Imagine for a moment you're sharing an AI conversation
with a colleague. Maybe it's a brilliant new
marketing idea or even a sensitive project proposal.
What if that whole discussion full of private
details just suddenly popped up on Google? For
anyone to see, this isn't some far -fetched fear.
It was actually a very real thing for a lot of
people just recently. Yeah, it was a proper wake
-up call, wasn't it? Kind of like finding your
private diary. just displayed in the town square,
a true stark reminder of our digital footprint
these days. Welcome to the Deep Dive. Today we're
digging into a pretty critical incident from
mid -2025. It was when a technical glitch exposed
private chat GPT conversations to the public
internet. But this deep dive isn't just about
that one platform, right? It's really a bigger
lesson in digital privacy for all of us. Exactly.
Our mission today is, well, first to help you
understand what actually happened, then guide
you through the immediate steps to clean up any
potential past exposures from that. After that,
we'll kind of zoom out. look at other AI data
risks lurking around. Then we'll talk about building
safer habits, definitely. And we'll even show
you a smarter, much more secure way to share
AI -generated stuff. And finally, yeah, we'll
unpack this really sophisticated prompt that
basically acts like your own personal AI safety
co -pilot. OK, so it's all about understanding
what went wrong, how to react now. and crucially,
how to build a much safer workflow going forward
in this AI world. Let's unpack this. So this
specific incident with ChatGPT where the shared
links somehow got indexed by search engines,
it really exposed a kind of hidden vulnerability.
Now, OpenAI, they acted quickly to fix it, which
is good. But even with that fix, some data, like
a digital residue, might still be lingering out
there, just floating around. That's exactly right,
like a digital ghost in the machine, as you said.
So the first... And honestly, the most fundamental
step, if you've ever created a shared link through
ChatGPT, is simply to delete it right from inside
the platform. Go log into your account, click
on your profile name, usually bottom left corner,
then navigate to Settings. From there, head over
to Data Controls and you'll see Shared Links.
Just review that list carefully. For each link
you find, click the little three dot icon, the
vertical one, and select Delete Shared Link.
Doing that makes the old URL return a 404 not
found error. So it breaks the link. That's the
start. OK. But a 404, while good, isn't always
the complete end of the story, is it? Like the
title or maybe a small snippet of that conversation
might still hang around in search results for
a bit, like an echo. Precisely. That echo is
the problem. And that's where the second crucial
step comes in. You need to actively speed up
its complete removal from Google search index
using their own tool. So after you've deleted
the link inside ChatGPT, you go straight to Google's
remove outdated content tool. Once you're there,
you click new request, paste in that ChatGPT
URL you just deleted, and just follow their instructions.
They're pretty straightforward. Google usually
processes these requests in maybe a few hours.
Sometimes it can take up to two or three days
for it to fully vanish from the search results,
but it gets it done. So just to be crystal clear,
then, if we're talking about the single most
crucial non -negotiable action for a really complete
cleanup, what is that? It's definitely a two
-part dance. You absolutely have to delete the
link inside ChatGPT first. But critically, you
must follow that up by using Google's tool to
tell them to remove the outdated content from
their search results. You need both steps. One
without the other isn't really a full cleanup.
Right. Got it. Okay, so this chat GPT thing was
no doubt a big deal, a really stark reminder
about how fragile digital privacy can be sometimes.
But connecting this to the bigger picture, it
does feel like maybe just the tip of the iceberg,
doesn't it? The risk isn't just about ChatGPT.
Oh, absolutely. While ChatGPT got all the headlines,
you know, other AI models like Google Gemini
or Claude, they have their own ways of sharing
their own data policies. You really need to look
into those, too. But beyond just sharing, there
are other risks, maybe more subtle ones, lurking
around, like, for instance, how AI models use
your data for training. Many models, sort of
by default, might use your conversations to train
future versions. Basically, the AI learns from
your input to get better. unless you specifically
go into settings and turn that off. Then you've
got third -party extensions, these little browser
add -ons that connect with AI. They can sometimes
harvest your data without you really knowing
exactly what they're taking. And finally, there's
this idea of supply chain attacks. AI tools often
rely on lots of other software libraries and
services. A vulnerability anywhere in that chain,
like one weak link, could potentially expose
your data that flows through it. So it sounds
like it's not just about how we choose to share
information, but we need to be fundamentally
aware of where our data is going and maybe who
else might be seeing it or using it. Beyond the
direct sharing risk, in your view, what's maybe
the most significant hidden risk that people
tend to overlook? That's a great question. I'd
argue it's that AI training aspect we just touched
on, the fact that models might be learning from
your private conversations without you actively
consenting or even realizing it's the default
setting. Many platforms just use your inputs
to improve themselves unless you opt out. That's
a huge, often unseen data exposure right there.
OK, this definitely raises a really important
question then. Given all these risks, how do
we fundamentally shift our approach? How do we
actually build safer, more robust AI work habits?
The source material gives us three sort of golden
rules. Yeah, and these rules are really about
changing your mindset. The first one is maybe
the most profound. Treat every single input as
if it's a potential public record. Before you
type anything into an AI, just pause for a second,
ask yourself, would I be okay with this information
appearing on the front page of a newspaper tomorrow?
If the answer's no, just don't enter it. It's
a really simple but incredibly powerful mental
check. That is a powerful filter. I have to admit,
I still wrestle with prompt drift myself sometimes.
You know, you get into a long chat and you start
getting maybe a little too comfortable, a little
too detailed. What's the next golden rule for
us? The second rule is all about practical vigilance.
Regularly audit your privacy settings, like really
regularly. AI platforms update all the time and
sometimes those updates quietly change default
settings, so. Make it a habit, maybe monthly.
Just pop into the settings sections, look for
things like data controls, privacy, security,
and your AI tools. Make sure everything still
aligns with how you want your data handled. Things
change fast, so your checks need to keep up.
And the third rule, it sounds like it shifts
the responsibility. It makes it broader than
just say, the IT department's job. It's everyone's
job now. That's exactly right. Digital hygiene,
especially with AI tools, is now just a core
part of modern work culture. It has to be. Whether
you're a leader, a creator, an employee, whatever
your role, actively protecting data, your own,
your teams, your customers. It's just an essential
skill now, non -negotiable. It's really everyone's
shared responsibility to keep that digital environment
secure. OK, so if we boil these rules down to
a core message, for our daily habits, what's
the essence we should carry forward? What's the
takeaway? I think the essence is really a three
-part commitment. First, think before you type
anything sensitive. Second, regularly check your
privacy settings. And third, always remember
that data security is a team effort. Those are
kind of the pillars for safe AI interaction,
personal responsibility plus collective awareness.
OK, so if sharing those live links directly from
ChadGPD or similar platforms carries these inherent
risks we've talked about. What's a safer, more
reliable workflow we should actually adopt? Especially
for sharing AI -generated ideas or information
with colleagues. Yeah, great question. The main
goal here is to completely break that direct
link back to the original AI chat. The new, safer
process is actually pretty simple though. Okay,
it does add a few extra minutes. First step,
obviously, generate your idea or content with
the AI. Then, this is crucial, copy that content
out of the AI and into a secure, controlled document.
Something like Google Docs, Notion, maybe a standard
Word doc. Then, and you must do this diligently,
rigorously, redact and remove all identifying
information. I mean, things like client names,
specific financial numbers, internal project
codes. Replace them all with generic placeholders
like client name or project X. Only after that
cleansing process do you share that secure document.
Never. ever the original AI link itself. And
hey, for stuff that's extremely sensitive, you
can even go the route of taking screenshots and
manually blacking out the critical bits. It's
definitely less convenient, sure, but it is undeniably
safer. It completely severs the tie. So the absolute
safest approach for sharing AI insights really
boils down to what? What's the core principle?
It's adding that layer of rigorous manual review
to the content first, and then sharing it only
through secure, controlled channels you trust,
never directly linking back to the raw AI conversation
itself. Copy, cleanse, share securely. OK. We've
talked about reacting to incidents after they
occur, about building better habits moving forward.
But proactive defense. Yeah. That feels like
the real key to long -term security, doesn't
it? What if we could integrate some kind of safety
co -pilot directly into our AI workflow, something
that reviews our inputs before we even hit send?
Okay, now this is where it gets really interesting
and honestly kind of mind -blowing. Our source
material shares this incredibly detailed prompt
template. And it's not just a simple command,
you know, it's basically a full -blown risk management
framework baked into a prompt. Think of it like
a rapid safety audit you run before you'd have
any sensitive interaction with the AI. The brain
behind this prompt. It's built on three really
solid pillars. First, the NIST AI RMF. That's
the U .S. National Institute of Standards and
Technology's AI Risk Management Framework. Basically,
a structured government process for governing,
mapping, measuring, and managing AI risks. The
prompt actually simulates this process. Second,
the OWASP LLM Top 10. That's the open worldwide
application security projects list of the biggest
security vulnerabilities for large language models.
common attacks like prompt injection or leaking
sensitive info, the prompt actively looks for
these. And third, GDPR principles. You know,
Europe's big data protection regulation, which
really emphasizes data minimization, basically
only collecting and keeping data that's absolutely
necessary. The prompt pushes you towards that,
recommending removing extra info. So by combining
these three things, the prompt doesn't just check
your text, it forces the AI to almost think like
a security expert, a data privacy lawyer, and
a risk manager all at once. Whoa. I mean, imagine
scaling that kind of proactive defense across
a whole organization. security right into the
workflow from the start. That's genuinely powerful
stuff for data governance. That sounds incredibly
sophisticated in its design. Let's just briefly
look in the prompt template structure. How does
it actually function? Yeah, the structure is
super clear, which is great. It starts with a
system instruction. This tells the AI what its
role is. Essentially, it becomes your personal
safety and privacy reviewer for this interaction.
Then you have the in PUSC section. This is where
you fill in the key details for context. Your
country, the purpose of the AI interaction, what
kinds of data are involved, like PII, financial
data, that sort of thing, how you plan to share
the output, and importantly, your acceptable
risk tolerance level. And then finally, you have
the task itself. This tells the AI to execute
five specific crucial steps based on your inputs.
And those five steps, that's where the real analysis,
the real safety check happens, right? Exactly.
Each step does a specific job. First is pre -check
map. This step scans your input and identifies
any sensitive bits, PII, secrets, anything that
might violate data minimization principles like
under GDPR, and it even flags risks from that
OWASP list like potential prompt injection vulnerabilities.
Second step is measure. Here it actually scores
each risk it found based on severity and likelihood.
It presents this in a little table super easy
to grasp like accidental PIR exposure, high risk
or prompt injection, medium risk. Third is manage.
This is the mitigation part. It gives you concrete
suggestions for redactions and rewrites. It might
replace names with placeholders like employee
name, rewrite sentences to avoid revealing confidential
figures, maybe reduce the granularity of data,
or add constraints like do not ask for home addresses.
Fourth is safety controls. Based on your context
and region, it adds five specific guardrails,
things like ensure no external links are executed
by the AI or limit data retention and logging
for this specific chat session. tailored advice.
And finally, step five is the final gate. This
is the bottom line. It gives you one of three
outputs, either a clean pumped output, safe version,
ready to use, or a concise checklist of things
you still need to do manually. Or if it decides
the risk is still too high, even after mitigations,
it just says do not use and briefly explains
why. OK, let's try to make this really tangible.
Could you walk us through how, say, an HR professional
might use this in their day to day work? Perfect
example. OK, imagine an HR or maybe a legal professional,
let's say, based in Vietnam, they need to use
an AI to help summarize an employee complaint
before briefing internal leadership. So their
inputs would be country, Vietnam, context, employee
complaint summary for leadership briefing, data
types, PII, sensitive allegations, sharing, internal
leadership only, risk tolerance, very low because
it's sensitive HR data. Okay, so they run the
prompt. The output would likely include a risk
table highlighting the high risk of PII exposure
and maybe potential legal implications if not
handled correctly. Then the manage step would
generate a carefully anonymized summary. It would
replace specific names, like maybe Ning Yen Ven
Eh, with a placeholder like complainant. Specific
dates or times might become vaguer, like early
August. And the accompanying checklist, the final
gate part, would probably include crucial points
like Share this summary only through encrypted
email channels, store the original complaint
document on a secure, access -controlled server,
and critically, ensure the AI chat history for
this specific session is disabled and then deleted
immediately after use. You see, it's not just
cleaning the text, it's building in these essential
process safeguards around the interaction. Wow.
So it really is like having a digital lawyer
and a security expert built into your process,
reviewing your AI interactions before any sensitive
data even leaves your control. Yeah. What would
you say is the ultimate most profound benefit
of using a prompt like this for our listeners?
The ultimate benefit is truly gaining an AI co
-pilot dedicated to proactive security. It embeds
a really sophisticated risk assessment right
into your daily workflow. It helps transform
AI from what could be a potential liability into
a securely managed powerful asset. That's the
core value, mid -roll sponsor read. So when we
step back and look at all this, what does it
really mean for us, this chat GPT incident? It
feels like it was a pivotal moment, maybe even
an expensive lesson for this digital age we're
in. An expensive lesson, maybe, but a truly valuable
one, I think. The big idea, the main takeaway
we want you to hold onto is this. AI is an unbelievably
powerful tool. It's a genuine amplifier of human
capability, no doubt about it. But with that
immense power comes an equally immense responsibility.
It really is on us, the users, to be vigilant,
to truly understand the dynamic digital landscape
we're operating in, and to take active conscious
ownership of our data. Yes, exactly. By proactively
cleaning up our digital footprints, like we discussed,
by adopting these much safer workflows, and by
consciously building a truly vigilant privacy
-first mindset. We really can harness AI's incredible
potential without constantly worrying about sacrificing
our privacy and security. It's all about intelligent
engagement, isn't it? Informed, responsible engagement.
Couldn't agree more. It really means embracing
the innovation that AI offers, wholeheartedly,
but doing it while maintaining unwavering conscious
control over our data. It's about finding that
balance. This deep dive has hopefully shed some
light on both the immediate actions you might
need to take and the kind of strategic mindset
required for a more secure AI future. Now as
we wrap up, we want to leave you with a thought
to maybe mull over. Yeah, what other hidden risks
might still be lurking just beneath the surface
as AI continues to evolve at this absolutely
breakneck pace? And maybe more importantly, think
about how you personally will integrate this
newfound vigilance, this awareness, into your
own daily digital interactions from now on. Definitely
something to consider. Food for thought indeed.
Thank you so much for diving deep with us today.
Until next time, stay curious and above all,
stay safe out there. OTR Music.