Invisible Technologies Case Study - Full Video

You know, nobody wants to be in an article negatively, so
not all press is good press.
Hi, I'm Patrick McKinney.
I'm the head of Security and Invisible Technologies.
And coming into Invisible Privacy
and data security was very manual.
So we're looking at tooling to identify sensitive data
and how we can automate remediations
and privacy controls across the board.
And so our data stays in our environment while it's being
scanned and categorized and labeled.
And Lightbeam has a very easy UX ui and deploying lightbeam
and Google Cloud was super simple.
It took less than an hour data to all set up,
and then we were scanning within two hours of deployments.
And unstructured data is one
of those things in the DSPM space.
It's not easy to get right all the time, you know,
with accuracy and the accuracy.
Post scan, post labeling was dead on.
They started showing us where our at risk data was.
We could start getting some of those wins
around the ROI came very quickly at a reasonable price,
DS a r requests that would come to me
through forwarded emails and having lightbeam
and creating a centralized point of intake
for those DS a s here.
And it took down the level of effort by about 80, 90%.
For US. Data security at Invisible is around protecting ip.
As we're using ai, we're using it responsibly
and making sure that data that doesn't need
to go into the AI models is in fact not trained on
and doesn't go into those models.
And really just using this as the single pane
of glass in the first lens of our look
into our data as a whole.
So it's gonna be great.