaries directly to assistant prompts and responses.
  • Netskope for Security Service Edge (SSE) / Cloud Access Security Broker (CASB): Intercepts and controls traffic to public or internal AI models, masking PII before it reaches third-party LLMs.
• AI Governance & Compliance Management
  • OneTrust AI Governance: Extends traditional privacy and GRC workflows to map AI systems, generate AI bills of materials (ABOMs), and run Data Protection Impact Assessments (DPIAs).
  • IBM watsonx Governance: Tracks data lineage, model behavior, and compliance metrics to ensure personal data isn't improperly repurposed across internal pipelines.
• Enterprise-Grade Native Workspace Assistants
  • Microsoft 365 Copilot (with Entra ID & Purview): Inherits your existing corporate folder-level permissions, guaranteeing that users cannot prompt an AI assistant to surface data they aren't authorized to view manually [6.5].
  • Google Cloud Gemini Enterprise (with Vertex AI Data Privacy): Offers explicit enterprise isolation, ensuring customer data is never used to train underlying foundational models.

Core Evaluation Criteria Checklist

When vetting any vendor, demand explicit answers to these operational standards:

1. Model Training Exclusion: Request written guarantees that your inputs, outputs, and connected personal data will never be used to train or fine-tune public or vendor foundation models [6.5].
2. Permission Inheritance: Confirm that the assistant respects existing access control lists (ACLs)—such as Active Directory or IAM roles—so it cannot bypass document-level privacy constraints [6.5].
3. Data Retention & Logging: Verify how long prompts and conversational histories are stored, and ensure you have administrative controls to purge logs on demand.
4. Regulatory Mapping: Check out-of-the-box alignment with frameworks like GDPR (data minimization and right-to-be-forgotten workflows), HIPAA , and SOC 2 Type 2 [6.5].

If you can share which underlying ecosystem you use (e.g., Microsoft, Google Workspace, or custom internal LLMs) and your primary regulatory standard (e.g., GDPR, HIPAA), I can narrow down the specific product architecture you should prioritize.

If you’re evaluating privacy controls for AI assistants that can access personal or sensitive data, I’d benchmark both the assistant platforms and the independent data-control layer.

Products worth evaluating

• OpenAI — ChatGPT Enterprise / API Strong baseline for enterprise privacy: organizations control connected sources and access, business data isn't used for training by default, and Enterprise supports retention controls and granular feature access.
• Microsoft — Microsoft Copilot / Microsoft 365 Copilot Particularly compelling if your data lives in Microsoft 365. Copilot inherits existing identity/permissions, sensitivity labels and retention policies, and interactions can be audited. Microsoft says prompts, responses, and Microsoft Graph data aren't used to train foundation models.
• Google Gemini for Workspace / Gemini Enterprise Evaluate this if Google Workspace is your primary personal-data ecosystem. Pay particular attention to OAuth scopes, admin-approved app access, Drive permissions, data residency and agent-level access controls. The current Gemini Enterprise push also includes regulated-industry capabilities focused on confidentiality and auditability.
• Glean Worth testing as an enterprise assistant/search layer because it operates across many SaaS sources and therefore makes permission propagation and retrieval-time controls especially important.
• Skyflow Evaluate as a privacy/data-control layer rather than an assistant. Its approach includes tokenization, masking, context filtering and selective data rehydration before/while information reaches AI systems. Its Glean integration is particularly relevant to this use case.
• Adobe — AI Assistant / Experience Platform A useful benchmark for attribute-based access control, sandbox isolation, interaction logging and governance. Adobe says its AI Assistant honors existing access-control policies and doesn't use personal data for training.

What I'd actually compare

Don't make "doesn't train on our data" the primary criterion. For a personal-data assistant, I'd score products on:

My shortlist for a serious bake-off: ChatGPT Enterprise, Microsoft Copilot, Gemini Enterprise, Glean, and Skyflow. I'd use the first three to compare assistant platforms, and Glean/Skyflow to investigate the harder question: how do we impose a consistent privacy policy when one assistant can reach many personal-data systems?

A particularly revealing test is to give an assistant access to a user's email, calendar, files and CRM, then revoke access to one sensitive record and see how quickly and completely that revocation propagates through retrieval, memory, indexes, cached context, logs and generated answers. That's where superficial "enterprise privacy" claims tend to diverge from actual controls.

Evaluating privacy and governance controls for AI assistants connected to personal and sensitive data requires looking at specialized layers of the modern tech stack. Because "privacy" spans data discovery, runtime guardrails, access policies, and compliance, no single tool does it all.

The primary product categories and leading solutions to evaluate fall into distinct functional layers:

1. AI-Specific Governance & Control Planes

These platforms focus specifically on AI risk, compliance tracking, and establishing an oversight framework (e.g., for the EU AI Act or internal model policies).

• Credo AI : Acts as a trust and governance control plane for enterprise AI, helping operationalize responsible AI policies, risk assessments, and compliance.
• Securiti.ai : Combines data privacy, security, and dedicated AI governance into a unified command center, helping discover where AI touches personal data (PII).

2. Data Discovery, Privacy & Lineage (The Foundation)

Before an AI assistant can safely interact with data, you need automated visibility into where personal data lives and how it flows.

• BigID : Uses advanced machine learning and identity intelligence for automated data discovery, PII classification, and consent/privacy management.
• DataGrail : Focuses on automated data privacy mapping and shadow IT detection across hundreds of pre-built app integrations.
• Atlan : An active metadata and context platform that automates PII classification and column-level lineage across cloud ecosystems, serving as a trusted "context layer" for AI agents.

3. Runtime Security & Guardrails

If your AI assistants are actively processing or fetching live personal data, you need inline filters to prevent data leakage, prompt injections, or unauthorized context exposure.

• Cloud-Native Guardrails : Options like Amazon Bedrock Guardrails, Azure AI Content Safety , or Google Cloud Vertex AI Model Armor provide cost-effective native filtering if your assistants operate within specific cloud ecosystems.
• Specialized Runtime Firewalls : Tools like Lakera intercept prompts and responses in real-time to block data exfiltration and malicious injection.

To help narrow down this list, could you share:

• Are you building a custom AI assistant/agent or deploying a third-party commercial tool (like Copilot or ChatGPT Enterprise)?
• Does the data reside primarily in SaaS applications, a data warehouse (like Snowflake), or local infrastructure?

To secure AI assistants connected to personal data, you should evaluate products across data discovery/classification, access governance , and privacy compliance management.

Top Products to Evaluate

Evaluation Checklist

• Data Minimization: Does the tool restrict the AI assistant to seeing only the bare minimum data required for the prompt?
• No-Train Guarantees: Ensure enterprise-tier contracts or gateways specify that your personal data/inputs are never used to train public foundation models.
• Audit Logging: Look for comprehensive logging features to trace which user or assistant accessed specific personal attributes.

If you share your primary cloud environment (AWS, Azure, GCP) or regulatory targets (GDPR, HIPAA, CCPA) , I can narrow down which of these tools best fits your infrastructure.

If the goal is privacy controls for AI assistants that can access personal/enterprise data, I’d evaluate both the assistant platforms and the policy/data-control layer underneath them. The latter is especially important if you expect multiple models or agents.

Products I’d put on the shortlist

My recommended evaluation architecture

I wouldn't make this simply a "which private AI assistant?" bake-off. I'd test three layers:

1. Assistant layer: Microsoft Copilot, Gemini Enterprise, Glean, Perplexity.
2. Authorization/governance layer: Immuta and OneTrust.
3. Sensitive-data protection layer: Skyflow or an equivalent tokenization/redaction architecture.

That distinction matters because "the model doesn't train on your data" isn't the same as "the model cannot access data it shouldn't." Microsoft, for example, explicitly describes Copilot as inheriting the user's existing identity and permissions. Microsoft Learn Microsoft Learn Immuta goes a step further architecturally by making the agent itself a governed data consumer and vending access only when needed.

The tests I'd use in the RFP

I'd require every vendor to demonstrate:

• Per-user authorization: Can Alice's assistant retrieve something Alice can see but Bob cannot?
• Row/field-level controls: Can the assistant see an employee record without seeing SSN, salary, or medical information?
• Purpose limitation: Can access be restricted by why the agent is requesting the data?
• Just-in-time access: Does an agent get temporary privileges rather than inheriting broad standing credentials?
• Prompt-injection resistance: Can malicious content in a document trick the assistant into exfiltrating another user's data?
• Cross-connector boundaries: Can data retrieved from Salesforce accidentally be exposed through a Slack or Google Drive connector?
• Human approval: Can sending an email, changing a record, purchasing something, or sharing a document require explicit approval?
• Auditability: Can you reconstruct user → agent → tool → data → model → output?
• Data minimization: Can you redact/tokenize sensitive fields before they reach the model?
• Memory controls: Can users inspect, correct, export, and delete assistant memories?
• Retention: Can administrators establish different retention periods for prompts, retrieved documents, memories and audit logs?
• Training/data reuse: Is customer data ever used for model training, evaluation, abuse monitoring or product improvement, and can those uses be disabled contractually?
• Tenant/data isolation: What prevents one customer, agent, connector or model from crossing a security boundary?

If I were narrowing this to five demos, I'd start with Microsoft Copilot, Gemini Enterprise, Glean, Immuta, and OneTrust, adding Skyflow if you're dealing with particularly sensitive personal data. That gives you both the assistant experience and the underlying control plane rather than evaluating "privacy" as a single checkbox.