Netskope watches the traffic. Wald understands the AI.

Keep Netskope on your web, cloud and SaaS traffic. Add Wald as the DLP for employee AI use, reading context and intent on-device before a prompt ever reaches the model.

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THE GAP

Your DLP was built for files. Your risk now travels as language.

Patterns, keywords and classifiers can tell you what data looks like. AI risk often depends on what the user means, what they’re asking and what they’re trying to do.

Data and interaction comparisonData and interaction comparison
1. PATTERN, NO RISK

A match isn’t always a threat.

  • A 9 digit SKU looks like an SSN.
  • Synthetic data triggers a policy.
  • Legitimate work gets flagged.
The pattern is right. 
The decision is wrong.
2. RISK, NO PATTERN

Risk doesn’t always look sensitive.

  • An unannounced layoff.
  • Pre-release financials.
  • An M&A code name.
The risk is real.  The pattern isn’t.
3. RIGHT SIGNAL, WRONG MOMENT

Detection isn’t preventionif it’s too late.

Monitoring that reads a prompt after it’s sent can’t take it back. By then, the exposure has already happened.

The right decision has to happen before the model sees it.

AI security needs to understand the interaction, not just match the data.

The Challenge

Still think you can configure a policy for this?

Let's put that to the test. Here's a real prompt with no obvious sensitive data, keywords or patterns to match.

Same prompt, two checks. Pick a category and run it: one check looks for patterns, the other reads what the prompt means.

Rule-based DLP

Checks keywords, regex, patterns
No credential No key No connection string No secret to detect
Allowed. 0 matches.The full prompt goes to the model.

Wald DLP

Checked on-device
Flagged as sensitive.No pattern needed. Wald reads the meaning.

No rules written. No patterns defined. No tuning cycle.

Rule-based DLP can't detect what the pattern can't see

01

You can't configure a policy for this.

Nothing here is a secret in the pattern sense. The risk is the described weakness itself, an exploitable gap explained in fluent English to a third-party model.

02

And if you did, you'd build a blanket policy.

The nearest anchor is “token.” A rule built around it catches every authentication ticket, every API question, every engineer describing routine work.

03

Which creates a false positive problem.

Your security team spends its week triaging its own developers. Real architecture disclosures arrive in different words and may never match the rule.

EXISTING STACK

Keep Netskope. Add AI-native DLP.

Netskope keeps securing web, cloud and SaaS traffic exactly as it does today. Wald sits at the point of interaction to govern what employees share with AI, including desktop apps, agents and MCP connections.

Netskope SWG and CASB alongside Wald's contextual AI security layer for AI apps, agents, MCP and custom AINetskope SWG and CASB alongside Wald's contextual AI security layer for AI apps, agents, MCP and custom AI
SAME FOUNDATION. A STRONGER SECURITY POSTURE FOR THE AI ERA.
SIDE BY SIDE

Two layers. Two different jobs.

Netskope applies inline DLP and user coaching to ChatGPT and other AI apps. Wald checks each interaction on the device, by what it means, before it’s sent.

Netskope
Wald
Inspects AI prompts and uploads
Yes. Inline DLP and user coaching apply to ChatGPT and other AI apps.
Yes. Every interaction is checked on-device before it’s sent.
Where the prompt is checked
In Netskope’s cloud, after traffic is steered and decrypted.
On the device. The prompt never goes to another cloud to be checked.
What it evaluates
The data: identifiers, patterns and classifiers.
The interaction: what the user is asking the model to do.
Risk with no sensitive pattern
Caught when a policy or classifier can describe it.
Caught from context.
What it takes to deploy
Inline SWG/CASB licensing and traffic steering.
Rolled out through your existing MDM. No traffic rerouting.
Web, cloud and SaaS traffic
Core strength. Keep it.
Not in scope. Wald works alongside Netskope.

Netskope capabilities based on Netskope’s published product information, November 2026.

OUTCOME

Make AI security work the way your teams need it to.

More protection. Less manual work. A smoother experience for every team.

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No manual policy training needed.

Wald automatically understands the context of an AI interaction and enforces the right policy in real time.

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Fewer false positives.Superior accuracy.

Context helps distinguish legitimate work from genuine risk, so security teams spend less time reviewing unnecessary alerts.

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Seamless acrossplatforms.

Checked on the device, not in another cloud. Windows and macOS, with no traffic rerouting.

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Scan and scale.

Scan unlimited files across AI workflows and static docs without any extra charges.

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See how Wald fits alongside your Netskope deployment.