How can I find secure enterprise software for AI-powered teams?

Davis Christenhuis

-April 30, 2026

How Can I Find Secure Enterprise Software For AI-Powered Teams

Choosing AI software for enterprise teams means balancing capability with security. This guide covers how to evaluate platforms on the security criteria that matter, from compliance certifications to data handling and access controls.

📌 TL;DR

Here's what you need to know about finding secure enterprise AI software:

Step 1 - Define security requirements: Document your data classification, mandatory compliance frameworks, and user access needs before evaluating platforms.
Step 2 - Check compliance certifications: Verify SOC 2 Type II, GDPR, ISO 27001, and industry-specific certifications through the vendor's trust center.
Step 3 - Evaluate data privacy and access controls: Look for encryption standards, role-based permissions, SSO integration, and granular data selection.
Step 4 - Understand data management: Confirm zero data retention, no model training, regional hosting options, and comprehensive audit logging.
Step 5 - Choose an enterprise-grade platform: Select platforms with purpose-built security controls and clearly understand how they manage access to connected data sources.
Step 6 - Involve IT and security teams: Include technical stakeholders during vendor selection to catch risks and prevent contract delays.

Six steps to finding secure AI software for your enterprise

Step 1 - Define your security requirements

Before evaluating any platform, document what security means for your organization. Identify which types of data the AI system will access. Customer information, financial records, and employee data each carry different risk profiles and regulatory obligations. Map your data classification system to understand where sensitive information lives and which teams need access.

Write down your mandatory requirements like compliance frameworks, data residency rules, and audit capabilities. A healthcare company needs HIPAA compliance, while organizations handling sensitive customer data across any industry frequently require SOC 2 Type II certification from their vendors.

Step 2 - Check for compliance certifications

Compliance certifications verify that a platform follows documented security practices. SOC 2 Type II certification demonstrates that a vendor has implemented controls around security, availability, and privacy, and that those controls operate effectively over time.

ISO 27001 certification indicates a comprehensive information security management system covering risk assessment, security policies, incident response, and many other security domains. Look for current certifications rather than "in progress" claims, and verify them through the vendor's trust center.

For regulated industries, check for industry-specific certifications like HIPAA for healthcare or PCI DSS for organizations that store, process, or transmit payment card data. Ask vendors to provide their most recent audit reports and compliance documentation before moving forward.

Step 3 - Evaluate data privacy and access controls

Data privacy in AI platforms comes down to three questions: where does your data go, who can see it, and how long does it stay there. Encryption is table stakes. Data should be encrypted with AES-256 at rest and TLS 1.2 or higher in transit.

Role-based access control lets you assign permissions based on job function, with administrative users getting different access than end users. Single Sign-On integration through SAML 2.0 or OIDC connects the platform to your existing identity provider, centralizing authentication and ensuring access gets revoked when someone leaves.

Granular data selection controls what information the platform can access. Some platforms require all-or-nothing access to connected systems. Better platforms let you specify exactly which folders, channels, databases, or repositories get indexed. This principle of least privilege reduces exposure if a security incident occurs.

Step 4 - Understand how the platform manages your data

The most critical security question is whether your data trains AI models or gets retained by third-party providers. Zero data retention means that when you send a query to an AI model, the provider processes the request and immediately discards both your input and the model's output.

Ask vendors directly: does my data train your models or get used to improve your service? Enterprise platforms should contractually guarantee that your data never enters training pipelines, including third-party model providers the platform uses.

Regional data residency matters for organizations with data localization requirements. Platforms with regional hosting options let you choose where your data lives, whether that's the EU, the US, or other jurisdictions. Check audit logging capabilities as well.

Enterprise security teams need visibility into who accessed what data and when. Comprehensive audit logs track user actions, data queries, system changes, and security events to support compliance requirements and security investigations.

Step 5 - Choose an enterprise-grade AI platform

Purpose-built enterprise AI platforms handle security differently than consumer tools adapted for business use. These platforms connect directly to your company's knowledge systems through dedicated integrations.

Access to data is managed through the platform's own permission layer, typically through workspace or space-level controls that administrators configure. When evaluating a platform, ask specifically whether it inherits permissions from source systems at query time, or manages access independently.

Several platforms built for enterprise security standards include:

Dust: Connects AI agents to company data with SOC 2 Type II, GDPR compliance, and HIPAA enablement. Offers zero data retention, regional hosting, and Space-based access controls across 50+ integrations. Administrators select which data to index and assign it to open or restricted Spaces.
Microsoft Copilot Studio: Integrates deeply with Microsoft 365 environments, inheriting existing security controls and compliance certifications from Azure infrastructure.
Gemini Enterprise Agent Platform (Google Cloud): Provides enterprise-grade security through Google Cloud Platform with data residency options and compliance certifications.
Amazon Q Business (now evolving into Amazon Quick Suite): Built on AWS infrastructure with enterprise security features and integration with existing AWS security services.

Step 6 - Involve IT and security teams early

IT and security stakeholders catch risks that business teams miss. Security teams can evaluate vendor documentation, assess architecture diagrams, and identify gaps that create exposure. IT teams can assess whether authentication mechanisms work with your identity provider and whether API limits align with your usage patterns. Involving them during vendor selection rather than contract negotiation prevents delays and rework.

Schedule a security review session with the vendor and your internal teams. Have the vendor walk through their architecture, data handling practices, and compliance evidence. Request security documentation before signing contracts, including architecture diagrams, penetration test results, and third-party assessments.

How Dust addresses enterprise AI security requirements

Dust is a platform that deploys AI agents safely connected to your company's knowledge and tools. The platform handles security at the infrastructure, data, and access control layers so teams can build and use agents without requiring engineering expertise.

Key features:

50+ integrations: Connect to Notion, Slack, GitHub, Google Drive, Salesforce, and databases through standard integrations or an API.
Model flexibility: Choose between models from OpenAI, Anthropic, Google, and Mistral depending on task requirements and data sensitivity.
No-code builder: Create agents through a visual interface without writing code. Configure agent behavior through natural language instructions.
Chrome extension: Access agents directly in existing workflows without switching contexts.
Usage analytics: Monitor how agents are being used across the organization.

AI security requirements coverage:

Compliance certifications: GDPR Compliant and SOC 2 Type II certified. Enables HIPAA compliance for regulated industries. Verification is available through Dust's public Trust Center.
Encryption and regional hosting: Data is encrypted with AES-256 at rest and TLS in tranit. Host in the EU or US to meet your regulatory needs.
Zero data retention and no model training: No data is stored by third-party model providers. Your data is never used to train models.
Granular data selection: Fully control which data Dust ingests from each source, down to specific folders, channels, or repositories.
Single Sign-On (SSO): Use SSO to manage user access across the workspace (available on Enterprise plan; supports Okta, Entra ID, Jumpcloud).
Private Spaces: Use private spaces for sensitive data, restricting access by role.

💡 Need AI that meets your enterprise security standards? Start your free 14-day trial →

Summary

Aspect	What to evaluate
Security & Encryption	AES-256 at rest, TLS 1.2+ in transit, data isolation
Compliance Certifications	SOC 2 Type II, GDPR compliance, HIPAA enablement,
Data Privacy	Zero retention policy, no model training on your data
Data Residency	Regional hosting options (EU, US, or other jurisdictions)
Access & Permissions	SSO, role-based permissions, granular data controls
Integration Capabilities	Works with your existing identity provider and enterprise systems
Monitoring & Auditing	User activity tracking, data access logs, compliance reporting
Vendor Transparency	Public trust center, security documentation, audit reports

💡 Case Study: See how Vanta evaluated 7 AI platforms and chose Dust. Read the full story →

Frequently asked questions (FAQs)

What's the difference between SOC 2 Type I and Type II certification?

SOC 2 Type I confirms a vendor has designed appropriate security controls at a single point in time. Type II tests whether those controls operate effectively over a few months. Type II provides stronger assurance because it verifies sustained security practices, not just documentation. When evaluating vendors, prioritize Type II certification. Ask when the audit period ended and when the next audit begins to ensure certifications stay current.

What should I look for in an AI platform's data retention policy?

Look for "zero retention" language in the vendor's terms of service. This means your queries and outputs are not stored by the platform or any third-party model providers. Ask whether this applies to all model providers they use, including OpenAI, Anthropic, and Google. Some platforms retain data for 30 days or longer, creating unnecessary exposure. Enterprise platforms should contractually guarantee zero retention and provide documentation showing how they enforce this with downstream providers.

What's the biggest security risk most companies overlook when adopting AI?

Permission inheritance from connected systems. Many AI platforms index data from Slack, Notion, or Google Drive without respecting existing permission boundaries. This means users could access information they shouldn't see in those source systems. When evaluating platforms, ask whether the AI respects existing permissions. Test this by connecting a restricted data source, then query as a user who shouldn't have access. Permission-aware platforms check user access rights before surfacing information, preventing data leakage.