AI Guardrails for Enterprise Data: Lightbeam AI Data Security Demo
Lightbeam inspects AI interactions in real time, connects them to the Data Identity Graph, and takes action to block, redact, remediate, or audit sensitive data exposure.
AI Guardrails for Enterprise Data: Lightbeam AI Data Security Demo
Lightbeam inspects AI interactions in real time, connects them to the Data Identity Graph, and takes action to block, redact, remediate, or audit sensitive data exposure.
Transcript
AI is already helping teams move faster.
Developers are using Claude to write code, exposing source code and
secrets. Marketing teams are using ChatGPT to draft
content, exposing confidential mergers and acquisitions.
Finance teams are using Copilot to analyze business data, exposing
confidential financial information.
But inside those everyday AI interactions can be your most sensitive
assets: proprietary code, confidential transactions, and
non-public financial data. That gap is already material.
One in five organizations have experienced an AI-related breach.
60% of AI-related incidents involve sensitive data, and the average
US breach now costs $10.2 million.
AI risk is not just about data types.
It is about whose data is involved. A credit card number belongs to a customer.
Social Security number or pay stub belongs to an employee.
Source code belongs to the business as intellectual property.
To govern AI safely, you need to understand not just that sensitive data
exists, but whose data it is and why it matters.
This is where Lightbeam is different.
The Data Identity Graph connects the identity of the data subject to the role
or entity accessing the data, the sensitivity of the information, the
business purpose for using it, and the policy that should govern it.
That context is what turns AI security from simple detection into
precise decisions. First, Lightbeam stops shadow AI.
It inspects AI interactions in real time and prevents sensitive data from
being sent to unauthorized AI tools.
Here, a user attempts to upload a sensitive file to Claude, and
Lightbeam blocks that interaction before the data is ingested by the AI
system. Second, Lightbeam applies AI data security policies.
It discovers and classifies sensitive data in prompts, files,
responses, and attachments. It can determine whether content contains real
sensitive data, connect it to existing enterprise context, and apply
the right policy before AI use proceeds.
Lightbeam can also redact sensitive information in real time.
If an AI response includes regulated or confidential data, Lightbeam can
remove the sensitive values before the user sees them while preserving the
safe parts of the answer. Third, Lightbeam enforces least privilege AI
access. AI often uses the permissions your organization has already
granted. Lightbeam shows which users, copilots, agents, and
connected data sources create exposure, then helps reduce that risk
by tightening access and limiting what AI can reach.
Fourth, Lightbeam governs AI use and risk.
Security teams can see which AI platforms are being used, which prompts
involved sensitive data, which users or departments create the most risk, and
which agents are over-permissioned.
That creates the audit-ready evidence AI governance teams need to prove
controls are working. This is why Lightbeam is different.
Many tools can monitor AI prompts or applications, but visibility
is not enough. Lightbeam connects AI interactions to the Data
Identity Graph and takes action to block, redact,
remediate, or audit sensitive data exposure across petabyte scale
enterprise environments. The goal is not to slow AI down.
It is to make AI safe. See how Lightbeam helps you understand what AI can
reach, whose data is exposed, and which actions reduce risk
fastest.
Developers are using Claude to write code, exposing source code and
secrets. Marketing teams are using ChatGPT to draft
content, exposing confidential mergers and acquisitions.
Finance teams are using Copilot to analyze business data, exposing
confidential financial information.
But inside those everyday AI interactions can be your most sensitive
assets: proprietary code, confidential transactions, and
non-public financial data. That gap is already material.
One in five organizations have experienced an AI-related breach.
60% of AI-related incidents involve sensitive data, and the average
US breach now costs $10.2 million.
AI risk is not just about data types.
It is about whose data is involved. A credit card number belongs to a customer.
Social Security number or pay stub belongs to an employee.
Source code belongs to the business as intellectual property.
To govern AI safely, you need to understand not just that sensitive data
exists, but whose data it is and why it matters.
This is where Lightbeam is different.
The Data Identity Graph connects the identity of the data subject to the role
or entity accessing the data, the sensitivity of the information, the
business purpose for using it, and the policy that should govern it.
That context is what turns AI security from simple detection into
precise decisions. First, Lightbeam stops shadow AI.
It inspects AI interactions in real time and prevents sensitive data from
being sent to unauthorized AI tools.
Here, a user attempts to upload a sensitive file to Claude, and
Lightbeam blocks that interaction before the data is ingested by the AI
system. Second, Lightbeam applies AI data security policies.
It discovers and classifies sensitive data in prompts, files,
responses, and attachments. It can determine whether content contains real
sensitive data, connect it to existing enterprise context, and apply
the right policy before AI use proceeds.
Lightbeam can also redact sensitive information in real time.
If an AI response includes regulated or confidential data, Lightbeam can
remove the sensitive values before the user sees them while preserving the
safe parts of the answer. Third, Lightbeam enforces least privilege AI
access. AI often uses the permissions your organization has already
granted. Lightbeam shows which users, copilots, agents, and
connected data sources create exposure, then helps reduce that risk
by tightening access and limiting what AI can reach.
Fourth, Lightbeam governs AI use and risk.
Security teams can see which AI platforms are being used, which prompts
involved sensitive data, which users or departments create the most risk, and
which agents are over-permissioned.
That creates the audit-ready evidence AI governance teams need to prove
controls are working. This is why Lightbeam is different.
Many tools can monitor AI prompts or applications, but visibility
is not enough. Lightbeam connects AI interactions to the Data
Identity Graph and takes action to block, redact,
remediate, or audit sensitive data exposure across petabyte scale
enterprise environments. The goal is not to slow AI down.
It is to make AI safe. See how Lightbeam helps you understand what AI can
reach, whose data is exposed, and which actions reduce risk
fastest.
