Privacy code scanning: How to sync privacy compliance with software development

By year-end 2024, Gartner predicts that 75% of the world's population will have its personal data covered under modern privacy regulations. Just since the beginning of 2023, three countries (Switzerland, South Korea, and Saudi Arabia) and five U.S. states have put new privacy regulations into effect, including the pivotal California Privacy Rights Act (CPRA). These regulations pose real concerns around data privacy and data security for businesses. 

Companies have poured considerable resources into their people, processes, and technology to keep up with these laws. Yet, many are still grappling with the fundamental issue of "where is our data?", resulting in steep fines from regulatory bodies like the US Federal Trade Commission (FTC) and EU regulators for improper data usage and sensitive data breaches.

Most privacy tools today focus on mapping data in storage, but they lack the data flow visibility necessary for even a base-level of privacy governance. These tools, often called data discovery tools, can only identify what data has been stored; they cannot determine how personal data has been collected, used, or shared. This full data lifecycle visibility is necessary to prevent a host of potential privacy violations. 

The predominant method for building full lifecycle data maps is currently through manual assessments. Privacy teams conduct these manual assessments by sending product and engineering teams questionnaires asking how the company’s websites, user-facing applications, and backend systems collect, use, share, and store personal data. In an attempt to speed up this slow process, privacy teams sometimes also interview product and engineering stakeholders. 

With or without data discovery tools, companies that process personal data still conduct manual assessments and still struggle to get full data visibility and prevent privacy violations. Manual assessments do not scale. They are slow and subjective, resulting in incomplete, inaccurate, and outdated information. 

To truly address these issues, we need a new approach that tackles the fundamental source of the problem: the code itself. 

The code is the primary source of privacy risk because it is where developers define the data collection, sharing, usage, and storage logic. By implementing privacy code scanning, companies can bridge the gap between privacy and engineering. This innovative solution provides complete visibility into the data lifecycle, including collection, flows, sharing, and storage. It also enables governance of data usage and allows for continuous privacy compliance within the software development lifecycle. 

For any company building software that processes personal data, privacy code scanning is the only solution available to proactively minimize privacy risks and sync privacy compliance with software development. 

In this guide to privacy code scanning, we will delve into:

• What privacy code scanning is
• Use cases for privacy code scanning
• How privacy code scanning differs from current approaches
• What impact privacy code scanning can have on your organization

Approaches to privacy

What is privacy code scanning?

Privacy code scanning solutions create full lifecycle data maps and implement programmatic privacy governance. The approach starts with the code. This is where business logic for data collection, storage, sharing, use, and processing is written by developers. 

Privacy code scanning solutions specifically scan the code written by a company’s engineering teams. For software-driven companies, their engineering teams’ code is what collects personal data and moves it in and out their websites, user-facing applications, and backend systems. 

By scanning the codebase, privacy code scanning solutions can automatically identify and classify all personal data by using a combination of algorithms and AI/machine-learning models. This is a much more efficient process than scanning data in storage because a company’s entire codebase lives in typically one, maybe two, source code management tools and only the code has to be scanned, not the enormous amount of data itself. 

In addition, privacy code scanning can automatically determine the context of personal data processing. Each instance of personal data processing can be linked to the exact code within an application, and engineers can quickly validate how the code is collecting, using, sharing, or storing personal data. When data processing violates privacy policies, issues are linked to the exact code causing the violation, and engineers can quickly resolve the issue.

Privacy code scanning solutions typically run scans by securely integrating with source code management tools that store a company’s entire codebase. This approach is similar to how many application security tools scan code to identify security vulnerabilities. 

After an initial scan is run to map all personal data flows and identify all live privacy issues, privacy code scans are triggered each time a change is made to the codebase. By continuously scanning for code changes, data maps, assessments, and reports can be updated automatically and privacy issues can be identified immediately. 

With this level of real-time visibility, privacy code scanning solutions can implement Privacy by Design workflows to automatically flag and even stop privacy violations before they occur. These workflows can be set up to monitor and enforce internal privacy policies and privacy regulations like GDPR, CCPA, CPRA, MHMDA, the FTC, and HIPAA. Because privacy code scanning can be integrated into standard software development and delivery processes, non-compliant code can be flagged and fixed before it goes live. 

Use cases for privacy code scanning

Digital tracking governance: Prevent non-compliant data sharing 

In the US, the largest privacy risk right now is non-compliant data sharing with marketing partners. Since 2023, the FTC has fined at least five companies for improperly sharing personal health data to marketing partners like Meta and Google. 

In February and March of 2024, enforcement launched for two groundbreaking data privacy regulations: the California Privacy Rights Act (CPRA) and Washington state’s My Health My Data Act (MHMDA). Both regulations put more onus on companies to collect, track and uphold consent before sharing user data. Meanwhile, the EU’s General Data Protection Regulation (GDPR) remains the strictest law governing personal data sharing, requiring opt-in consent before data is collected or shared. 

These new laws and increased enforcement require a new approach to stay compliant called digital tracking governance. Digital tracking governance is responsibly managing personal data shared with marketing partners by honoring user preferences. Privacy code scanning enables best-in-class digital tracking governance by: 

• Identifying all marketing partners: Build a live inventory of all 3rd parties receiving personal data via pixels, cookies, tag managers, and SDKs from your websites, apps, and backend integrations/APIs
• Tracking data flows: Gain full visibility by continuously monitoring how all data elements are collected and shared from your websites, apps, and internal systems 
• Ensuring consent compliance: Continuously audit websites and apps to ensure consent banners limit data sharing according to regulations and user preferences

Automate Record of Processing Activities for GDPR compliance

GDPR requires that all processors and controllers of personal data for people in the EU must regularly maintain a live Record of Processing Activities or RoPA. RoPAs require privacy teams to list each processing activity, identify what categories of data are being used, and describe the purpose of each activity. 

By leveraging its full lifecycle data maps, privacy code scanning can automate RoPA reporting to the point the engineers don’t need to do any questionnaires or interviews. Instead of waiting months to hear back from engineers, privacy teams can complete RoPAs in a matter of days.  

In addition, RoPAs can be automatically updated each time there’s a software update that changes data flows. Because RoPAs typically take several months to complete, they are usually only updated once a year. 

When over 42% of engineers release software at least once a month and over 69% release at least once every six months, most RoPAs are out-of-date before they’re even done. In addition, the RoPAs built from subjective questionnaires are likely to have missing or inaccurate information.

Privacy code scanning eliminates compliance risks from inaccurate RoPA reporting by automatically generating reports based on real-time data flows. 

Implement scalable Privacy by Design

Identify and resolve privacy risks without assessments 

In addition to compliance reporting like RoPAs, companies conduct internal manual assessments to identify potential privacy risks for new software products or features, changes to existing products or software infrastructure, or newly acquired businesses. 

Without privacy code scanning, privacy teams rely on manual privacy assessments to identify nearly all privacy risks, even for small website or application changes such as adding a marketing partner’s SDK to a mobile app. 

With privacy code scanning, privacy teams can automatically identify risks for small changes and reserve lengthy privacy assessments for larger, high-risk projects. Workflows can be set up in privacy code scanning solutions that automatically evaluate changes and identify risks according to regulations and internal privacy policies.  

For example, a workflow can be set up to automatically identify if a marketing partner’s SDK collects any personal data without the user’s consent. In this case, when the new SDK’s code is pushed live in the next app update, a scan could identify whether the SDK is collecting or sharing any personal data in violation of this policy workflow. All the necessary mobile SDK checks could be put into workflows that automatically identify risks without having to conduct a manual privacy assessment. 

Manual privacy assessments dramatically slow down privacy and engineering teams, and they should only be initiated for more complex situations such as building a new personal health app. Privacy code scanning can save an enormous amount of time from eliminating assessments for minor changes while enabling faster risk resolution. 

Prevent privacy risks before they go live

In addition to identifying live privacy risks, privacy code scanning can be used to prevent privacy risks in the dev process before they even go live. Similar to code scanning solutions for application security, privacy code scanning solutions can integrate with a company continuous integration / continuous delivery (CI/CD) pipeline tool to run a scan each time new code is submitted for review, before it is pushed live.

This way privacy and engineering teams can identify and resolve risks before they ever affect users’ data. Although privacy assessments are typically done when new products/features are being designed, privacy risks often still arise because software commonly changes and evolves during the development process. 

Instead of privacy teams only finding out about a software change after a privacy incident occurs, privacy code scanning can ensure non-compliant software updates don’t launch if they deviate from the latest privacy assessments or violate any privacy policies.

Furthermore, integrating privacy code scanning in the development process can even accelerate product launches. If the privacy team is informed of software changes affecting privacy after the design phase, this will typically trigger manual privacy assessments that may take weeks. Only once the assessment is complete will the product team be informed of changes they need to make, causing the product launch to be delayed even further.

With privacy code scanning, the privacy and product teams are both immediately alerted of privacy risks as the product is developed. This approach shifts privacy left in the process and enables developers to eliminate privacy risks before they cause further delays or issues.  

Privacy assessment automation: PIAs, DPIAs, etc.

When privacy assessments are needed for more complex, high-risk projects, privacy code scanning can automate the majority of assessments with more accurate information than a manual assessment. 

The most common privacy assessments are Data Protection Impact Assessment or DPIAs and Privacy Impact Assessments (PIAs), and the bulk of information they attempt to gather is related to data maps generated by privacy code scanning. 

Standard and custom reports can be built within privacy code scanning platforms to automatically pull in the required data map information such as what personal data is processed, how it is used, where it is sent to, etc. Such reporting can be combined with standard and custom questionnaires to fill in any remaining information. 

For companies operating in the EU, GDPR requires a DPIA for “high risk” projects involving personal data. GDPR provides guidelines for how to conduct a DPIA and for when a DPIA is needed. It is typically up to the company’s Data Protection Officer or DPO to determine exactly how and when DPIAs are conducted. Regulators typically only review DPIAs if a company is being investigated for a GDPR violation.   

The other most common privacy assessment is a PIA. PIAs are similar to DPIAs except they are conducted when DPIAs are not required by GDPR, most often in the US where GDPR does not typically apply. PIAs are less standardized than DPIAs, but they are used for similar high-risk projects and collect similar information such as how personal data is used and shared. 

Privacy code scanning platforms can be set up to automate DPIAs and PIAs that are custom to the needs of each company and even project. Typically the most important and time-intensive information to gather lies in the code that generates privacy code scanning data maps. That is why privacy code scanning is best positioned to enable faster and more accurate privacy assessments. 

Automate privacy reports for app store approval

Apple and Google both require app owners to submit privacy reports for apps to be published in their respective app stores: the App Store and Google Play. The reports for both app stores require information that privacy code scanning gathers automatically: what personal data is collected, who it is shared with, and for what purpose. 

Apple requires privacy manifest reports each time a new app or app update is submitted to the App Store for approval and requires app owners to maintain accurate Privacy Nutrition Labels. Privacy manifests are designed for Apple to determine privacy compliance when approving an app for the App Store while Privacy Nutrition Labels are designed to transparently communicate the app’s data privacy practices to users.  

The Google Play Store requires app owners to complete their data safety form that is similar to Apple’s Privacy Nutrition Labels; the form is used to populate the data safety section that tells users how personal data is processed for each app in the Google Play Store.  

To accurately complete these reports for each app, developers have to manually review their app’s code or documentation or wait for third parties to complete questionnaires explaining how they process personal data. Utilizing privacy code scanning, these reports can be generated automatically so that they simply need to be double-checked, saving an enormous amount of time while providing more accurate, up-to-date information.  

Block sensitive data sharing with AI applications

With AI application development and adoption at an all-time high, AI governance couldn’t be more important to privacy teams. AI applications are built with and fine-tuned with data that may include sensitive personal data. Users also input data into certain AI applications that may need to be filtered out for privacy or other reasons. 

For data that engineering teams send to internal or external AI applications, privacy code scanning can ensure that no sensitive personal data is shared. Policy workflows can be set up to restrict select or all personal data elements from being sent to applications flagged as AI. 

Govern data shared across borders

In today’s ever evolving global privacy landscape, many countries now have laws that restrict cross-border transfers of personal data. Most notably, the EU’s GDPR and China’s Personal Information Protection Law (PIPL) restrict what data can be sent where and under what circumstances. 

Privacy code scanning can prevent non-compliant data sharing across borders both internally and externally. Third parties and internal destinations can be categorized by location, and policy workflows can be set up to limit what personal data is sent where. 

Assess and mitigate privacy risk for mergers & acquisitions 

When acquiring or merging with another company, privacy code scanning can quickly assess their privacy risk profile and identify how to address compliance issues. Different companies have different privacy policies and practices, and typically the company with higher privacy standards has to spend months assessing the other company’s risk by reviewing documentation, conducting interviews, and/or waiting on teams to complete questionnaires.

Privacy code scanning can eliminate the vast majority of those manual assessment activities. By scanning the new company’s entire codebase, a full inventory of personal data elements and potential privacy risks can be generated without any manual effort. Privacy code scanning enables a more comprehensive and accurate assessment to be completed in days that would normally take months. 

After an acquisition is completed, it can also take months if not years for the new company to adopt the acquiring company’s privacy standards. Privacy code scanning can rapidly accelerate this integration process.

The acquiring company’s privacy standards can be easily converted into privacy code scanning checks that identify exactly what code is violating which policy. Instead of new products and features getting dramatically delayed for not meeting the privacy standards, the automated checks can be built into the software development process, enabling developers to build with privacy in mind. As code moves to the code review stage, privacy checks can alert developers how to address deviations from the privacy standards. 

Generate transparent privacy reporting for software vendors to expedite vendor assessments

B2B sales can take a long time, especially when enterprise companies evaluate a new software vendor. B2B enterprise software cycles typically take 6-12 months and drain a lot of resources from the buyer and vendor in the process. 

A privacy review of a vendor is one of many things that can slow down a deal along with reviewing security, technical feasibility, ethical practices, etc. What if the vendor could provide the buyer with an unbiased, objective report that enables the buyer to skip the privacy review altogether? This could save both sides many hours from reviewing privacy practices and completing and evaluating RFP questionnaires. 

Privacy code scanning solutions can automatically create such a report for software vendors. This way software vendors could come to each deal with a standard report that may preemptively answer all of the buyer’s privacy questions.

For example, these reports could show data maps with all personal data the vendor’s product collects, uses, stores, and shares. Depending on the buyer’s policies, the report could be tailored to include additional automated checks for each privacy regulation and standard required by the buyer. To enable quick validation, each finding in the report could be linked to each instance in the codebase where the data processing originates.  

How does privacy code scanning compare to current approaches?

Data discovery tools 

Data discovery tools help companies build an inventory of all data they have in storage; this includes personal data and any other data relevant to the business. 

Although these solutions are effective at building data inventories of what data is stored, they offer no coverage for how data is collected, used, or shared. This is because the logic for how data is generated and moved lives in the code of a website, app, or backend system. 

Data discovery tools inventory data by scanning structured and unstructured data across data stores and select third party applications. Data discovery tools can scan column names and the actual data, using ML/AI techniques to discover and classify data.

Data discovery can feel like playing whack-a-mole, where you are always reacting to personal data popping up in data stores with no control over the source of the problem. 

Once the lengthy 6-12 month data discovery process is done, privacy teams still struggle to identify which teams use this data and still lack the data flows needed to accurately create RoPAs, conduct PIAs, and find privacy issues. 

Doing data discovery alone can create a false sense of maturity in privacy programs because you know the data you have in data stores. But in reality, you don’t understand how your data is being used, you don’t know how it is being shared, and you don’t know how it is being collected. These gaps lead to privacy issues such as: 

• Excessive data collection 
• Sensitive data sharing 
• Misuse of personal data 
• Non-compliant data processing activities 

Manual assessments

After companies build a complete inventory of all data in storage, they still have to ask several teams how the data is collected, used, and shared. Visibility into the full data lifecycle is needed to complete RoPAs, DPIAs, PIAs, etc. and ensure compliance. 

To get this visibility, most privacy teams send questionnaires and interview requests to teams that may know how personal data is being processed including, product management, engineering, data analytics, and marketing. This even includes privacy teams who already completed a 6-12 month implementation of a data discovery tool because data discovery tools can only identify what data is stored, not how it is used or shared. 

If the privacy team asks the engineering team what personal data their websites and applications process, they would attempt to manually do what privacy code scanning does automatically, review their code. 

Before doing that, engineering leads would struggle to find all the engineers with the knowledge of how their software processes personal data. Because some engineers have left the company and the engineering leads likely don’t know or don’t have the time to find the right owner for every part of the code for every application, a handful of engineers without full context or privacy expertise will attempt to answer the privacy team’s questionnaires for all applications. 

After first waiting weeks or even months to look at the questionnaires because they’re busy meeting engineering sprint deadlines, each engineer will need to spend hours asking other engineers, reviewing documentation, and reviewing the code itself to complete questionnaires for each application. 

Even for the engineers, the code is the best place to find the answers to the privacy questionnaires. The issue is it’s impossible for any one person to manually review a company’s entire codebase. 

On top of that, the codebase is constantly changing as many engineering organizations now ship software updates at least once a week. 

For companies that try to employ a Privacy by Design approach, they may do privacy reviews for new product changes at the design stage. While this is possible for top-down planned features, many features are built bottoms-up after the design stage. Even if design reviews are conducted for all new changes, development can still deviate from the original design, causing privacy gaps and issues to emerge. 

The bottom line is that manual assessments do not scale and yield imprecise, out-of-date outputs. As a result, manual assessments open up companies to many unknown privacy risks while dramatically slowing down engineering and privacy teams. 

Key advantages of privacy code scanning

• Enables full data lifecycle visibility for software-driven companies: For companies building software that processes personal data, privacy teams can autogenerate data maps showing how all personal data elements are collected, used, shared, and stored.
• Leverages AI/ML models that enable unparalleled accuracy: Static code analysis is supplemented with AI models that continue to increase data mapping accuracy and enable generative outputs like processing activity descriptions.
• Continuous and real-time governance: Proactively detects privacy risks based out-of-the-box and custom privacy policy workflows and prevents risks by running privacy checks in the development process
• Creates efficiencies for privacy and engineering teams: Automated data mapping and risk detection eliminates the vast majority of manual privacy assessment activities that typically require engineers to complete questionnaires and interviews, lasting months if not all year
• Preserves data security: No personal data is ever scanned or accessed; only code is scanned. Customer code is never stored or shared and is never used to train AI models.
• Rapid time to value: Get full visibility and governance in a matter of weeks. Privacy code scanning typically requires just one integration with a company’s source code management tool. The integration can be completed in a few weeks. Data mapping and risk identification can be completed in just a few days. 

Key capabilities of privacy code scanning solutions

Data visibility 

• Inventory of all personal data collected, stored, or shared 
• Sensitive data tags for CPRA, GDPR, MHMDA, etc. 
• Inventory of all data destinations: third parties and internal systems receiving personal data via pixels, cookies, tag managers, SDKs, customer data platforms (CDPs), APIs, etc. 
• Data flows showing every third party and internal data destination for each data element
• Autogenerated descriptions of all processing activities

Privacy governance

• Risk discovery: Out-of-the-box and custom workflows to generate alerts for potential violations to internal policies and regulations like CCPA, CPRA, GDPR, MHMDA, and HIPAA
  • Stop non-compliant data sharing with marketing partners
  • Block sensitive data sharing with AI applications
  • Govern data transferred across borders internally and externally
  • Continuously scan websites and apps to audit that consent is collected and acted on appropriately
• Risk prevention: Workflows to block code with privacy risks during the dev release cycle
• Assessment automation: Pre-filled, self-updating RoPAs, DPIAs, PIAs, etc. 

Developer enablement

• Privacy risk alerts embedded in dev tools 
• Root cause identification: Flag exact code causing risk
• Automated dev tickets for quick risk resolution 

Impact driven by privacy code scanning

• Provides accurate picture of privacy risks: It’s impossible to prevent unknown risks. Make critical business decisions based on a comprehensive understanding of all potential live risks within websites and applications and future risks with code in the development process  
• Reduces risk at scale: Convert privacy policies into automated workflows that identify and block risks at scale as your tech stack grows and evolves
• Enables rather than slows down product teams: Provide automated privacy guidance and risk alerts as developers code instead of delaying product launches for assessments that require engineers to stop coding and fill out questionnaires 
• Breaks down communication gap between privacy and engineering teams: Translate privacy policies into privacy checks that identify exactly what code is violating which privacy policy  
• Eliminates manual processes and saves time for privacy and engineering teams: Fully automate data mapping in days instead of waiting months to complete data discovery and questionnaires, eliminate unnecessary assessments for minor changes, and automate the majority of DPIAs and PIA with information synced from data maps
• Allows for more focus on risk mitigation: Instead of spending the majority of resources on data gathering, address risks head on before a major breach or violation occurs

Key takeaways

• Privacy regulation and enforcement is increasing rapidly, particularly in the US: Since 2023, the FTC has fined at least five companies for improperly sharing personal health data to marketing partners like Meta and Google. As of March 2024, US companies must be compliant with the California Privacy Rights Act and Washington state’s My Health My Data Act. Nearly every US state without a privacy law in effect is currently in the process of implementing one.   
• Current approaches to data privacy are inadequate: Data discovery tools can only determine what data is being stored, and they still require manual assessments to determine how data is collected, used, and shared. Manual assessments do not scale and yield imprecise, out-of-date results.
• For companies building software that processes personal data, most privacy risks start in their codebase. The code determines what data is collected and how it flows in and out a company’s websites, user-facing applications, and backend systems.‍
• Privacy code scanning enables complete and continuous data visibility and privacy governance by scanning the code that runs a company’s websites, user-facing applications, and backend systems to monitor how personal data is collected, used, shared, and stored.

Learn more: Privacy code scanning whitepaper

Read our whitepaper on privacy code scanning to learn more about this new approach. Download now.

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Frequently asked questions

What is privacy code scanning?

Privacy code scanning enables full data lifecycle visibility and continuous privacy governance by scanning the code that runs a company’s websites, user-facing applications, and backend systems to monitor how personal data is collected, used, shared, and stored.

How is privacy code scanning different from data discovery tools?

Data discovery tools scan data stores to build a comprehensive inventory of all data in storage, not just personal data. Data discovery tools can only determine what personal data is stored; they lack coverage for how personal data is collected, used, or shared. 

Privacy code scanning solutions scan code, not data. By scanning the code that controls the creation and movement of personal data, privacy code scanning solutions can build full lifecycle data maps of how personal data is collected, used, shared, and stored. Privacy code scanning also enables continuous privacy governance by automatically identifying privacy risks as the codebase is updated. 

How is privacy code scanning different application security tools that scan code?

They scan code for different purposes and identify different risks. Application security scan code to identify security vulnerabilities such as unauthorized access to systems, cyberattacks, API token leaks, and outdated software packages. 

Privacy code scanning solutions build full lifecycle data maps of how personal data is collected, used, shared, and stored and identify risks for violating internal privacy policies and regulations such as GDPR, CPRA, HIPAA, etc. Privacy code scanning solutions also automate privacy assessments and flag code in the development process with potential privacy risks. 

What types of companies benefit most from privacy code scanning?

Any company building software that processes personal data can benefit from privacy code scanning. That software could be the code that runs their websites, user-facing applications, and/or backend systems. Typically, companies with over 200 software engineers need privacy code scanning to scale their privacy governance program. Privacy code scanning has successfully reduced privacy risk for companies across industries including, ecommerce, finance, healthcare, gaming, software, telecommunications, transportation, insurance, ad tech, and data intelligence.  

What code do privacy code scanning solutions scan? 

Privacy code scanning solutions can scan any code written by a company’s engineering team. This code can include the code that runs a company’s websites, user-facing applications, and backend systems. 

How do privacy code scanning solutions access code?

Privacy code scanning solutions typically need just one integration for implementation, the customer’s source code management tool. Source code management tools contain all the code written by your engineering team and have a wide range of capabilities including deploying software updates via a CI/CD pipeline. Only read-only access to source code management tools is needed, meaning nothing in the source code management tool can be changed, including the code. No customer code should ever be stored or shared by privacy code scanning solutions.   

Can privacy code scanning help my organization maintain compliance with GDPR?

Privacy code scanning solutions are designed to support several aspects of GDPR compliance including, data mapping, Records of Processing Activity (RoPA) automation, Data Protection Impact Assessment (DPIA) automation, and GDPR privacy risk prevention. Privacy code scanning prevents risks related to personal data collection, usage, 3rd party sharing, and storage as well as consent compliance auditing. 

Can privacy code scanning help my organization maintain compliance with CPRA?

Privacy code scanning solutions are designed to support several aspects of CPRA compliance including, data mapping, prevent non-compliant data sharing, and auditing consent compliance (i.e., “do not sell or share”).

How do privacy code scanning solutions communicate privacy risks to privacy and engineering teams? 

Privacy code scanning solutions communicate risks in their own platform and whichever other tools privacy and engineering teams use including privacy management (e.g., OneTrust), Slack, Teams, ticketing systems (e.g., Jira), dev tools (e.g., GitHub), etc. 

How can privacy code scanning build trust with stakeholders?

Privacy code scanning builds trust and collaboration across teams including privacy, product, engineering, etc. by translating privacy policies into automated workflows that identify what code is violating which policy. Linking data maps and risks to code enables immediate validation and resolution from engineering teams. Additionally, risks are communicated seamlessly in the tools and language that each team uses.

How can privacy code scanning build trust with customers?

Privacy code scanning builds customer trust by ensuring a company’s privacy promises to customers are followed through on. Privacy teams are given the visibility and governance to monitor and prevent violations to the privacy policies communicated to customers. 

Can privacy code scanning replace my current privacy management tool?

Privacy code scanning solutions are designed to supplement, not replace privacy management tools like OneTrust. Data maps and risks can be seamlessly synced to privacy management tools to increase their efficiency and effectiveness. 

Can privacy code scanning govern data used in AI applications? 

For data that engineering teams send to internal or external AI applications, privacy code scanning can ensure that no sensitive personal data is shared. Policy workflows can be set up to restrict select or all personal data elements from being sent to applications flagged as AI. 

Can privacy code scanning scan 3rd party applications to monitor personal data flows?

Privacy code scanning solutions cannot scan 3rd party applications like Salesforce or Workday unless one-off integrations are built for each 3rd party tool. Privado has built integrations with the most prevalent tag managers and customer data platforms to prevent non-compliant data from being shared to marketing partners from those tools.