How to Evaluate DSPM and DLP for Copilot and Gemini: A Security Architect’s Buyer’s Guide
Most security architects didn’t sign up to be AI product managers. Yet that’s what Copilot and Gemini rollouts feel like: “We want this in every business unit, as soon as possible. Make sure it’s safe.”
If you’re being asked to recommend or validate a DSPM platform, or to justify why your existing DLP stack is or isn’t enough, you need a realistic, vendor‑agnostic set of criteria that maps to how Copilot and Gemini actually work.
This guide is written from that perspective: what matters when you evaluate DSPM and DLP for AI assistants, what’s table stakes vs. differentiating, and what you should ask every vendor before you bring them to your steering committee.
1. Start with the AI use cases you actually have
Before you look at tools, clarify your Copilot and/or Gemini scope:
- Are you rolling out Microsoft 365 Copilot to a pilot group, or planning an org‑wide deployment?
- Are you enabling Gemini in Workspace only, or also Gemini for dev teams (Vertex AI, custom LLM apps, RAG)?
- Do you have existing AI initiatives (third‑party SaaS copilots, homegrown assistants) that will access M365 or Google data?
This matters because different tools have very different coverage:
- Some are M365‑centric with shallow Google support.
- Others focus on cloud infrastructure and data warehouses, and barely touch SaaS.
- Very few provide deep, in‑environment visibility across both SaaS and cloud platforms, which is what you need if Copilot/Gemini are just the tip of your AI iceberg.
Define the boundary first; evaluate tools second.
2. Non‑negotiable DSPM capabilities for Copilot and Gemini
When Copilot and Gemini are in scope, “generic DSPM” is not enough. You need specific capabilities that touch how those assistants see and use data.
2.1 Native visibility into M365 and Workspace
At minimum, a viable DSPM platform must:
- Discover and classify sensitive data across SharePoint, OneDrive, Exchange, Teams and Google Drive / shared drives.
- Understand sharing constructs (public/org‑wide links, external guests, shared drives) and relate them to data sensitivity.
- Support unstructured formats including Office docs, PDFs, images, and audio/video files.
Ask vendors:
- “Show me, live, how you discover sensitive data in Teams chats and OneDrive/Drive folders that are Copilot/Gemini‑accessible.”
- “Show me how you handle PDFs, audio, and meeting recordings - not just Word docs and spreadsheets.”
Sentra, for example, was explicitly built to discover sensitive data across IaaS, PaaS, SaaS, and on‑prem, and to handle formats like audio/video and complex PDFs as first‑class sources.
2.2 In‑place, agentless scanning
For many organizations, it’s now a hard requirement that data never leaves their cloud environment for scanning. Evaluate if the vendor scan in‑place within your tenants, using cloud APIs and serverless functions or do they require copying data or metadata into their infrastructure?
Sentra’s architecture is explicitly “data stays in the customer environment”, which is why large, regulated enterprises have standardized on it.
2.3 AI‑grade classification accuracy and context
Copilot and Gemini are only as safe as your labels and identity model. That requires:
- High‑accuracy classification (>98%) across structured and unstructured content.
- The ability to distinguish synthetic vs. real data and to attach rich context: department, geography, business function, sensitivity, owner.
Ask:
- “How do you measure classification accuracy, and on what datasets?”
- “Can you show me how your platform treats, for example, a Zoom recording vs. a scanned PDF vs. a CSV export?”
Sentra uses AI‑assisted models and granular context classes at both file and entity level, which is why customers report >98% accuracy and trust the labels enough to drive enforcement.
3. Evaluating DLP in an AI‑first world
Most enterprises already have DLP: endpoint, email, web, CASB. The question is whether it can handle AI assistants and the honest answer is that DLP alone usually can’t, because:
- It operates blind to real data context, relying on regex and static rules.
- It usually doesn’t see unstructured SaaS stores or AI outputs reliably.
- Policies quickly become so noisy that they get weakened or disabled.
The evaluation question is not “DLP or DSPM?” It’s:
“Which DSPM platform can make my DLP stack effective for Copilot and Gemini, without a rip‑and‑replace?”
Look for:
- Tight integration with Microsoft Purview (for MPIP labels and Copilot DLP) and, where relevant, Google DLP.
- The ability to auto‑apply and maintain labels that DLP actually enforces.
- Support for feeding data context (sensitivity + business impact + access graphs) into enforcement decisions.
Sentra becomes the single source of truth for sensitivity and business impact that existing DLP tools rely on.
4. Scale, performance, and operating cost
AI rollouts increase data volumes and usage faster than most teams expect. A DSPM that looks fine on 50 TB may struggle at 5 PB.
Evaluation questions:
- “What’s your largest production deployment by data volume? How many PB?”
- “How long does an initial full scan take at that scale, and what’s the recurring scan pattern?”
- “What does cloud compute spend look like at 10 PB, 50 PB, 100 PB?”
Sentra customer tests prove ability to scan 9 PB in under 72 hours at 10–1000x greater scan efficiency than legacy platforms, with projected scanning of 100 PB at roughly $40,000/year in cloud compute.
If a vendor can’t answer those questions quantitatively, assume you’ll be rationing scans, which undercuts the whole point of DSPM for AI.
5. Governance, reporting, and “explainability” for architects
Your stakeholders, security leadership, compliance, boards, will ask three things:
- “Where, exactly, can Copilot and Gemini see regulated data?”
- “How do we know permissions and labels are correct?”
- “Can you prove we’re compliant right now, not just at audit time?”
A strong DSPM platform helps you answer those questions without building custom reporting in a SIEM:
- AI‑specific risk views that show AI assistants, datasets, and identities in one place.
- Compliance mappings to frameworks like GLBA, SOX, FFIEC, GDPR, HIPAA, PCI DSS, and state privacy laws.
- Executive‑ready summaries of AI‑related data risk and progress over time (e.g., percentage of regulated data coverage, number of Copilot‑accessible high‑risk stores before vs. after remediation).
Sentra’s AI Data Readiness and continuous compliance materials give a good template for what “explainable DSPM” looks like in practice.
6. Putting it together: A concise RFP checklist
When you boil it down, your evaluation criteria for DSPM/DLP for Copilot and Gemini should include:
- In‑place, multi‑cloud/SaaS discovery with strong M365 and Workspace coverage
- Proven high‑accuracy classification and rich business context for unstructured data
- Identity‑to‑data mapping with least‑privilege insights
- Native integrations with MPIP/Purview and Google DLP, with label automation
- Real‑world scale (PB‑level) and quantified cloud cost
- AI‑aware risk views, compliance mappings, and reporting
Use those as your “table stakes” in RFPs and technical deep dives. You can add vendor‑specific questions on top, but if a tool can’t clear this bar, it will not make Copilot and Gemini genuinely safe - it will just give you more dashboards.
<blogcta-big>
Nikki Ralston is Senior Product Marketing Manager at Sentra, with over 20 years of experience bringing cybersecurity innovations to global markets. She works at the intersection of product, sales, and markets translating complex technical solutions into clear value. Nikki is passionate about connecting technology with users to solve hard problems.
