The hype around generative AI is real, but so are AI risks and the pressure to justify your decisions, especially in regulated industries. Whether you’re fielding questions from your board, compliance team or most tech-savvy end users, the conversation always circles back to two things: security and privacy.

We have spent a lot of time digging into the security and privacy documentation for six of the biggest GenAI platforms on the market. Here’s what you need to know before you roll them out.

Microsoft Copilot

If you’re already a Microsoft 365 shop, Copilot is the safest first step. It’s built with regulated industries in mind, layering on encryption, zero trust architecture and data isolation between tenants. Authentication runs through EntraID, so your existing permissions and policies carry over.

Microsoft acts as a data processor under its data processing agreement, and — crucially — your prompts and responses aren’t used to train its models. Web queries are anonymized, and you get full audit logs and eDiscovery through Purview.

Don’t use the consumer version for anything regulated. Stick to enterprise deployments.

OpenAI ChatGPT

ChatGPT is the most popular GenAI platform in the world, but not all versions are created equal. The enterprise, business and API versions offer robust security, SOC 2 Type II and ISO certifications, and external penetration testing. You own your data, and by default, it’s not used for training unless you opt in.

But here’s the catch: The consumer versions (free and plus) are not recommended for regulated environments. Data may be retained and used for training unless you explicitly opt out, and admin controls are minimal.

If you’re in finance, healthcare or any regulated space, opt for enterprise.

Google Gemini

Google’s Gemini is all-in on AI, and its enterprise deployments (via Google Cloud or Workspace) come with strong security features: AES-256 and TLS encryption, identity and access management controls, and VPC Service Controls.

By default, Gemini doesn’t train on customer data unless you opt in. However, consumer-facing Gemini apps don’t offer the same controls and may retain data longer.

If you’re a Google Workspace institution, Gemini is a solid choice. Otherwise, steer clear of the consumer apps for anything sensitive.

Anthropic Claude

Claude has made waves with its Claude for Financial Services model, tailored for analysts, underwriters and portfolio managers. Security is enterprise-grade, and you get zero-data-retention mode and custom retention policies.

Claude does not train on user data by default and offers granular retention controls and opt-in memory features.

If you’re processing large documents or need the strongest privacy protections, Claude is worth a look.

Perplexity

Perplexity is best known for its research and citation capabilities, and the Enterprise Pro for Finance version offers SOC 2 Type II compliance and zero data retention for API interactions.

Consumer versions may retain data and lack strong authentication. Multifactor authentication isn’t built in, and some ambiguity remains in its data usage policies. If you’re considering Perplexity, stick to the enterprise version and review the documentation carefully.

It’s great for deep research, but it shouldn’t replace your core compliance workflows.

DeepSeek

DeepSeek is known for being cost-effective and open source, but it’s not recommended for regulated environments. It lacks a clear encryption standard, appears to offer limited security controls and its servers are in China — meaning your data is subject to PRC law. Data may be retained indefinitely and used for model improvement.

If you must use DeepSeek, self-host it and build your own security and compliance layers. Otherwise, steer clear if you’re handling sensitive financial or personal data.

Key Takeaways

Before deploying GenAI, step back and think strategically about governance, security and privacy.

• Enterprise deployments are your friend. Consumer GenAI tools rarely offer the security you need.
• Governance matters. Establish AI policies, inventory, risk assessments and training before deployment.
• Know your use case. Not every platform fits every need. Be prepared to say no when risk outweighs value.
• The Chief AI Officer seat is real. Start preparing now — AI governance is quickly becoming a board-level concern.

This article was originally published on sbscyber.com.