What are privacy-enhancing technologies (PETs)?

Privacy-enhancing technologies are techniques, masking, tokenisation, pseudonymization, encryption, and more, that protect personal data while it's collected, processed, or shared. Under the DPDP Act, PETs turn data minimisation from a policy line into something a system enforces.

What are the 4 types of privacy?

The four commonly cited types are bodily, behavioural, communication, and data privacy. The DPDP Act, and Privy, focus on data privacy: how personal data is collected, processed, shared, and protected across its lifecycle.

What is an example of a privacy-enhancing feature?

Tokenising a customer's mobile number before it reaches a third-party notification vendor is a PET in action. The vendor gets what it needs to send an SMS, nothing more, and the original number never leaves your system.

What are the 7 types of privacy?

Researchers extend the four core types into seven: person, behavioural, communication, data and image, thoughts and feelings, location and space, and association. Data privacy is the one every DPO and CISO answers to under the DPDP Act, and where Privy's PETs operate.

Privacy-Enhancing Technologies (PETs) for AI-Ready Data Governance

Privy determines and recommends the right privacy-enhancing technology – masking, tokenisation, pseudonymization, anonymisation or encryption – for every processing activity, protecting personal data without slowing applications, analytics or AI.

Privacy-Enhancing Technologies Illustration

The Challenge

The Protection Gap: Where Data Privacy Loses Precision

The challenge isn’t simply protecting personal data. It is applying the right protection every time data is stored, shared, analysed or used.

When One Privacy Control Is Applied Everywhere

Customer service, analytics, legacy systems and AI workloads use personal data differently. Applying the same control everywhere can leave data exposed or make it unusable.

When Protection Stops at Storage

Personal data is continuously accessed, transformed and shared across applications, APIs, cloud platforms, third parties and AI systems. Encryption at rest alone cannot protect it throughout these interactions.

When Privacy Slows the Business

Every privacy-enhancing technology introduces trade-offs across latency, infrastructure complexity, reversibility and data utility. The wrong technique can affect application performance, analytics and AI adoption.

Privacy-Enhancing Technologies

Privacy-Enhancing Technologies: The Right Protection for Every Data Use

Apply context-aware privacy controls across data at rest, in motion, and in use – while preserving performance, analytical utility, and business agility.

Context-Aware PET Selection

Recommend privacy protection based on how personal data is actually being processed.

Analyse data sensitivity, processing purpose, user access, downstream flows, and regulatory obligations.

Identify the appropriate category of protection for every processing activity.

Recommend the specific technique that best balances privacy, functionality, and performance.

Protect Data at Rest

Secure stored personal data with controls aligned to its sensitivity and intended use.

Encrypt sensitive identifiers such as mobile numbers, Aadhaar, and PAN.

Use format-preserving encryption where legacy applications require existing data formats.

Apply tokenisation, static masking, and access governance to minimise unnecessary exposure.

Secure Data in Motion

Protect personal data as it moves across applications, APIs, cloud platforms, third parties, and AI ecosystems.

Tokenise identifiers shared across systems while preserving referential integrity.

Dynamically mask or selectively disclose only the information required for processing.

Enforce policy-driven sharing based on purpose, consent, lineage, and recipient context.

Preserve Privacy in Use

Protect personal data during analytics, AI inference, model training, customer servicing, and operational processing.

Use pseudonymised, anonymised, or synthetic datasets for analytics and AI workloads.

Apply controlled decryption or ephemeral processing only for authorised computations.

Minimise exposure without compromising computational and analytical utility.

Utility-Preserving Privacy Controls

Choose protection that works with the application, not against it.

Balance privacy with latency, reversibility, infrastructure requirements, and business utility.

Preserve data formats and relationships where operational processes depend on them.

Prevent privacy controls from slowing applications, analytics, or enterprise AI adoption.

Connecting Privacy Protection Across
Every Data Workflow

Privy brings data intelligence, processing context, privacy policies, and PET enforcement together to protect personal data throughout its lifecycle.

Continuously discover and classify personal and sensitive data across enterprise systems.

Connect data with its purpose, users, applications, computations, obligations, and downstream flows.

Recommend the right privacy-enhancing technology for every workload based on its processing context and privacy objective.

Enforce and monitor privacy controls consistently across databases, APIs, applications, third parties, analytics, and AI systems.

Meet the AI Compliance Copilot: The Intelligence Behind Every Privacy Decision

Privy’s AI Compliance Copilot uses an AI-driven Context Graph to understand what personal data is, how it is used, where it flows, and which obligations apply. It recommends and helps enforce the right privacy-enhancing technology for every processing activity.

Understand data sensitivity and regulatory context.

Map data flows across applications, vendors, APIs and AI systems.

Detect risks such as re-identification and excessive data exposure.

Recommend PETs that preserve privacy, performance and data utility.

Trusted by India’s Leaders in Privacy and Compliance

Enterprises across BFSI, fintech, and digital commerce trust Privy to keep their data, consent, and compliance under control.

Still have a question?

Privacy enhancing technologies, or PETs, are techniques that protect personal data while it is stored, processed, analysed or shared. They include encryption, tokenisation, masking, pseudonymization, anonymisation and synthetic data.

Privy supports context-aware controls such as encryption, format-preserving encryption, tokenisation, static and dynamic masking, pseudonymization, anonymisation, synthetic data, selective disclosure and controlled decryption.

Privy evaluates the sensitivity of the data, processing purpose, user access, downstream flows, computational requirements and applicable obligations. It then recommends the privacy-enhancing technology best suited to the workload.

For data at rest, PETs can encrypt, tokenise or mask stored identifiers. For data in motion, they can minimise what is shared across APIs, applications and third parties. For data in use, PETs protect information during analytics, AI inference, model training and operational processing.

PETs allow organisations to use data for AI and analytics while reducing unnecessary exposure. Techniques such as pseudonymization, anonymisation and synthetic data can preserve patterns and analytical utility without directly revealing individual identities.

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Privacy-Enhancing Technologies (PETs) for AI-Ready Data Governance

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