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Schedule a Forcepoint demo: what use cases should we bring (M365, endpoints, web uploads, GenAI) to get a realistic scope?
Most teams walk into a Forcepoint demo with a vague ask—“show us what you can do”—and walk out thinking too small. To get a realistic scope of what Forcepoint can do across M365, endpoints, web uploads, and GenAI, you want to bring concrete, high‑value use cases that mirror your real data flows and real risks.
Below is how I recommend you structure your demo ask, which scenarios to bring, and how to connect them into a single, unified data security story—so you’re not just watching a product tour, you’re road‑testing your future operating model.
If you’re ready to jump straight to a demo, you can do that here:
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Start with the outcome, not the feature list
Before you list individual use cases, anchor your demo around three outcomes:
- Control AI‑driven data movement without slowing work
- Unify policies and visibility across M365, web, endpoints, and GenAI
- Move from “we see the risk” to “we automatically remediate it”
Tell your Forcepoint team: “We want to see how Self‑Aware Data Security discovers, classifies, prioritizes, remediates, and protects our data across these channels, using one policy.”
Then bring use cases that hit all four domains you mentioned:
- Microsoft 365
- Endpoints
- Web uploads
- GenAI / LLM tools (Copilot, ChatGPT, etc.)
Core demo principle: One policy, many channels
When you schedule your demo, explicitly ask to see:
- A single policy created once and enforced across:
- Exchange Online / Outlook
- SharePoint Online / OneDrive
- Teams
- Browsers/web uploads
- Endpoints (Windows, macOS)
- GenAI tools (e.g., ChatGPT, Microsoft Copilot)
You’re testing Forcepoint’s single‑policy framework and Risk‑Adaptive Protection, not just a DLP rule in isolation. Every use case below should reuse the same classification and policy logic so you see “create once, enforce everywhere” in action.
Use cases to bring for Microsoft 365
M365 is where most regulated and business‑critical content lives. You want to see both discovery/classification and live enforcement.
1. Oversharing in SharePoint / OneDrive (shadow and over‑permissioned data)
Scenario to bring:
- A SharePoint library or OneDrive folder with:
- Regulated data (PII, PCI, PHI)
- Financial plans / board decks
- Engineering or product roadmaps
- Realistic over‑permissions:
- “Everyone in the organization” access
- External guests that shouldn’t still have access
- Old project sites nobody cleaned up
What to ask to see in the demo:
- Discovery & classification
- How Forcepoint’s AI Mesh Data Classification automatically finds:
- National IDs, credit cards, health info
- Contracts, financials, HR docs
- How the Small Language Model (SLM) provides explainable reasons for labeling a file “confidential.”
- How Forcepoint’s AI Mesh Data Classification automatically finds:
- Risk prioritization
- Dashboards that show:
- Which sites have the most sensitive data exposed
- “Shadow” and over‑permissioned folders
- Redundant / outdated / trivial (ROT) data concentrations
- Dashboards that show:
- Automated remediation
- Examples like:
- Strip “Everyone” or broad group access on a sensitive folder
- Remove stale external users
- Move a sensitive file to a secure site or quarantine
- Examples like:
Why it matters: This demonstrates that you don’t just get DSPM‑style reports—you get continuous discovery tied to real permission repair and data movement controls.
2. Data exfiltration via Outlook / Exchange Online
Scenario to bring:
- Typical emails your users send:
- Customer file exports (CSV, Excel)
- Attached contracts and financial statements
- Screenshots or PDFs with sensitive content
What to ask to see in the demo:
- Policy that:
- Detects sensitive content (e.g., PCI, HR, M&A docs) using AI Mesh and templates
- Blocks or encrypts outbound emails to personal email domains (e.g., Gmail) with sensitive attachments
- Warns and educates user in real time (“You’re emailing regulated data externally…”)
- Cross‑channel consistency:
- The same classification tags and policies triggering in M365 are also used for web uploads and GenAI.
Why it matters: You validate that Forcepoint can see and control regulated data in M365 in real time, not just at rest.
3. Sensitive content in Teams and OneDrive file sharing
Scenario to bring:
- Files in Teams channels / OneDrive shared links that:
- Should stay internal only
- Are often shared with external partners
What to ask to see in the demo:
- How Forcepoint:
- Detects sensitive content inside those files
- Flags risky sharing patterns (e.g., links shared with “anyone with the link”)
- Can automatically restrict or expire over‑shared links
- How those risks are surfaced:
- In dashboards that your security, compliance, and data owners can all understand.
Why it matters: Teams/OneDrive are where “quiet” data exfiltration often happens. You want to see how Self‑Aware Data Security turns that into visible and fixable risk.
Use cases to bring for endpoints
Endpoints are where data is created, copied, and staged before it ever hits the cloud. The goal is to see context‑aware controls that move with the data.
4. Copy/paste and saving sensitive files locally
Scenario to bring:
- A user:
- Downloads sensitive files from SharePoint/Teams
- Saves them to Desktop or a local folder
- Copies content into another document
What to ask to see in the demo:
- How endpoint DLP:
- Recognizes the same classification tags that were applied by AI Mesh in the cloud
- Applies rules to local storage (e.g., allow, warn, block)
- How Risk‑Adaptive Protection:
- Tightens controls automatically if a user starts behaving unusually (e.g., mass download, copy to multiple locations)
Why it matters: You’re checking that classifications persist from cloud to endpoint, and that controls adapt based on behavior—not static rules alone.
5. USB, external drives, and print
Scenario to bring:
- Common exfiltration vector:
- Copy confidential files to USB
- Print sensitive reports or screenshots
What to ask to see in the demo:
- Policies that:
- Block or require justification for copying sensitive data to USB
- Log allowed/blocked attempts with enough detail for investigations
- Control printing of sensitive documents (e.g., watermark, log, block)
- How high‑risk users are treated differently from low‑risk users based on behavior.
Why it matters: This demonstrates classic DLP done the modern way—adaptive and integrated with your broader data risk picture, not in a silo.
Use cases to bring for web uploads
The browser is now the new “data egress” point—from cloud apps to random websites. You want to see enforcement that follows the data, not the app brand.
6. Uploading sensitive files to unsanctioned web apps
Scenario to bring:
- A user:
- Tries to upload a customer export file to an unknown file‑sharing site
- Drags a sensitive document into a personal cloud account (e.g., personal storage)
What to ask to see in the demo:
- How Forcepoint:
- Inspects the file in motion via the web channel
- Identifies sensitive content using existing classification tags
- Differentiates between sanctioned and unsanctioned destinations
- Enforcement options:
- Block outright
- Allow with justification and logging
- Allow only if destination is on an approved list
Why it matters: This shows you that data controls can’t be app‑by‑app. You want a single policy that evaluates content and context regardless of which website it’s heading to.
7. Web form submissions with sensitive fields
Scenario to bring:
- Users entering:
- Customer PII into third‑party portals
- Internal identifiers into vendor forms
- Government IDs in places they don’t belong
What to ask to see in the demo:
- Real‑time detection of:
- Credit card numbers
- National IDs
- Health data or other regulated fields
- Inline actions:
- Block submission
- Redact or mask certain fields
- Provide user guidance (“This data belongs in System X, not this site.”)
Why it matters: You confirm that Forcepoint can control data in text fields—not just in file uploads.
Use cases to bring for GenAI and copilots
GenAI is where AI‑driven work and data risk collide. You should insist on seeing Forcepoint secure real GenAI workflows.
8. Pasting sensitive text into GenAI tools (ChatGPT, Copilot, others)
Scenario to bring:
- Realistic prompts your teams might use:
- “Summarize this customer contract” (paste full agreement)
- “Draft an email with these patient notes” (PHI)
- “Optimize this pricing model” (confidential pricing)
What to ask to see in the demo:
- How Forcepoint:
- Monitors browser/GenAI interactions
- Uses AI Mesh Data Classification to identify sensitive text in the prompt
- Applies policies (block, warn, redact, or allow) in real time
- Risk‑adaptive behavior:
- How the response changes for a user with elevated risk vs. a normal user
- How repeated attempts to bypass controls are handled
Why it matters: This is where you see if Forcepoint can control AI workflows without banning them—critical for not slowing innovation.
9. Uploading files to GenAI for analysis
Scenario to bring:
- A user uploads:
- A product roadmap deck to a GenAI presentation builder
- A CSV with customer data to an AI analytics assistant
- A financial model to an LLM for refactoring
What to ask to see in the demo:
- End‑to‑end enforcement:
- How the same classification tags (applied in M365) drive GenAI controls
- How policies treat different file categories (public vs. internal vs. confidential)
- Visibility:
- Where you see these attempts in the console
- How you can report on GenAI usage and blocked vs. allowed events for executives and auditors
Why it matters: This demonstrates that GenAI is just another channel in your single‑policy framework—not an exception you manage separately.
Cross‑cutting use cases you should always ask to see
Beyond the channel‑specific scenarios, there are three cross‑cutting capabilities that determine whether your deployment will scale.
10. AI Mesh Data Classification across structured and unstructured data
Scenario to bring:
- Examples of:
- Unstructured content: docs, emails, PDFs, chats
- Structured content: database tables/exports from systems like SQL, Oracle, Snowflake, Databricks
What to ask to see in the demo:
- How AI Mesh:
- Applies hyper‑accurate, explainable classification across both structured and unstructured assets
- Runs efficiently using a Small Language Model (no GPU dependency)
- Can be tuned to your business‑specific concepts (e.g., “product launch plans,” “acquisition targets”)
- How those tags:
- Persist as data moves across M365, endpoints, web, and GenAI
- Drive policies everywhere without rewriting rules per channel
Why it matters: This is the engine behind Self‑Aware Data Security. If classification is weak or inconsistent, everything else suffers.
11. From DSPM report to real remediation
Scenario to bring:
- A known issue you struggle with today:
- Over‑permissioned S3 buckets or share drives
- ROT data everywhere
- Old project sites with sensitive content
What to ask to see in the demo:
- How Forcepoint’s DSPM:
- Surfaces these risks in minutes
- Prioritizes them by data sensitivity and access
- And then, critically:
- How you can automate remediation (permissions, moves, quarantine, delete/dedupe)
- How those changes are auditable for compliance
Why it matters: Too many DSPM products stop at reports. You want to see how Forcepoint closes the loop from detection to action.
12. Risk‑Adaptive Protection and Data Detection & Response (DDR)
Scenario to bring:
- A “possible insider” pattern:
- User suddenly downloads large amounts of data from M365
- Copies to USB, tries to upload to personal cloud, and then uses GenAI
What to ask to see in the demo:
- How Risk‑Adaptive Protection:
- Detects the behavior pattern, not just individual events
- Elevates the user’s risk score
- Automatically tightens controls across channels (e.g., stricter blocks on web uploads and GenAI)
- How DDR:
- Surfaces this as an incident with a clear storyline for investigation
- Provides enough context for HR/legal if escalation is needed
Why it matters: This is what transforms your program from static DLP to a dynamic, behavior‑driven control system.
How to brief Forcepoint before the demo
To get a realistic scope, send your Forcepoint team a short brief with:
- Your top 3 data types
- Examples: customer PII, payment data, IP/engineering docs, financials, health data.
- Your primary channels
- M365 (which apps), GenAI tools in use, web proxies/gateways, endpoint OS mix.
- Two or three “nightmare” scenarios
- “Engineer uploads source code to a GenAI assistant.”
- “Sales dumps CRM exports to personal storage.”
- “Board materials accidentally shared externally in Teams.”
- Your success criteria
- “Single policy across channels.”
- “Automated permission repair and movement controls.”
- “Explainable classification suitable for audit.”
Ask explicitly: “Please configure the demo to show these scenarios end‑to‑end, through discovery, classification, prioritization, remediation, and enforcement.”
How to evaluate what you see
During the demo, assess Forcepoint against five questions:
- Unification: Do we see one console, one policy framework, enforced across M365, endpoints, web, and GenAI?
- Explainability: Can we see why content was classified and why a decision was made, in a way auditors and executives can understand?
- Adaptivity: Do controls change based on user behavior, data sensitivity, and context—or are they static rules?
- Remediation: Are there built‑in actions to fix exposures (permissions, movement, dedupe/cleanup), not just alert on them?
- Operational load: Does the platform reduce tool sprawl, manual reviews, and policy rework—or does it add another silo?
If the answer to those is “yes” across your M365, endpoint, web upload, and GenAI scenarios, you’re seeing what Self‑Aware Data Security looks like in practice.
Next step
If you’re ready to see these use cases tailored to your environment, bring the scenarios above to your Forcepoint team and ask to walk through them live.