Ask Sophia: Data Retention
Automate retention and minimization, find expired files, archive or delete with proof, cut risk, cost, and audit time.
Ask Sophia: Data Retention
Automate retention and minimization, find expired files, archive or delete with proof, cut risk, cost, and audit time.
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 risk in retention is deleting blindly or keeping sensitive data
long past its purpose because metadata rarely tells you who the data belongs
to or whether it still has a valid reason to exist.
LightBeam solves that by tying sensitive records back to the people they
represent and extracting retention dates from the content itself so teams
can remove duplicate and stale data defensibly with audit-ready evidence.
Let me pull up how identity-aware retention turns stale, sensitive data into
something you can actually delete defensibly.
Going ahead, the policy screen lets you codify those triggers,
adding rules that flag suspicious activity, ransomware-style behavior,
and the exact retention windows you just defined so the system can act
without generating noise.
Next, the retention view surfaces every document whose embedded dates
indicate it's past its life cycle, giving you a real-time map of
aging, over-retained data across the estate.
Once you're done with that, clicking a specific rule opens its detail panel where
you see the exact expiration criteria, the objects in scope, and the
current review status, a clear audit trail for any regulator.
From here, the criteria page lets you fine-tune the rule.
For example, set the signing date plus a seven-year threshold or a last
modified cutoff and bind the appropriate action, like deletion or
archiving, to shrink your attack surface.
Right after that, the repositories view shows exactly which storage locations
the rule touches, so you can prioritize the buckets that hold the most at-risk
contracts.
Moving on, the alerts pane surfaces any files that have already slipped past the
policy so you can quickly review and confirm over-retained items before they
become compliance liabilities.
Following that, the automation builder lets you translate those alerts into
defensible deletion jobs, removing unnecessary copies without manual
handoffs.
And then, in settings, you define the business logic, telling LightBeam that
the signing date inside a contract is the clock start, not the file's creation
timestamp.
Now, because the engine evaluates retention based on the actual signing date
extracted from the document, you get a defensible, audit-ready timeline that
eliminates guesswork and reduces exposure.
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 risk in retention is deleting blindly or keeping sensitive data
long past its purpose because metadata rarely tells you who the data belongs
to or whether it still has a valid reason to exist.
LightBeam solves that by tying sensitive records back to the people they
represent and extracting retention dates from the content itself so teams
can remove duplicate and stale data defensibly with audit-ready evidence.
Let me pull up how identity-aware retention turns stale, sensitive data into
something you can actually delete defensibly.
Going ahead, the policy screen lets you codify those triggers,
adding rules that flag suspicious activity, ransomware-style behavior,
and the exact retention windows you just defined so the system can act
without generating noise.
Next, the retention view surfaces every document whose embedded dates
indicate it's past its life cycle, giving you a real-time map of
aging, over-retained data across the estate.
Once you're done with that, clicking a specific rule opens its detail panel where
you see the exact expiration criteria, the objects in scope, and the
current review status, a clear audit trail for any regulator.
From here, the criteria page lets you fine-tune the rule.
For example, set the signing date plus a seven-year threshold or a last
modified cutoff and bind the appropriate action, like deletion or
archiving, to shrink your attack surface.
Right after that, the repositories view shows exactly which storage locations
the rule touches, so you can prioritize the buckets that hold the most at-risk
contracts.
Moving on, the alerts pane surfaces any files that have already slipped past the
policy so you can quickly review and confirm over-retained items before they
become compliance liabilities.
Following that, the automation builder lets you translate those alerts into
defensible deletion jobs, removing unnecessary copies without manual
handoffs.
And then, in settings, you define the business logic, telling LightBeam that
the signing date inside a contract is the clock start, not the file's creation
timestamp.
Now, because the engine evaluates retention based on the actual signing date
extracted from the document, you get a defensible, audit-ready timeline that
eliminates guesswork and reduces exposure.
