Ask Sophia: AI Security
See AI risk in real time. Protect sensitive data by governing GenAI and Copilot with confidence.
Ask Sophia: AI Security
Transcript
Hi, I'm Sophia, LightBeam's AI product guide.
I help security teams understand where sensitive data exists, who can access
it, and where it may be exposed. In this short walkthrough, I'll show you how
LightBeam uses AI to discover sensitive data, connect it to identities,
and help reduce risk across your environment. What would you like to explore?
The real problem is that AI tools can expose sensitive data faster than
most teams can see it, because prompts, files, and user entitlements are
rarely tied together in one control plane.
LightBeam closes that gap by linking every prompt and file to the real
user and access path, classifying sensitive content in real time, and
containing risky activity within seconds with a full audit trail.
Let me pull up how AI prompts, files, and user entitlements connect so
shadow AI risk stops being a blind spot.
The hidden danger is that untagged repositories can feed AI models,
exposing regulated data before anyone knows it exists.
Going ahead, this Copilot data source view maps the sensitive data
landscape inside the AI environment, showing classifications, risk
density, and any unresolved alerts.
Next, the prompt activity pane surfaces the actual messages,
entities, and linked files, letting you pinpoint which interactions are most likely
to create compliance risk.
Once you're done with that, the user prompt view reveals exactly what each person
asked the AI, so you can spot requests for data that should never be shared.
From here, we drill into a single conversation, giving context on whether
the AI response pulled in any protected information.
Right after this, the preview shows the prompt, the AI's answer, and the
associated files so you can verify why the result is flagged as
sensitive.
Moving on, LightBeam surfaces the sensitive content that was actually returned,
letting you judge if the exposure was appropriate.
Following that, the file view lists all documents the AI could reach,
highlighting which assets are in scope for AI-driven leakage.
And then, applying a financial filter narrows the list to high-risk financial
records, speeding the review of regulated data.
Now, we examine a specific high-risk file, inspecting its metadata
and sensitivity rating to decide on redaction or restriction.
After that, the preview confirms the exact data elements that make the file
sensitive, cutting down the time needed for manual analysis.
Finally, revealing the sensitive content inside the file ties the AI
exposure back to the underlying data, giving you a clear audit trail and the
ability to enforce governance instantly.
I help security teams understand where sensitive data exists, who can access
it, and where it may be exposed. In this short walkthrough, I'll show you how
LightBeam uses AI to discover sensitive data, connect it to identities,
and help reduce risk across your environment. What would you like to explore?
The real problem is that AI tools can expose sensitive data faster than
most teams can see it, because prompts, files, and user entitlements are
rarely tied together in one control plane.
LightBeam closes that gap by linking every prompt and file to the real
user and access path, classifying sensitive content in real time, and
containing risky activity within seconds with a full audit trail.
Let me pull up how AI prompts, files, and user entitlements connect so
shadow AI risk stops being a blind spot.
The hidden danger is that untagged repositories can feed AI models,
exposing regulated data before anyone knows it exists.
Going ahead, this Copilot data source view maps the sensitive data
landscape inside the AI environment, showing classifications, risk
density, and any unresolved alerts.
Next, the prompt activity pane surfaces the actual messages,
entities, and linked files, letting you pinpoint which interactions are most likely
to create compliance risk.
Once you're done with that, the user prompt view reveals exactly what each person
asked the AI, so you can spot requests for data that should never be shared.
From here, we drill into a single conversation, giving context on whether
the AI response pulled in any protected information.
Right after this, the preview shows the prompt, the AI's answer, and the
associated files so you can verify why the result is flagged as
sensitive.
Moving on, LightBeam surfaces the sensitive content that was actually returned,
letting you judge if the exposure was appropriate.
Following that, the file view lists all documents the AI could reach,
highlighting which assets are in scope for AI-driven leakage.
And then, applying a financial filter narrows the list to high-risk financial
records, speeding the review of regulated data.
Now, we examine a specific high-risk file, inspecting its metadata
and sensitivity rating to decide on redaction or restriction.
After that, the preview confirms the exact data elements that make the file
sensitive, cutting down the time needed for manual analysis.
Finally, revealing the sensitive content inside the file ties the AI
exposure back to the underlying data, giving you a clear audit trail and the
ability to enforce governance instantly.
