Hi everyone, welcome back to Collaboration Simplified.
Today we're going to talk about three things
that people get wrong when it comes to M365 Copilot
and security. First one, good data hygiene is
good data hygiene. Number two, the LLM does not
know or care about your data. And number three,
Copilot does not have a memory. This is Kitty,
a compliance technical specialist focused on
data security for M365 Copilot. Copilot really
does things on behalf of the user, which means
that if a user has rights to it, then so does
Copilot. If a user doesn't have rights to it,
then Copilot doesn't either. The way I like to
put it is Copilot isn't going to break down the
walls. It's just going to show you where the
holes are. What it highlights is really good
data security practices or bad data security
practices. Everyone has permissions to everything.
Copilot is going to make it easier for them to
surface things that they probably shouldn't have
access to. If you have put good guardrails and
good permissions into place, Copilot is going
to only show them what it is that they need for
productivity. And ultimately, that is what Copilot
is. And that makes a ton of sense, Kitty. So
what are some examples of good... Data hygiene
practices. Permissions. If you have a SharePoint
site, if you have a repository of some kind,
you need to make sure that you review permissions
regularly. Look at the site. Who has access?
Why do they have access? Should they still have
access? Know where your sensitive data is. It's
kind of a no -brainer, but people tend to dump
data into repositories because it's convenient.
They don't think about the potential problems
that having access to this data can cause. So
put data where it's appropriate. Know where your
data is stored. And then lastly, make sure that
you are labeling your data appropriately and
that you have DLP policies in place to catch.
data trying to leave or data being misused. The
second thing that people get wrong about co -pilot
and security, what do you have for us there?
So the second one is all about the actual LLM.
Obviously, LLM is trained on data. And that is,
by the way, when we say LLM, we're talking large
language model. The LLM is an interpreter. It
says, hey, I'm taking what you're saying in natural
language, which is what you were talking about,
and translates that into a search query against
the data stores in M365. And lots of people are
afraid that the LLM is somehow manipulating and
retaining their data. The problem with this is
that if we allow the LLM to do something with
questions that people ask it, or data that is
asked of it, then we open ourselves up to AI
poisoning. And if you want to know what AI poisoning
looks like, do a search in your favorite search
engine of muffin versus dog. And if you do that,
what you'll find is a lot of muffins and dogs,
and they look like each other. Because we're
human, and we have an understanding. of what
a dog is versus what a muffin is, we are actually
able to see the difference and we can easily
distinguish between a dog and a muffin. An AI
can't. And the more muffins you throw at it and
tell it that these are dogs, the more likely
the LLM will be to drift in its model. Microsoft
doesn't actually allow user data into the LLM.
to be retained because we don't want the LLM
to slide from its original purpose and from its
original training. And that training is controlled
very strictly by not only an ethics board that
follows ethical AI standards, but also a group
of engineers who are responsible for training
the model. What all this is to say is that the
LLM does not know. or care about the data that
you input into it it is purely taking the question
and turning it into something that the rest of
the systems can understand without ever really
retaining anything because i think a common concern
is the large language model learning from my
prompts what i type in that the questions that
i ask my data the documents that i share with
it and basically what you're saying is don't
worry, the large language models that we use
are not learning. Correct. And more to the point,
we wouldn't want that in the first place because
that could lead to undesirable results. And I
think we've seen that with other language models
that have been open to the internet and the kind
of drift that has been introduced. What about
like a good scenario for this, right? Like if
certain business has a lot of acronyms or...
does business in a very unique way, and they
actually want the large language model to learn?
Yeah, that's actually a great question. There
are really several ways to solve that. The first
is obviously to create your own model. We're
not going to talk about that because that's not
my area of expertise. The second is actually
a different part. And this is something that
a lot of people misconstrue about Copilot. Copilot
doesn't really have a memory. Copilot's only
memory is something called the semantic index.
Semantic index relates you to your data. What
it's doing is every time that you interact with
Copilot, it is learning about what you mean.
If a company has a lot of acronyms, it has a
lot of things that are unique to that company,
as users in that company use those types of things,
their individual semantic index We'll actually
learn about that. So if I'm talking to you all
the time and I continually use a set group of
words, that is going to be written to my semantic
index, which is individual to me and your index
is individual to you. And it's going to learn
that if I use the acronym. AMS that I mean this
and not that. It is a kind of interesting learning
model that helps the LLM to make the responses
more personalized and more interesting to you.
It also shortcuts how long it takes for the LLM
to respond. So as an example, If I'm looking
for information in documents that I commonly
use, then what the semantic index allows the
LLM and Copilot to do is to say, well, I use
this file all the time. So I'm going to check
there before I go search the entire SharePoint
library that it's available to me. And that makes
the response quicker. It also makes it. the research
results more relevant to you. That is what a
lot of people actually mistake for memory. Copilot,
after you've typed in a prompt, Copilot does
not remember what you typed in. This is why if
you reference a previous session with Copilot,
it's actually not going to be able to get that
information. It looks like it's available, and
that is because the conversations you've had
with Copilot are actually retained in a log of
sorts that's stored in your mailbox. It's not
accessible to you, but it is accessible to the
corporation. and to administrators in order to
be able to do things like e -discovery on them
for security purposes. That information is stored
in the context of that session only. Copilot
really has no access to previous queries. Thank
you for joining. If you did enjoy the content,
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you want to see additional content on security
in M365 Copilot, Let us know in the comments
and I'll catch you on the next one.