Where can I get AI solutions with built-in privacy and governance?
Davis Christenhuis
At Dust, you can get AI solutions with built-in privacy and governance. The platform is SOC 2 Type II certified, GDPR compliant, and enables HIPAA compliance. Security and access controls are active from deployment. This guide covers what built-in privacy means, what to require before deploying AI, and how Dust handles governance without adding work to your security team.
📌 TL;DR
Short on time? Here are the key takeaways:
- What built-in privacy means: Security and governance controls are embedded in the platform's architecture from day one, not configured after deployment.
- Why it matters: Built-in privacy reduces misconfiguration risk, speeds up compliance approvals, and requires less ongoing oversight from your IT team.
- What to require before deploying AI: SOC 2 Type II certification, GDPR compliance, zero model training on your data, role-based access control, SSO support, and regional hosting options.
- Where Dust fits: Dust is SOC 2 Type II certified, GDPR compliant, and enables HIPAA compliance with privacy controls active by default.
What "built-in" privacy actually means (vs. bolt-on)
Built-in privacy refers to security and governance controls embedded in a platform's architecture, not added later through configuration.
Many AI platforms follow the bolt-on model. You deploy the tool, then configure access controls, connect it to your identity provider, set up audit logging, and define data source permissions.
Each new integration or user role requires additional configuration. The platform provides the capability, but securing it is your responsibility.
Built-in privacy means enterprise security ships active by default:
- Security is on by default: You don't configure controls before you're protected. The platform enforces them from the moment you deploy.
- The vendor owns compliance, not you: Certifications like SOC 2 and GDPR are maintained by the platform provider and renewed on their schedule, not yours.
- Access follows roles, not trust: Users only reach the data they're permitted to see. There's no "grant access later" step.
The distinction is architectural. Bolt-on systems give you security features to configure. Built-in systems ship with those features already enforced.
What to require before deploying AI at your company
Before approving any AI platform for production use, confirm it meets these requirements:
- Active compliance certifications: SOC 2 Type II is the baseline for enterprise SaaS. GDPR compliance is required for any organization processing the personal data of EU residents, regardless of where the company is based. HIPAA enablement is non-negotiable for healthcare.
- Role-based access control (RBAC): Different roles need different access levels. Admins should control who can build agents, which users can access sensitive data, and which data sources get connected.
- Data residency options: For regulatory or contractual reasons, you may need to host data in specific regions. Platforms should offer EU or US hosting at minimum.
- Agent-level permissions: AI agents should inherit permissions from the data sources they access. If a user cannot view a Slack channel, an agent they invoke should not surface messages from that channel either.
- Single Sign-On (SSO) support: Authentication should integrate with your existing identity provider through SAML or OAuth. Multi-factor authentication (MFA) should be enforceable at the workspace level.
- Audit and activity logs: Every query, data access, and configuration change should be logged for security reviews and compliance audits.
- End-to-end encryption: Data at rest should use AES-256. Data in transit should use TLS 1.2 or higher.
- Zero data retention by LLM providers: When your platform sends a prompt to OpenAI, Anthropic, or Google, those providers should not store the request or response for their own purposes.
How Dust handles privacy and governance out of the box
Dust is a platform that lets teams build custom AI agents connected to company data sources like Slack, Notion, Google Drive, Salesforce, GitHub, and cloud data warehouses like Snowflake and BigQuery. Unlike general-purpose AI systems, Dust is designed for enterprises where data access, permissions, and compliance are foundational requirements.
The platform combines flexibility with enterprise controls:
- No-code agent builder: Create AI agents without writing code. Simple agents can be configured quickly; more complex deployments connected to multiple data sources take longer depending on your setup.
- Model-agnostic: Use OpenAI, Anthropic, Google, Mistral, and other LLM providers. You can switch models without rebuilding your agent's structure.
- 50+ integrations: Connect Dust to the tools your team already uses.
- Cross-functional deployment: Teams across sales, support, legal, engineering, and operations use Dust to automate workflows and access company knowledge.
Privacy and governance are built into Dust's architecture at every layer:
- SOC 2 Type II certified, GDPR compliant, enables HIPAA compliance: Dust maintains current compliance certifications with recurring audit periods and operates under GDPR data protection requirements.
- Spaces-based access control: Agents access data through Spaces, and they inherit the permission requirements of every Space whose resources they use. A user can only invoke an agent if they have access to all of its referenced Spaces. Restricted Spaces are accessible only to designated members, ensuring agents never surface data outside their configured boundaries regardless of who invokes them.
- Regional hosting: Enterprise customers can choose to host their data in the EU or US to meet regulatory or contractual requirements.
💡 Want to see how Dust handles permissions and governance in practice? Start your free trial →
Why Vanta chose Dust
Vanta is the number one Agentic Trust Platform, helping companies manage compliance, risk, and customer trust workflows. When their GTM organization needed an AI platform to automate workflows, they evaluated seven different platforms.
Most were either too shallow for enterprise use or too technical for widespread adoption. Dust struck the right balance between being simple enough for individual builders and extensible enough for ops and engineering to scale programmatically.
Vanta built a system of connected AI agents that automate quarterly business review prep by pulling data from finance, GRC, product, and customer feedback systems. What used to take hours of manual work now happens in minutes.
The result: Vanta saves around 400 hours per week on QBR prep alone, translating to thousands of hours reclaimed annually across GTM.
Privacy and governance checklist: what to look for in any AI platform
Requirement | What to verify |
Compliance certifications | SOC 2 Type II, GDPR, HIPAA enablement (request reports or check trust center) |
Access controls | RBAC with role assignment, SSO/SAML support, MFA enforcement |
Data security | AES-256 encryption at rest, TLS 1.2+ in transit, regional hosting options |
Audit capabilities | Activity logs for all queries, data access, and configuration changes |
Frequently asked questions (FAQs)
Can AI platforms be GDPR compliant?
Yes. GDPR compliance depends on how a platform processes personal data, not whether it uses AI. Key requirements include processing data lawfully under a valid legal basis, signing Data Processing Agreements with vendors, respecting data subject rights (access, deletion, portability, and more), encrypting data at rest and in transit, restricting cross-border transfers to approved regions, and implementing data protection by design. This is a partial summary; full GDPR compliance covers additional obligations including breach notification, records of processing, and data minimization principles.
What's the difference between built-in and bolt-on privacy in AI platforms?
Built-in privacy means security controls are embedded in the platform from day one, while bolt-on privacy requires you to configure them after deployment. With built-in privacy, encryption, access controls, and audit logging are active by default. Bolt-on systems offer the same features but need manual setup, which creates risk if steps are skipped or misconfigured.
What is a Data Processing Agreement (DPA)?
A Data Processing Agreement is a contract between you and an AI vendor that defines how your data will be processed, stored, and protected. Under GDPR, any vendor processing personal data must sign a DPA outlining their security measures, data retention policies, and breach procedures. The DPA should confirm that your data will not be used for model training, specify where data is stored, and list compliance certifications. Review the DPA before deployment to ensure it meets your legal requirements.