Leading Privado.ai as Founder and CTO, our mission is to embed privacy into the DNA of software engineering. Overseeing the development of Privado, our open-source tool, we enable engineers to manage sensitive data with unmatched precision. This endeavor reflects our core value of making privacy a fundamental aspect of technology.

Prashant Mahajan

Privacy teams should be worried. This is exactly how the same mistake happens twice.

Crunchyroll already paid $16M over privacy allegations tied to user viewing data.

Now it is being sued again. Different vendor, same underlying problem: a company allegedly let third-party tooling access what people were watching, tied to who they were.

That is not a policy failure first.
It is a systems failure.

From the outside, this looks like “data sharing.”
Inside the product, it usually looks routine.

An SDK gets added for engagement.
Events start flowing. User identifiers get attached.
Viewing activity becomes a usable signal for someone outside your system.

That is how legal risk gets created quietly.

This is the part many privacy programs still miss:
- The contract can look clean
- The policy can sound right
- The consent language can be reviewed

But if the system is still sending user-level viewing events to a third party, none of that matters much.

Legal teams need to ask harder questions:
1. What events are actually leaving the app?
2. What identifiers go with them?
3. Can a vendor tie content consumption back to a person?
4. Who verified the live data flow after the tool was implemented?

This is why privacy work cannot stop at notices, reviews, and vendor paperwork.

Policies describe intent.
Systems reveal reality.

If your privacy team cannot see what product telemetry is doing in production, you are relying on assumptions, not controls.

How many companies that settled privacy claims have actually fixed the underlying data flow?

#Privacy #DataPrivacy #Compliance #Legal #Law

Privacy Engineering Infrastructure Leader | Founder & CTO, Privado.ai | Built $100M+ Scale Systems | Defining AI-Driven Privacy Automation

This week California regulators fined a company most people wouldn’t expect in a privacy case.

A high school sports ticketing platform.

Students used it to buy tickets for football games, prom, and school events.

But behind the scenes, the platform was running tracking technologies for targeted advertising.

And here was the catch.

You couldn’t view your ticket without clicking “agree” to tracking.

No real opt-out or alternative path.

So attending a school event effectively meant agreeing to ad-tech tracking.

Regulators fined the company $1.1M and forced changes to its practices.

What stands out to me is the architecture behind this.

A ticketing system quietly became part of an advertising infrastructure.

The core product and the tracking stack were intertwined.

This happens more often than teams realize.

A simple product integrates analytics, marketing tools, or ad-tech.

Over time those integrations start shaping how the product works.

And suddenly core functionality depends on tracking.

That’s where privacy usually breaks... in the system design.

If someone can’t use your product without being tracked, it’s worth asking:
Was tracking ever meant to be part of the product experience?

#Privacy #Engineering #DataPrivacy #CCPA #Legal

Over the last few months, I kept walking out of customer conversations with the same feeling in my head.

Privacy teams are carrying way too much.

Not because they are not good at what they do. Not because they are not working hard. Actually the opposite. The people I speak with are thoughtful, sharp, and deeply serious about the work. But the day to day of privacy work still feels heavier than it should.

One customer told us their setup was still very manual and dependent on annual refreshes.

Another said something that really stayed with me: 
“I don’t need a person to fill out a spreadsheet anymore.”

Another put it even more plainly:
“We need the result. We need the answer.”

And another said what I think a lot of teams feel, even if they do not always say it out loud: “We don’t know what we don’t know.”

That is the part I kept coming back to.

None of these people were asking for something flashy. Nobody was asking for another dashboard, another workflow, or another pile of features to learn.

They were asking for something much simpler.

Clarity.

A better way to understand what is happening across their digital environment. What changed. What matters. What needs attention. What they can trust.

That is why we announced Wren today.

Wren did not begin as a vague idea about AI. It came from a much more practical place. We kept hearing the same pain, from different companies, in different words, and it became hard to ignore.

Privacy teams are working in environments that keep moving. Websites change. Apps change. Cookies change. Configurations drift. New tools show up. Old assumptions stop being true. But too much of the work still depends on manual effort, fragmented visibility, and tools that make you dig for the answer instead of helping you get there.

So even when teams have scans, inventories, workflows, and documentation, the real questions are still there:
- What changed?
- Why was this flagged?
- Is this actually a risk?
- Can I trust this result?
- What do I take to legal?
- What do I do next?

That gap is exactly what led us to build Wren.

We want it to take on the investigative work privacy teams should not have to do manually anymore. 

To spot when something new needs review. 
To help trigger the right assessment. 
To pull in the right docs, scans, contracts, and context. 
To help generate the assessment with evidence, not guesswork. 
And to help teams track the risks that actually need action.

For us, this announcement is not about launching something new just because we can. It is about taking a problem we kept hearing again and again and deciding to build for it properly.

We want privacy teams to investigate faster. 
We want them to get to the “so what” without all the chasing. 
We want them to feel more clear, more confident, and a little less buried in manual work.

A part of why we are building Wren is very simple: 
We want privacy pros to smile more

Check the announcement link in the comment.

When the Meta AI glasses lawsuit came out, I did not just see a product story.
I saw a privacy operations story.

Because this is what keeps happening. Legal teams document one version of data use. The system quietly evolves into another.

A feature gets shipped. A vendor gets added. A review workflow appears.

Data starts moving in ways no one properly mapped. And by the time anyone notices, the documentation is already out of date.

That is why manual privacy processes are struggling in 2026.

They depend on people explaining what they think the system does. But privacy risk does not live in what people think.

It lives in what the system is actually doing. That is the gap.

Spreadsheets, policy reviews, screenshots, and questionnaires still have a role.  

But they are not enough for environments where products change constantly and AI introduces new data flows faster than governance teams can keep up.

Automation matters because it gives privacy teams something manual processes cannot.

Actual visibility. Not more opinions or better wording.

Evidence.

For legal and compliance teams, that changes the conversation from:
what was approved
what was documented
what teams said

to:
what is running
where data is going
which vendors are involved
what changed since the last review

That is a much stronger foundation for privacy accountability.

In 2026, the problem is not that teams do not have privacy processes. The problem is that most of those processes still cannot keep up with the systems they are supposed to govern.

How are you handling that gap today between documented privacy controls and actual system behavior?

#privacy #data #AI #automation

40 million Americans are asking AI about their health.

Most assume HIPAA follows that conversation.

It doesn’t.

When a hospital uses an AI tool, there’s a Business Associate Agreement. That creates shared legal responsibility. Access is scoped. Logs exist. Controls are negotiated. OCR can enforce.

When a consumer opens ChatGPT Health or Claude and uploads lab results, that structure disappears.

There is no covered entity. No BAA. No HIPAA enforcement mechanism.

The protection comes from product design and terms of service. Not healthcare regulation.

From a systems perspective, this is a completely different environment.

Enterprise deployments sit inside defined trust boundaries. Identity controls. Access segmentation. Audit logs. Contractual limits.

Consumer tools are multi-tenant platforms. Even with encryption and isolation, governance is standardized. Not negotiated.

Legal assumption. Health data equals HIPAA.

System reality. Regulation attaches to relationships, not data types.

That gap is where risk lives.

If you lead privacy, consider this:
- Are employees pasting patient data into consumer AI tools?
- Do your policies clearly separate enterprise AI from personal experimentation?
- Can you technically detect where health data is flowing?

If regulators asked, could you evidence the difference?

None of this makes consumer AI unsafe.

But it does mean the regulatory posture is different. And most organisations are not modelling that difference clearly.

AI in healthcare now operates in two silos.

If your governance framework doesn’t reflect that, you’re relying on assumption.

Can you actually prove which environment your data is in?

#dataprivacy #HIPAA #law #AI

GDPR fines have now crossed €5.6B in publicly known enforcement.

Most people will read that as a regulator story. I think it is really an evidence story.

The hard part is no longer writing the policy. The hard part is proving what actually happened inside the system.

That is where most privacy programs break.

On paper, the company has a notice, a retention policy, a RoPA, DPAs, and internal controls.

In production, the reality is usually different.

Data fields get reused. Events change names. New SDKs get pushed live.
Vendors receive data nobody reviewed.

AI features get added on top of existing systems without anyone fully mapping the downstream flow.

Legal teams are still expected to stand behind disclosures and controls.

But those claims are often based on interviews, screenshots, and documentation created at a point in time.

Systems do not work that way. They change every week. Sometimes every day.

So when enforcement happens, the real question is not, “Do you have a policy?”

It is, “Can you show evidence of what your systems actually did with personal data?”

That changes a lot.

Documentation shows intent... system evidence shows reality... privacy reviews without engineering visibility create blind spots and accountability without technical proof is mostly trust

This is the gap legal and privacy teams need to pay attention to.

The risk is not just non-compliance. The risk is defending a position that was true when it was written, but false in the live system.

Privacy programs need to move closer to engineering reality.

Not because lawyers need to become engineers. Because legal accountability now depends on technical evidence.

How is your team validating what production systems are actually doing with personal data today, beyond documentation?

#privacy #GDPR

I’ve built systems at scale. That’s exactly why I don’t believe manual data mapping works.

I was talking recently with a data governance leader at a large media publisher.

Their process still depended on manual updates, annual check-ins with system owners, and lineage they didn’t really trust.

Honestly, that’s not surprising.

The problem is bigger than process.

Most privacy teams are still being asked to understand data flow through surveys, spreadsheets, and interviews.

But production systems do not wait for annual refreshes.

Tags change. Vendors change. Pipelines change. New use cases appear. Data starts moving in ways nobody captures fast enough.

So the map gets updated.

The system has already moved on.

That’s the gap privacy teams are dealing with now.

Not a policy gap. A visibility gap.

My view is simple:

Policies describe intent. Systems reveal reality.

If your data map depends on manual refreshes, it’s probably already outdated.

How are teams closing that gap today?

#privacy #legal #law #data

Former Google Head of Sales & LinkedIn Managing Director | Creator of The Top 5% Method® | Helping High-Performing Professionals Build Influence and Design Elite Careers

Most careers are built by default.

The top 5% achievers design them deliberately.

I spent more than two decades inside the growth engines of the technology industry, including 10 years at Google and leadership roles at LinkedIn where I scaled billion-dollar portfolios across sales, marketing, product, and go-to-market strategy.

During that time, one pattern became impossible to ignore.

The professionals who rise fastest are not simply the most capable.

They understand how visibility, positioning, and influence operate inside complex organizations.

That insight became the foundation for The Top 5% Method®.

A strategic framework that helps high-performing professionals move from:

Execution → Strategic influence
Competence → Authority
Career ladder → Career architecture

Since 2015, I have coached 15,000+ professionals globally, including large leadership cohorts at Google, helping ambitious leaders redesign their trajectories and accelerate their impact.

Today I am the Founder and Chief Revenue Officer of Driving Impact, where I advise high performers and emerging leaders on how to:

• design intentional career trajectories
• become visible to decision-makers
• navigate power structures inside organizations
• build authority beyond their job title
• develop diversified income and portfolio careers

My clients include professionals from organizations such as:

Google
Accenture
Mastercard
Deloitte
LinkedIn
Delta
Barclays
and high-growth startups.

I also host Driving Impact: The Top 5% Method® Podcast, ranked in Apple’s Top 200 Business Podcasts, where I explore how top performers build influence, authority, and long-term impact.

If you are ready to stop simply climbing the ladder — and start designing your career deliberately:

Explore The Top 5% Method®

https://www.top5percentmethod.com
hello@drivingimpact.io

Katheline Jean-Pierre MSc

AI isn’t complicated. It’s just explained terribly.

If you're building with AI, here are 12 core concepts you need to know:

(From Luís Rodrigues, give him a follow)

1/ LLM (Large Language Model)

Massive neural networks trained on billions of words to predict and generate human-like text.
 
Used in: ChatGPT, Claude, Gemini, GitHub Copilot

2/ Tokens

AI reads in chunks, not characters. "Unbelievable" might be 3 tokens. Every token costs compute and money.
 
Used in: Model planning, API pricing, context management

3/ Prompt Engineering

How you ask matters as much as what you ask. I've gotten completely different outputs from the same model just by restructuring one sentence.
 
Used in: Content creation, business automation, image generation

4/ Transformers

The architecture powering modern AI. Self-attention lets the model weigh which words matter most in a sentence: "bank" means something different near "river" vs "money."
 
Used in: All modern LLMs, Google Search

5/ AI Agents

Models that plan, take actions, and loop back on results without a human clicking next at every step.
 
Used in: Workflow automation, research, multi-step tasks

6/ Context Window

Think of it as working memory. The bigger the window, the more text the model can hold and reason over at once. GPT-4 started at 8K tokens. Some models now handle 1M+.

7/ Multimodality

One model handling text, images, audio, and video. You can describe a photo, transcribe speech, and generate a report in a single pipeline.
 
Used in: Visual search, video analysis, accessibility tools

8/ RAG (Retrieval Augmented Generation)

Instead of relying only on training data, RAG fetches relevant info before answering. Helps a lot with accuracy and keeping things current.
 
Used in: Enterprise chatbots, customer support, documentation tools

9/ AI Alignment

Teaching models to behave in ways that are actually safe and useful. This is arguably the hardest problem in the field.

10/ Embeddings

Text turned into numbers that capture meaning. "Dog" and "puppy" end up closer together than "dog" and "spreadsheet."
 
Used in: Search engines, recommendation systems, document comparison

11/ Fine-tuning

Training a general model on specific data so it gets good at one thing. A base model knows everything broadly. A fine-tuned one knows your domain.
 
Used in: Medical diagnosis, custom chatbots, industry tools

 
12/ Hallucination

AI doesn't know what it doesn't know. It fills gaps with plausible-sounding, sometimes completely wrong answers. This is why human review still matters.
 
Impact: Trust, accuracy, business decisions

--

♻️ Repost to help your network understand AI.
➕ Follow me Katheline Jean-Pierre MSc for practical Business insights

12 Most Know GenAI terms from Luis Rodrigues

I built an app today.

Zero lines of code. Zero developers. Zero excuses.

because my students are busy. They're execs, high-performers, people in the middle of career pivots... and they want the Top 5% Method® Career Accelerator in their pocket, on the go... or wherever life takes them.

So I built it.
Live. By myself. In one afternoon.

With Replit, I went from "I wish I had an app" to "I have an app" 
and the whole thing felt almost unfairly easy.

This is what the new era of building looks like. You don't even need to wait anymore. You just need to start.


With replit, see how beautiful life is.
 🤍
C'est beau la vie. 

try it for yourself: https://lnkd.in/gymvyNT9

♻️ Repost if you’re ready to ship v1 today... not someday.

#Replitpartner #app #AI #replit

I Help Tech 👩🏾‍💻 Workers Become more secure in their Career, Pivot & Grow their Brand Authority | Fortune 100 Revenue Leader | Former Google, LinkedIn & Deloitte |

Emma Watson just said what every ambitious woman needs to hear right now.

"I'm WILLING to move forward even when I feel alone. 
I'm WILLING to go to bed each night at peace with myself. 
I'm WILLING to be my biggest, bestest, most powerful self."

Notice:
she didn't say "I will when I'm ready."
Or "I will when they notice me..."
Or "I will once I have permission...."

Just willing... and that's the key...

This International Women's Month, your willingness is your superpower.

Your readiness to move forward... 
even scared, 
even alone, 
even without permission... 
that's what separates you from everyone still waiting for the "right time."

What are you willing to do today that you've been putting off?


♻️ repost to inspire another woman today. 
📣 Join the next masterclass to elevate your voice today: https://luma.com/xyx53ude

They don’t need your perfection.
They just need your honesty.


The insight you think is “basic”
is someone else’s breakthrough.

The lesson you learned the hard way
is someone else’s shortcut.


When you carry a gift... when you carry God’s light. When your experience could help someone move forward, it's your duty to share it.

Even if it feels ordinary to you...
It might be life-changing for THEM.

Share your gift.



🔁 If this resonates, repost it to inspire someone else.
📣  Follow me, Katheline Jean-Pierre MSc, for practical ways to build brand authority in your career and on LinkedIn.

Fortune 100 Revenue Leader | Former Google, LinkedIn & Deloitte | I Help Women in Tech 👩🏾‍💻 Become more secure in their career & pivot |

A black woman in a pink dress presenting to a crowd

You’ve been told to wait your turn.
To stay humble...
Keep your head down. 

What nobody tells you:
These myths were never made to help you rise ❌

They were made to keep you safe.
But not successful.

In this carousel, I’m breaking down 7 outdated career myths women are still told, and what you actually need to believe instead.

It’s time to rewrite the rules.
Unlearn the noise.
And build a career your way.


Myth #1: “If I work hard, I’ll be noticed.”

Myth #2: “I need to be 100% qualified 
before applying.”

Myth #3: “Saying no makes me difficult.”

Myth #4: “Career breaks are a setback.”

Myth #5: “Good girls get promoted.”

Myth #6: “I shouldn’t talk about my accomplishments.”

Myth #7: “I have to choose between ambition and family.”



Swipe through the carousel to go deeper →

* There is no time like now to get the authority and recognition you deserve. You won't regret it. 

________
👉🏽 Learn how to build brand authority inside and outside or your company: https://luma.com/xyx53ude
👉🏽 Join the career acceleration waitlist: https://lnkd.in/gSjNSxT5
_______


♻️ Repost to help others elevate their brand.

CAREER MYTHS YOU NEED TO UNLEARN NOW

Your AI can process data.
But it can not build trust.


And in an AI-first world, trust becomes the real advantage… Your x-factor.

Because trust is not a vibe. It’s heartfelt consistent behavior over a long time period. 


Here are 20 ways to build trust at work and in your relationships, organized into 5 frameworks you can actually remember:

1) The TRUST Stack (the order matters)
Make outcomes repeatable
Close loops fast
Prove you understand
Create emotional safety
Tell the whole story

2) The 4 C’s of Credibility
6. Show your work
7. Be predictably solid
8. Make it about their win
9. Say the hard thing early

3) The Integrity Triangle
10. Match words & actions
11. Reveal incentives (“here’s what I’m optimizing for”)
12. No secret standards
13. Own your part

4) The Repair Protocol
14. Name the impact
15. Take clean responsibility
16. Offer a fix + deadline
17. Rebuild proof over time

5) The Trust Flywheel
18. Start with clarity (roles, decisions, timelines, expectations)
19. Ask for “friction feedback”
20. Call out trust wins publicly

If you want to stand out while everyone else is automating… become  the person people can trust under pressure AND in multiple contexts! 

Now... Which language of trust do you practice right now: reliability, candor, or repair?

♻️ Repost to remind everyone that trust still wins.
🚀 Drop your story in the comments.

an infographic that says "20 ways to build trust" 

She was brilliant but invisible.

Smart. Experienced.... The real deal. 
But, she was still the best kept secret in her field.

I recently spoke with a consultant specializing in AI governance for financial services firms. 

She wasn’t attracting decision-makers.
No CEOs, no CFOs, no Chief AI officers...
No steady inbound leads.
No clear authority positioning.

The harsh reality… is that LinkedIn has evolved.
Posting is not going to be enough.
Polished content without positioning does not convert.

She didn’t need more content.
She needed a clear Brand Authority strategy.

We’re now refining her positioning, and building sales, marketing, and content systems that turns visibility into executive-level conversations. 

AND LEADS!

If you’re a consultant or expert who wants to:
• Attract decision-makers
• Build inbound leads
• Become the go-to in your niche

It’s time your LinkedIn matched your expertise.

I’m hosting a Brand Authority Masterclass where I’ll break down what’s actually working right now.  


👉🏽 Sign-up now to the Masterclass at this link: https://luma.com/xyx53ude
👉🏽 Join the waitlist: https://lnkd.in/gSjNSxT5
♻️ Repost to help others accelerate their career with a strong personal brand.

 Bon Samedi.

a woman sitting in a chair giving a conference with a microphone at a conference. she wears a beige vest and a green dress. her name is katheline jean-pierre a brand authority career coach

The light you're hiding could change someone's trajectory. 
Yes, really. 
Even if you don't believe it. 

When you carry a gift,
when your story holds light,
and your path could help others find their own.

You have a responsibility.

Distill what you’ve learned.
And share it.

Even if it feels ordinary to you...
it could be life-changing for someone else.
Share your gift.
Share your voice.
Someone out there needs it.

Try it this week. 
Share one story and tell me what happens.

🔁 If this resonates, repost it to inspire someone else.
📣 Follow me, Katheline, to build your brand authority in your career.

taplio

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Prashant Mahajan

Katheline Jean-Pierre MSc

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taplio

The Battle

Prashant Mahajan

Katheline Jean-Pierre MSc

Followers

Posts

Engagements

Likes

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