When a breach makes headlines, no one remembers which vendor was responsible—they remember the brand that trusted them. In today’s hyperconnected business ecosystem, privacy leaders recognize that third-party risk is no longer a niche compliance concern; it has become a board-level imperative.

Effective vendor privacy risk management has become central to every mature privacy program, ensuring accountability across all third-party relationships.

With AI, automation, and global data sharing driving innovation, organizations are increasingly relying on vendors for critical operations. But each partnership introduces new exposure, especially as vendors rely on their vendors. Managing this expanding web of risk is now a defining test of a mature privacy program.

Identify and assess vendor risks faster with TrustArc’s Data Mapping & Risk Manager. Automate discovery, visualize data flows, and prioritize high-risk vendors in one place.

The rise of privacy risk: When “your vendor’s fault” becomes your problem

Vendor reliance has expanded across various industries, from SaaS and cloud services to data analytics and AI-powered platforms. According to Security Scorecard’s Global Third Party Breach Report, 35% of breaches in 2024 were tied to third parties.

These incidents have shifted third-party risk management (TPRM) from a box-checking exercise to a strategic necessity. As privacy expectations and regulations evolve, organizations need vendor risk management for privacy programs that go beyond security questionnaires to include continuous oversight and automation.

Modern privacy laws make this explicit. Under GDPR, controllers must ensure processors provide sufficient guarantees for lawful processing, and they can be held jointly liable for vendor missteps. U.S. regulations, including the CCPA, as well as privacy laws in Colorado and Virginia, echo these requirements, mandating data processing agreements, oversight mechanisms, and transparency into vendor activities.

Put bluntly: regulators and customers don’t care whose fault it was. Whether a third-party vendor mishandles data or an AI system behaves unpredictably, the organization that collects the data bears responsibility.

Beyond fines: The real cost of third-party failure

Regulatory penalties are only part of the fallout. The MOVEit breach, which affected over 2,700 organizations worldwide, serves as a cautionary tale: even companies with compliant contracts in place were drawn into headlines, lawsuits, and breach notifications.

The ripple effects are brutal:

• Regulatory scrutiny intensifies with every incident, consuming resources and damaging relationships with data protection authorities.
• Reputational damage erodes customer trust faster than any fine can.
• Remediation costs, including forensics, credit monitoring, class-action lawsuits, and system overhauls, can persist for years after the incident.

And once you’re in regulators’ sights, as one former FTC employee explained, “they’re not keen to leave.” The takeaway: proactive vendor oversight isn’t just about avoiding penalties; it’s about staying off the front page.

See it in action: Use Data Mapping & Risk Manager to automatically surface and score third-party risks—so you can focus on prevention, not damage control.

Why third-party risk is now a privacy compliance issue

For years, vendor management was viewed as a function of IT or procurement. But the rise of AI, cross-border data transfers, and real-time personalization has turned it into a privacy compliance issue.

The convergence of privacy, security, and AI governance has created a new reality: vendor oversight can’t live in silos. Privacy leaders are consolidating procurement, legal, and IT functions into cohesive, risk-based frameworks that comprehensively manage third-party data exposure.

Building a unified approach to vendor risk assessment for privacy helps organizations identify high-risk vendors earlier and maintain compliance confidence as technologies evolve.

The C-suite and boards are paying attention, too. Vendor risk now sits alongside financial and cyber risk in enterprise risk management reports. Executives are asking not “if” privacy teams have vendor oversight, but “how mature and automated” that oversight really is.

The new frontier: AI, opacity, and “function creep”

AI has amplified vendor privacy risk in ways that defy traditional oversight. Vendors may use customer data for model training without consent, thereby undermining the GDPR’s purpose limitation principle and the CCPA’s data use restrictions. Others embed opaque models that make accountability nearly impossible.

“Function creep” has emerged as a growing privacy hazard, occurring when vendors expand their data use—say, from customer support to marketing or product training—without the organization’s awareness or approval.

As the EU AI Act and FTC’s “Operation AI Comply” expand regulatory scrutiny, privacy teams must evolve from checkbox compliance to continuous oversight. Annual questionnaires no longer cut it.

Bottom line: Privacy leaders must balance rigor with agility, building systems that move at business speed without compromising oversight.

Key risks in today’s third-party landscape

The modern third-party ecosystem is vast, fast-changing, and often invisible. The top risks include:

1. Hidden subprocessors: Fourth-party vendors often operate below the radar, increasing the chance of unmonitored data sharing.
2. Shadow AI: Employees or teams adopting unvetted AI tools can expose sensitive data outside governance controls.
3. Cross-border transfers: Vendors may dynamically shift processing locations, creating undisclosed international data flow risks.
4. Certification gaps: “AI-certified” vendors may rely on unverified or self-issued attestations—robots vouching for robots.
5. Contract complacency: Even airtight agreements fail without ongoing monitoring and audits.

Each of these risks underscores a central truth: vendor risk management is no longer a static checklist; it’s a living, breathing part of privacy compliance.

Automating vendor privacy monitoring for continuous compliance

As privacy programs scale, manual oversight becomes unsustainable. Adopting automated vendor privacy monitoring enables privacy teams to track data handling practices in real time, reduce administrative effort, and ensure audit readiness across all third-party relationships.

Accelerate your oversight: Automate continuous vendor monitoring and DPIAs with TrustArc’s Data Mapping & Risk Manager. Turn manual tracking into proactive compliance.

How to build a scalable, risk-based vendor assessment process

The most effective privacy programs treat vendor risk management as a lifecycle, not a milestone. A structured, repeatable process that spans planning, due diligence, tiering, and ongoing monitoring ensures consistency, accountability, and scalability. Modern vendor risk management software supports this lifecycle by centralizing assessments, automating due diligence, and standardizing reporting across departments.

1. Planning and strategy

Define your organization’s risk appetite and “no-go” thresholds before sourcing vendors. Align these with board expectations and regulatory frameworks. Identify categories such as SaaS, AI, cloud, and data processors, and establish tiering logic based on data sensitivity, business criticality, and AI involvement.

2. Sourcing and RFP

Require vendors to disclose their use of AI and subprocessors upfront. Screen out high-risk options that lack certifications, such as SOC 2 or ISO 27001. Engage Privacy and InfoSec jointly in the scoring process to align technical and legal evaluation.

3. Deeper due diligence

Move beyond yes/no questionnaires. Demand evidence of AI governance, training data limits, and red-teaming practices. Review data flow diagrams and cross-border transfers. Enforce audit rights, subprocessor approvals, and AI transparency clauses in contracts.

4. Risk tiering

Apply a consistent scoring model combining data sensitivity, access level, AI usage, and process criticality. Document why a vendor is high, medium, or low risk—this defensibility matters during audits.

5. Monitoring and change management

Implement continuous monitoring, not annual checkups. Trigger reviews when vendors add new features or pivot toward AI. Maintain a vendor change log and ensure contracts evolve as risks do.

6. Onboarding and offboarding

Grant least-privilege access and validate integrations before go-live. At offboarding, verify data return or certified deletion, including model retraining limits for AI vendors. Trust, but verify.

Comparing approaches: Manual, policy-driven, or automated

Organizations often evolve through three stages of vendor oversight: from manual tracking to policy-driven programs, and ultimately to automated platforms.

Automation doesn’t eliminate human judgment. It enables it. By centralizing data and workflows, privacy teams can evaluate vendor risk more efficiently, respond to changes dynamically, and maintain audit-ready documentation without manual effort.

Aligning Procurement, Legal, IT, and Privacy: Building the “guardians of the organization”

One of the most resonant insights from the TrustArc webinar came from Janalyn Schreiber, who described privacy and InfoSec as “the guardians of the organization.” Their mission: to protect innovation without slowing it down.

To achieve that balance:

• Create joint vendor review processes between Privacy, Legal, and InfoSec.
• Build shared dashboards that consolidate vendor risk insights across functions.
• Define clear swim lanes—who leads on contract review, technical evaluation, or regulatory mapping—to prevent bottlenecks.
• Train business teams to “ask the right questions” before adopting new tools.

This collaborative model ensures privacy leaders aren’t viewed as blockers but as strategic enablers who make responsible innovation possible.

How leading organizations use vendor risk management software to automate oversight

Forward-looking organizations are shifting from reactive to predictive oversight. According to the IAPP-EY Annual Privacy Governance Report, more than 60% of mature privacy programs now use automated systems to track vendor risk.

Today’s third-party risk automation tools help privacy leaders streamline workflows, maintain evidence, and proactively identify vendor risks before they escalate. TrustArc’s Data Mapping & Risk Manager, Assessment Manager, and PrivacyCentral tools exemplify this approach:

• Data Mapping & Risk Manager: Automates vendor discovery, dynamically scores jurisdictional and processing risks, and launches DPIAs or TIAs for high-risk vendors.
• Assessment Manager: Conducts scalable, automated assessments that tie directly to data flows and systems.
• PrivacyCentral: Benchmarks vendor activities against 130+ global laws and frameworks while automating compliance tracking.

Together, these solutions transform TPRM from a manual spreadsheet marathon into an intelligent, automated process that scales with the enterprise.

TrustArc’s AI-powered autofill can reduce manual effort by up to 80%, freeing privacy professionals to focus on strategy rather than tedious spreadsheet tasks.

From reactive to resilient: The future of vendor privacy risk management

The vendor landscape is evolving faster than regulation can keep pace. AI, decentralized architectures, and global data flows will continue to blur the boundaries of accountability.

But this is where privacy leaders thrive: at the intersection of innovation and integrity.

Organizations that embrace automated, risk-based vendor privacy management are doing more than complying; they’re building resilience. They’re turning oversight into opportunity and ensuring trust becomes a competitive advantage, not an afterthought.

Because in a world of infinite connections, your privacy program is only as strong as your weakest vendor. And with the right strategy, tools, and teamwork, that weakest link can become your strongest defense.

Ready to take vendor risk management from reactive to resilient?

Discover how TrustArc’s vendor privacy risk solutions, including Data Mapping & Risk Manager, Assessment Manager, and PrivacyCentral, serve as powerful third-party risk automation tools that streamline oversight, minimize regulatory exposure, and strengthen privacy compliance across your ecosystem.

Smarter Mapping. Stronger Risk Control.

Automatically discover, assess, and score vendor risks across your data ecosystem. Map data flows, streamline assessments, and launch DPIAs or TIAs in minutes—all from one intelligent platform.

Map risk with confidence

One Platform. Complete Compliance.

Unify your privacy operations with built-in intelligence. Benchmark activities against 130+ global laws, automate tracking, and manage compliance from a single command center.

Centralize your compliance

Privacy PowerUp #15

Tracking technologies are the silent sentinels of the internet, shaping the way digital advertising works and the privacy risks that come with it. For privacy, compliance, technology, and security professionals, understanding them isn’t just “nice to know.” It’s mission-critical.

From targeted ads to legal landmines, online tracking tools are everywhere—subtle, sneaky, and often shockingly sophisticated. Understanding them is the first step in avoiding regulatory risks and protecting consumer trust in an increasingly scrutinized digital landscape.

What is online tracking and why should you care?

Online tracking technology refers to various methods used to monitor, record, and analyze user behavior across websites, apps, and devices. These tools are foundational to the advertising technology ecosystem, better known as AdTech.

Think of online trackers as digital paparazzi: they’re always watching, noting what pages you visit, what products you check out, and even what device you’re using. Then, like a matchmaking algorithm for marketers, they deliver ads tailored to your behavior.

And this isn’t some fringe tech; this is the digital economy’s fuel.

How online trackers work: The tools in the toolkit

Online trackers come in many forms, each sneakier than the last:

• Cookies: The OG of trackers. These small text files live in your browser and remember your actions, from login info to shopping carts.
• Pixel tags: Invisible 1×1 images embedded in websites or emails that track user actions.
• Device IDs: Persistent identifiers that follow you across apps on mobile devices.
• Browser fingerprinting: This technique assembles a unique profile using your browser settings, fonts, plugins, and more.

Together, these trackers build a behavioral dossier that would make Sherlock Holmes blush.

They collect:

• Identifiers: Cookie IDs, user IDs, IP addresses.
• Device data: Operating system, browser type.
• Behavioral info: Pages visited, time spent, purchases made.
• Demographics and inferred interests: Even if you never offer them up.

This collected intel then feeds into audience segmentation, enabling hyper-targeted advertising campaigns that hit users with uncanny relevance.

AdTech: The industry powered by tracking

Tracking technologies are the lifeblood of modern AdTech. Without them, digital advertising would be like throwing darts in the dark.

Imagine shopping for a new pair of sneakers. Minutes later, ads for those very shoes (and their cousins) follow you across the web like an overly enthusiastic sales rep. That’s retargeting, a direct product of tracking.

AdTech companies use this data for:

• Behavioral targeting: Matching ads with likely interests.
• Performance measurement: Tracking clicks, conversions, and ROI.
• Cross-device tracking: Recognizing you as the same user on your phone, laptop, and smart TV.
• Real-time bidding (RTB): Where ad space is auctioned in milliseconds as pages load.

RTB works like a speed-dating event for ads. Your data is broadcast to an ad exchange the moment you land on a website. Bidders then offer top dollar for the chance to show you a personalized ad, all before you’ve even scrolled.

It’s quick, efficient, lucrative, and a ticking privacy time bomb.

Privacy concerns: Where the plot thickens

Tracking technologies may be an Adtech darling, but they’re a privacy professional’s worst nightmare. Here’s why:

1. Lack of consent

Most users don’t know they’re being tracked. Even when they do, privacy notices are often buried, vague, or intentionally confusing. As a result, consent is frequently uninformed, or worse, fabricated.

2. Data overload

The sheer amount of data collected (often sensitive and personally identifiable) is staggering. This includes geolocation, health inferences, political leanings, and even religious beliefs.

3. Opaque data flows

Many companies in the AdTech chain don’t know where the data goes or how it’s used after it’s shared. When personal data ping-pongs between dozens of vendors during RTB auctions, who’s accountable?

Regulatory minefields: The compliance tightrope

GDPR, CCPA, and beyond

These laws demand transparency, consent, and data minimization. They also pack a punch (just ask any company hit with multimillion-euro fines).

Key compliance must-haves:

• Valid consent before installing trackers.
• Clear privacy notices explaining who’s collecting what and why.
• Proper safeguards for data transfers (especially cross-border).

And don’t forget:

• The Schrems II ruling shattered the EU-U.S. Privacy Shield, exposing U.S.-bound tracker data to potential surveillance concerns.
• Several DPAs have ruled Google Analytics and similar trackers illegal under EU law due to cross-border transfer risks.

Privacy pros must now ask: “Is our tracking tech even legal in the countries where we operate?”

The hidden risks of tracking technologies

Let’s break it down like a late-night infomercial. Except what’s at stake isn’t your wallet, it’s your legal standing.

1. Data processing risks

• Security vulnerabilities: Collected data = breach potential.
• Loss of user trust: People don’t like being watched, especially in secret.
• Unclear data governance: Who owns it? Who protects it?

2. Litigation landmines

Old-school wiretap laws (like California’s CIPA) are being reborn to fight modern tracking. Plaintiffs argue that using tools like session replay software is akin to unauthorized surveillance.

Lawsuits are multiplying. Decisions are still pending. But the message is loud and clear: proceed with caution.

3. Cross-border data transfer risks

EU regulators have scrutinized trackers that transmit personal data to the U.S., citing national surveillance concerns. If the European Parliament can be found noncompliant, so can you.

Google Analytics, Meta Pixels, and similar tools are under fire. If your trackers cross international borders, buckle up.

4. Enforcement action

The U.S. Federal Trade Commission (FTC) and European DPAs aren’t just wagging fingers. They’re wielding hammers.

Recent FTC cases show:

• Selling location data without consent = fine.
• Misrepresenting health data use in ad targeting = fine.
• Failing to secure personal data = fine.

Spoiler: All of these are violations that tracking tech can trigger.

What businesses can do right now

Tracking may be a cornerstone of digital strategy, but that doesn’t mean it’s untouchable. Here’s how to walk the compliance walk:

Conduct a tracker audit

Inventory every tracking technology on your websites, apps, and third-party tools. Know what data is collected, where it goes, and who sees it.

Review consent mechanisms

Are you obtaining valid, verifiable consent? Are your cookie banners and privacy notices clear and honest?

Switch to privacy-by-design tools

Tools like contextual targeting and first-party data strategies offer alternatives to invasive trackers, without sacrificing performance.

Perform DPIAs

A Data Protection Impact Assessment (DPIA) helps you understand and mitigate the risks posed by trackers, especially in sensitive contexts or jurisdictions.

Train your teams

From marketing to IT, make sure everyone knows the rules of the (cookie) jar. Knowledge gaps are regulatory traps.

The future of tracking: Is there a path forward?

We’re at a crossroads.

One path leads to greater personalization, hyper-targeted campaigns, and rapid innovation. The other leads to regulatory smackdowns, class action lawsuits, and brand damage.

Can we have both?

The answer lies in accountability and transparency. Companies that embrace ethical data practices not just because they have to, but because it’s the right thing to do will win customer trust and regulatory goodwill.

Privacy is more than a compliance checkbox. It’s a business advantage.

Don’t be the last to wake up

If you think online tracking is just a marketing issue, think again. It’s a cross-functional challenge that touches every corner of the enterprise, from legal and compliance to security, data governance, and executive leadership.

Like the plot twist in a good spy thriller, the trackers are always one step ahead. But with the right tools, the right mindset, and a commitment to privacy, your organization doesn’t have to play catch-up.

Online tracking technology may be invisible. But its impact? Anything but.

Continue mastering the privacy essentials by reviewing all the resources in the Privacy PowerUp series.

Tracking Technologies in the Privacy Spotlight

View now

PowerUp Your Privacy

Watch all the videos in the Privacy PowerUp series – designed to help professionals master the privacy essentials.

Watch now

Read the next article in this series: #16 Data Inventory: Next-Level Classification for Privacy Professionals.

Read more from the Privacy PowerUp Series:

1. Getting Started in Privacy
2. Data Collection, Minimization, Retention, Deletion, and Necessity
3. Data Inventories, Mapping, and Records of Process
4. Understanding Data Subject Rights (Individual Rights) and Their Importance
5. The Foundation of Privacy Contracting
6. Choice and Consent: Key Strategies for Data Privacy
7. Managing the Complexities of International Data Transfers and Onward Transfers
8. Emerging Technologies in Privacy: AI and Machine Learning
9. Privacy Program Management: Buy-In, Governance, and Hierarchy
10. Managing Privacy Across the Organization
11. Assess the Risk Before it Hits
12. Contracts that Count: Mastering the 10 Most Negotiated Provisions in a Data Processing Agreement
13. Selling and Sharing Personal Information
14. Building a Privacy-Approved Vendor Management Program
15. Tracking Technologies: The Hidden Backbone of AdTech and the Looming Privacy Minefield
16. Data Inventory: Next-Level Classification for Privacy Professionals
17. Incident Incoming–Now What?

Privacy PowerUp #14

When it comes to privacy and compliance, your weakest link might be outside your organization. In an age of outsourcing, AI, and ever-evolving regulations, vendor management isn’t just a procurement function; it’s a privacy imperative. If you’ve ever worried about choosing the right processor, what goes in a contract, or how to stay ahead of regulators and reputational risks, this one’s for you.

Let’s demystify vendor management, build your confidence, and leave you with actionable steps to protect your business and your customers.

What is vendor management, really?

Vendor management is the lifecycle process of choosing, contracting, and overseeing third-party service providers (aka processors) who handle your data.

It’s the system behind selecting who to trust, setting the rules, and staying vigilant as that relationship evolves.

Think of it like assembling a pit crew in Formula 1. Each member plays a critical role, every second counts, and one wrong move can put your entire race at risk. Because when vendors touch your customer data, any mistake they make could become your PR nightmare.

Outsourcing may offer efficiency and scale, but it doesn’t outsource your accountability. The legal, ethical, and operational risks remain squarely your responsibility.

Controller vs. Processor: Who does what?

Understanding your role and theirs is foundational. In data protection terms:

• Controller = the organization that determines the “why” and “how” of data processing.
• Processor = the organization that processes data on behalf of the controller.

You might be both in different scenarios. For example, a SaaS company could be a controller when managing its employees’ payroll, and a processor when managing customer data in its platform.

But here’s the kicker: you can’t be both for the same processing activity. Each role comes with distinct responsibilities, so mapping out who does what helps you stay on the right side of the law.

Why vendor management matters now more than ever

From GDPR to CCPA to the emerging patchwork of global AI regulations, most modern privacy laws allow controllers to use processors, but with strings attached.

The most important? A Data Processing Agreement (DPA). This legally binding contract:

• Clarifies the scope and nature of the processing.
• Binds the processor to act only under your instructions.
• Details their obligations, your expectations, and how sub-processors are handled.

No DPA? No dice. That processor relationship is non-compliant by default.

Due diligence: Your pre-contract power move

Think of due diligence as your privacy polygraph. Before sharing a single byte of data, assess potential vendors like you’re hiring a bodyguard for your customers’ most sensitive secrets.

Here’s your checklist:

1. Expertise and capacity

Can they scale? Do they have the tech and people power to handle the job under pressure?

2. Jurisdiction

Domestic or foreign? Consider cross-border data transfer laws and whether their local government might access your data.

3. Reputation

What do privacy-minded peers say? Google reviews, industry forums, and watchdog reports are your best friends.

4. Data breach history

If it happened before, how did they respond? Have they fixed the root cause or just slapped on a Band-Aid?

5. Regulatory track record

Fined before? Under investigation now? Dig deep.

6. Employee turnover

High attrition can mean instability and heightened data risk.

7. Client satisfaction

Are current customers happy, or running for the exits?

8. Privacy maturity

Do they have a Data Protection Officer (DPO)? A documented privacy program?

AI: The wild card in modern vendor management

In the age of ChatGPT, predictive algorithms, and automated decision-making, AI is no longer optional. It’s operational.

If your vendors use AI, you need to know:

• Is your data used to train their AI model?
• Is their AI monitored for bias or unintended outcomes?
• Are humans reviewing key decisions, or is the process fully automated?
• Are they transparent about AI usage—to you and to the data subjects?

Why does this matter? Because AI use introduces new risks: discrimination, explainability issues, and regulatory scrutiny. If a vendor’s AI goes rogue, your brand takes the hit.

Are your AI vendors a help or a hazard? Take the AI Risk Assessment to determine your exposure.

Contracts: Cementing the relationship

Now that you’ve picked a privacy-savvy vendor, it’s time to get it in writing. The outsourcing agreement or DPA should cover:

• Purpose: What exactly is being processed, and why?
• Scope: Type of personal data and categories of data subjects.
• Instructions: Clear rules for what the vendor can and cannot do.
• Duration: How long they’re allowed to process the data.
• Obligations: Their duties for confidentiality, security, breach notification, and more.

And don’t forget clauses covering sub-processors, international data transfers, and audit rights. You’re not just covering your legal bases—you’re setting the tone for a trust-based relationship.

Remember Jurassic Park?

Just because you can outsource doesn’t mean you should do it without guardrails. The scientists didn’t stop to think whether they should resurrect dinosaurs, and chaos ensued.

The lesson? Complexity without control is a recipe for disaster.

Vendor management isn’t about saying “yes” or “no” to outsourcing. It’s about saying “yes, but…” and making sure the “but” includes binding contracts, strong oversight, and strategic thinking.

Monitor like a hawk: Ongoing oversight & auditing

This isn’t a set-it-and-forget-it deal. Data ecosystems evolve. So do threats. Even the best vendors can slip.

Here’s how to keep things tight:

• Questionnaires: Ask processors to attest to their ongoing compliance.
• Risk-based approach: High-risk vendors (those handling sensitive data or operating in high-threat regions) deserve closer scrutiny.
• Audit plans: Schedule audits based on the services they provide, data volume, and changes since the last assessment.
• Change detection: Always ask, “What’s changed since last year?” If their scope has shifted, your contract and oversight might need to shift too.
• Audit libraries: Create templates for different processor types to streamline future checks.

Spread the responsibility across teams—business units, procurement, and continuity planning. It’s a shared mission.

You can’t outsource accountability

This bears repeating: even if your processor fumbles the ball, you’re the one the ref (ahem, regulator) will penalize. As the controller, you are legally responsible for how vendors handle the data you provide.

That means staying vigilant from onboarding to offboarding. Data protection isn’t a department. It’s a discipline.

Privacy-first, risk-aware, future-ready

Vendor management is no longer a back-office checklist item. It’s a front-line defense for privacy professionals tasked with protecting consumers and corporate reputations.

By understanding roles, conducting robust due diligence, creating airtight contracts, and continually monitoring vendor activities, you not only comply with privacy regulations but also build trust, avoid risk, and future-proof your program.

Privacy isn’t a sprint. It’s an ecosystem. Vendor management is your blueprint to keeping it strong, smart, and secure.

Continue mastering the privacy essentials by reviewing all the resources in the Privacy PowerUp series.

Vendor Management Essentials

View now

PowerUp Your Privacy

Watch all the videos in the Privacy PowerUp series – designed to help professionals master the privacy essentials.

Watch now

Read the next article in this series: #15 Tracking Technologies: The Hidden Backbone of AdTech and the Looming Privacy Minefield.

Read more from the Privacy PowerUp Series:

1. Getting Started in Privacy
2. Data Collection, Minimization, Retention, Deletion, and Necessity
3. Data Inventories, Mapping, and Records of Process
4. Understanding Data Subject Rights (Individual Rights) and Their Importance
5. The Foundation of Privacy Contracting
6. Choice and Consent: Key Strategies for Data Privacy
7. Managing the Complexities of International Data Transfers and Onward Transfers
8. Emerging Technologies in Privacy: AI and Machine Learning
9. Privacy Program Management: Buy-In, Governance, and Hierarchy
10. Managing Privacy Across the Organization
11. Assess the Risk Before it Hits
12. Contracts that Count: Mastering the 10 Most Negotiated Provisions in a Data Processing Agreement
13. Selling and Sharing Personal Information
14. Building a Privacy-Approved Vendor Management Program
15. Tracking Technologies: The Hidden Backbone of AdTech and the Looming Privacy Minefield
16. Data Inventory: Next-Level Classification for Privacy Professionals
17. Incident Incoming–Now What?

Privacy PowerUp #13

Selling and sharing personal information impacts more than data management—it affects accountability, transparency, and even a brand’s trustworthiness.

This article explains how privacy teams can manage the legal and operational nuances of selling and sharing personal information. We’ll dive into regulatory assessments, data inventory must-haves, transparency and individual rights, and how to operationalize it all like a pro.

Selling and sharing: What’s the difference?

Depending on the laws, selling and sharing include the following:

• Selling includes transfer, disclosure, making available of personal information to a third party for “monetary or other valuable consideration”
• Sharing includes disclosing, making available, transferring of personal information to a third party for cross-context behavioral advertising, whether or not for monetary or other valuable consideration

Note that disclosing personal information to service providers for business purposes may not trigger additional requirements.

1. Legal and regulatory assessment: Know your regulatory obligations

One of the first steps should be assessing where you process personal information and, consequently, which laws apply to your organization.

California is the only state in the U.S. that explicitly covers the definitions of “selling” and “sharing”. States such as Colorado, Virginia, Utah, and Connecticut use explicit definitions of “selling”, but do not include “sharing” explicitly. While definitions and enforcement priorities vary, most of these laws outline consumer rights and business obligations tied to these concepts, especially in the context of digital advertising and third-party data transfers.

Outside of the U.S., laws like the GDPR implicitly include concepts of “selling” and “sharing.” Under the definition of processing of personal information, which includes collection, use, disclosure, or making available of personal information.

Understanding which laws apply to your organization is the foundation of any effective privacy program. If you’re looking to simplify that process, Nymity Research offers expert-curated insights, daily updates, and multi-jurisdictional comparisons, helping you identify your obligations faster and with greater confidence. That includes NymityAI, which can save you hours and has been built on the work of over 25 years by trusted privacy experts.

Regulatory applicability depends on multiple factors, depending on the regulations, geographical location, or data you are collecting, using, or disclosing. For example, in California, there is a revenue and volume threshold. The GDPR has an extraterritorial reach, so your company may fall under the scope of this regulation if it has no physical presence in the EU.

What else to consider in your assessment:

• Whether you collect sensitive personal information
• Engaging vendors and your vendor assessment practices
• Using personal information for cross-contextual advertising

Know your regulatory footprint

Multiple privacy regimes have a broad reach, and companies—including mid-sized businesses—need to know their obligations. If you operate in multiple jurisdictions, you will likely be covered by their privacy regulations. Understanding the concepts, such as “selling” and “sharing,” will be critical to designing scalable, compliant privacy operations.

If you’re collecting personal data, chances are you’re already in the game. The question is whether you’ve read the rulebook.

2. Data inventory: Build a map before you navigate

Data inventory is a critical element when thinking about data governance, data protection, and risk management.

You need to know:

• What categories of personal information do you collect, use, and disclose?
• Why do you process the data? What’s the purpose?
• Who do you share it with, and whether they’re service providers or third parties?
• Whether the data is sensitive and if these categories are necessary to achieve your goals?
• Do you use or disclose personal information in a way that would fall under categories of “selling”, “sharing”, or other applicable terms?

3. Transparency and individual rights.

Privacy experts recognize that transparency is not just about making the privacy notice public, but about ensuring that it is comprehensive, relevant, and understandable.

Most regulations require you to:

• Notify individuals at or before the point of data collection, use, and disclosure of personal information.
• Provide choice for the collection, use, or disclosure of personal information.
• Include the contact information for the organization.

Under the CCPA, among other requirements, companies need to provide:

• A clear, conspicuous Do Not Sell or Share My Personal Information opt-out link on your website.
• Categories of personal information sold or shared, and to whom.
• Information on the individual rights and how to exercise these rights.

Enforcement agencies have been increasingly focusing their attention on the notice and transparency requirements. It is very important to get this right and ensure that your data processing practices are clear and that you have appropriate measures in place.

Remember: The privacy notice is the frontline of your data trust strategy.

4. Operationalization and technical implementation: Turn policy into practice

So you’ve assessed your obligations and updated your notice—great. Now ensure that the mechanisms described in the privacy notice are fully implemented and that your systems support privacy requests.

Here’s how to make it real:

• Policies and procedures: Establish workflows for handling consumer rights requests; access, deletion, choice such as opt-out of sale/share.
• Technical implementation: Create opt-out tools that are easy to use and aligned with regulatory expectations. Avoid dark patterns.
• Minimization: Apply data minimization and ensure you do not collect personal information that is not necessary to achieve your goals. Always follow the regulations and best practices.
• Training: Ensure internal teams know how to process requests and handle data according to policy and the applicable laws.

Operational oversight:

• Monitor your systems for compliance drift.
• Audit vendors regularly.
• Update your internal documentation alongside public-facing policies.

A privacy program has many parts, some of which are visible, such as a privacy notice. But many others are unseen, such as staff training, internal policies and other documents, or ongoing monitoring. Always ensure that what you display publicly is matched by your practices behind the scenes.

Master the modern data exchange

Selling and sharing personal information touches everything from marketing and product design to customer service and executive decision-making. That’s why successful privacy programs aren’t reactive. They’re proactive, process-driven, and built on knowledge, communication, and control.

To thrive in today’s privacy-first landscape, you must:

• Know your legal obligations across every relevant jurisdiction.
• Inventory your data and understand how it flows.
• Communicate transparently with customers and regulators alike.
• Operationalize your opt-outs and rights mechanisms with precision.

Yes, the rules are evolving. But so are the tools, frameworks, and best practices to help you manage it. And when you get it right, you don’t just avoid fines—you earn customer trust, boost your brand, and position privacy as a competitive advantage.

Continue mastering the privacy essentials by reviewing all the resources in the Privacy PowerUp series.

Selling and Sharing: Privacy Rules You Can’t Ignore

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Read the next article in this series: #14 Building a Privacy Approved Vendor Management Program.

Read more from the Privacy PowerUp Series:

1. Getting Started in Privacy
2. Data Collection, Minimization, Retention, Deletion, and Necessity
3. Data Inventories, Mapping, and Records of Process
4. Understanding Data Subject Rights (Individual Rights) and Their Importance
5. The Foundation of Privacy Contracting
6. Choice and Consent: Key Strategies for Data Privacy
7. Managing the Complexities of International Data Transfers and Onward Transfers
8. Emerging Technologies in Privacy: AI and Machine Learning
9. Privacy Program Management: Buy-In, Governance, and Hierarchy
10. Managing Privacy Across the Organization
11. Assess the Risk Before it Hits
12. Contracts that Count: Mastering the 10 Most Negotiated Provisions in a Data Processing Agreement
13. Selling and Sharing Personal Information
14. Building a Privacy-Approved Vendor Management Program
15. Tracking Technologies: The Hidden Backbone of AdTech and the Looming Privacy Minefield
16. Data Inventory: Next-Level Classification for Privacy Professionals
17. Incident Incoming–Now What?

Smart cities and the privacy challenge

Imagine a city where traffic flows seamlessly, energy consumption is optimized, and public services respond intuitively to residents’ needs. This scene isn’t science fiction—it’s the promise of smart cities. By leveraging interconnected IoT devices, AI-driven analytics, and cloud computing, smart cities are revolutionizing urban life.

Across the globe, cities are embracing technology to enhance efficiency and improve residents’ lives. From Barcelona’s sensor-equipped streetlights that optimize energy use to Singapore’s real-time traffic monitoring system, which reduces congestion, smart cities are redefining urban living. While these innovations bring undeniable benefits, they also necessitate a careful approach to data privacy and security, ensuring that technological advancements do not compromise individual rights.

As former U.S. Supreme Court Justice Louis Brandeis once warned, “The greatest dangers to liberty lurk in the insidious encroachment by men of zeal, well-meaning but without understanding.” The same technologies that power smart cities also introduce serious privacy concerns, requiring a balance between innovation and ethical data governance. Mass data collection—ranging from facial recognition to behavioral analytics—creates an immense attack surface for cybercriminals while raising ethical questions about mass surveillance.

For privacy, cybersecurity, and compliance professionals, protecting personal data in smart cities is not just a technical necessity—it’s a regulatory, ethical, and business imperative. The challenge is clear: How do we enable innovation while ensuring privacy, security, and transparency?

The risks of personal data in smart cities: a security and compliance perspective

The digitization of urban infrastructure has enabled cities to function more efficiently, but this progress comes with significant risks. The vast amount of personal data collected through smart city technologies can lead to privacy vulnerabilities, cybersecurity threats, and regulatory challenges. Responsible data management is crucial to maintaining public trust and compliance with evolving laws. Below, we examine some key risks associated with personal data in smart cities.

1. Unprecedented data collection and processing

Smart cities thrive on data. Tons of data. From real-time traffic monitoring to biometric security, these systems collect personally identifiable information (PII), geolocation data, and behavioral patterns at an unprecedented scale.

Key risk: Even anonymized data can often be re-identified when combined with other datasets. According to the Future of Privacy Forum, the aggregation of data from various sources creates an increased risk of individual identification, even if personally identifiable details are initially stripped away.

2. Cybersecurity threats and attack vectors

The interconnected nature of smart city infrastructures makes them a prime target for cyber threats. Consider the following:

• IoT device vulnerabilities: Hackers can exploit unsecured smart meters, sensors, and traffic lights.
• Supply chain risks: A compromised vendor system can lead to city-wide breaches.
• AI-powered cyberattacks: Malicious actors leverage AI to bypass traditional security measures and manipulate data-driven decision-making.

Key risk: The International Association of Privacy Professionals (IAPP) report found that many smart cities lack standardized security controls, exposing critical systems to cyber threats.

3. Regulatory and compliance challenges

From GDPR to CCPA, privacy regulations are evolving—but how they apply to smart cities remains murky. Additionally, China’s Personal Information Protection Law (PIPL) introduces strict requirements on data transfers, posing compliance challenges for global smart city initiatives. Other sector-specific regulations, such as those governing health and financial data in smart city applications, further complicate compliance efforts. Navigating these frameworks requires careful coordination between legal, technical, and policy teams.

Challenges include:

• Cross-border data transfers: Cities using international cloud providers must navigate complex jurisdictional issues.
• Public-private partnerships: Many smart city projects involve private technology companies, raising concerns over data ownership and accountability.
• Auditability and transparency: Regulators require organizations to document how data is collected, processed, and stored, which is often challenging with fragmented city infrastructures.

Key risk: A World Economic Forum study found that only 25% of smart cities conduct privacy impact assessments (PIAs) before implementing new technology, exposing those not conducting PIAs to compliance failures.

4. Ethical and trust issues

Even if smart city initiatives are legally sound, they must also be ethically defensible. Consider:

• Facial recognition and AI bias: Automated systems can disproportionately impact marginalized communities.
• Mass surveillance concerns: Citizens may be unaware their data is being collected and analyzed.
• Trust erosion: Without transparency, public backlash can derail smart city projects before they launch.

Key risk: The Future of Privacy Forum warns that failure to address privacy concerns in smart cities could lead to public resistance, legal challenges, and potential regulatory crackdowns.

Smart cities must integrate privacy-by-design principles to avoid security risks, compliance failures, and public distrust.

Privacy concerns in smart cities don’t just live in policy papers—they show up in user sentiment. Learn what’s fueling the IoT trust gap and how smart city initiatives can meet rising consumer expectations with transparency, control, and ethical design.

The business and compliance implications of smart city data

As smart cities evolve, businesses and regulatory bodies must adapt to new data security challenges. From safeguarding personally identifiable information to ensuring compliance with global privacy regulations, the responsibility of protecting smart city data falls heavily on cybersecurity professionals and privacy leaders. Below, we explore the key considerations for these professionals and how they can mitigate risks in an increasingly connected urban landscape.

For cybersecurity professionals

• Data breach liabilities: With citizen data as a prime target, incident response plans must be airtight.
• Zero trust architectures: Role-based access control (RBAC) and least-privilege access models are critical to protecting sensitive data.
• Third-party risks: Vendors handling smart city data must undergo rigorous security assessments.

For privacy and compliance leaders

• Regulatory compliance: Mapping data flows across infrastructures ensures adherence to evolving legal requirements.
• Privacy Impact Assessments (PIAs): These are essential for identifying risks before rolling out new technology.
• Consent and transparency: Providing clear opt-in/opt-out mechanisms is key to maintaining public trust.

Organizations must integrate risk management, security frameworks, and privacy governance into smart city planning.

Business responsibilities: Who owns smart city data protection?

Ensuring privacy in smart cities is not the responsibility of a single entity—it requires a collaborative effort between public institutions, private sector leaders, and regulatory bodies. With vast amounts of data generated daily, cities must establish clearly defined roles and accountability measures to prevent misuse, enforce compliance, and uphold public trust. The following stakeholders play critical roles in managing smart city data protection.

Responsibilities include:

City governments and public entities: Enforcing privacy frameworks and ensuring transparency in data practices.

• Private sector and tech vendors: Embedding privacy-by-design principles in smart infrastructure.
• Third-party service providers: Securing APIs, cloud environments, and IoT ecosystems with robust access controls.
• Cybersecurity and privacy teams: Conducting continuous risk assessments and real-time monitoring.
• Regulatory bodies and compliance officers: Auditing data governance policies and imposing sanctions for violations.

Collaboration between municipalities, enterprises, and regulators is critical to creating a secure, privacy-centric smart city ecosystem.

The role of privacy management technology in smart cities

As smart cities become more data-driven, the need for advanced privacy management solutions has never been more urgent. Traditional security measures are no longer sufficient to safeguard the vast amounts of personal data collected. Privacy technology is crucial in mitigating risks, ensuring compliance, and fostering public trust.

Looking ahead, emerging technologies like privacy-enhancing technologies (PETs), blockchain for smart contracts, and AI governance frameworks will be essential for maintaining secure and ethical smart city operations. These tools help cities balance innovation with robust data protection practices.

Below, we explore key technologies that help address these challenges and enhance data protection in smart cities.

How privacy tech solves these challenges

1. Privacy automation and compliance tools

• AI-driven data discovery and classification ensures proper handling of PII.
• Automated data retention and deletion policies prevent unnecessary exposure.

2. Zero trust and cybersecurity solutions

• Multi-factor authentication (MFA) and end-to-end encryption safeguard smart city data.
• Network segmentation and continuous threat monitoring reduce attack vectors.

3. AI-powered anonymization and pseudonymization

• Differential privacy techniques enable data analytics without exposing individual identities.
• Privacy-preserving AI models mitigate bias in automated decision-making systems.

4. Consent and preference management platforms

• Blockchain-based consent tracking ensures auditability and compliance.
• Giving citizens direct control over their data fosters public trust.

5. Incident response and breach management

• AI-driven threat detection and automated response mechanisms reduce data breach risks.
• Forensic tools track and contain cyber incidents before they escalate.

Organizations can establish a proactive and resilient defense against emerging data risks by integrating privacy management technology into smart city infrastructures. This technology safeguards sensitive information, enables compliance with evolving regulations, and strengthens public confidence in digital urban ecosystems. As cities embrace innovation, a strong privacy framework will be the key to sustainable and ethical progress.

Leading the charge in smart city data protection

The rise of smart cities presents both opportunities and risks. Privacy and security leaders must proactively shape policies, deploy protective technologies, and champion ethical governance.

Organizations must adopt a forward-thinking approach to safeguarding personal information to ensure data protection in smart cities. A proactive approach begins with conducting PIAs before implementing new technologies, ensuring organizations identify and mitigate potential risks early. A robust security framework, including zero-trust security models and end-to-end encryption, is essential for preventing unauthorized access to sensitive data.

Additionally, leveraging automated privacy management and risk assessment tools enables organizations to monitor compliance and data protection efforts efficiently. Strong vendor due diligence is necessary to minimize third-party risks and ensure all external partners adhere to strict privacy and security standards.

Lastly, advocating for regulatory clarity and the ethical use of AI in smart city infrastructure will help shape policies that protect both organizations and the public.

How is your organization preparing for the privacy and security challenges of smart cities?

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