Unlocking Data and
Powering Insights with
Privacy Enhancing Solutions

Privacy by Design

Combining the power of Privacy enhancing technologies (PETs) with innovative privacy engineering practices,
we ensure that privacy is fundamental in every product and service we offer.
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We uphold the highest standards of data protection with our privacy by design philosophy,
powered by state-of-the-art privacy enhancing technologies and robust privacy engineering practices.

Unified Privacy Risk  Assessment

Privacy risk scoring:
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Privacy risk scoring is a method that quantifies the level of risk associated with a data flow or data assets. It involves assigning numerical or qualitative scores based on regulations or frameworks that govern data protection, such as GDPR in the EU, DPDP in India, CPRA in the US, or PDPL in Saudi Arabia. These scores help visualize the degree of privacy threats and provide mitigation recommendations in accordance with applicable privacy frameworks & regulations.
Privacy Attack Simulation:
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Privacy attack simulations, such as singling out attack, linkage attack, outlier attack, and inference attack, are methodologies used to evaluate the vulnerabilities and risks associated with in data assets / flows. These attacks aim to exploit weaknesses in data protection measures to uncover sensitive information that can compromise an individual's privacy.

Privacy Threat  Modeling

‍Privacy threat modelling is a structured approach used to identify, assess, and mitigate potential privacy risks and threats to an organization's systems, processes, or applications. It is a proactive method for understanding and addressing privacy concerns early in the development or implementation of technology or business processes. Privacy threat modelling helps organizations make informed decisions about how to protect individuals' personal information and comply with data protection regulations.

DPIA

‍A Data Protection Impact Assessment (DPIA) is a systematic process used to identify, evaluate and document the potential impact of processing personal data on the privacy and data protection rights of individuals. DPIAs are required under the Multiple global regulations for certain types of processing activities that are likely to result in high risks to individuals' data protection rights. The main purpose of a DPIA is to identify and mitigate these risks. It typically includes an analysis of the necessity and proportionality of the data processing, as well as the implementation of technical safeguards to ensure compliance with data protection regulations.

Privacy Preservation  & anonymization

Privacy preservation and anonymization are techniques and principles used to safeguard the privacy of individuals' personal information when handling, sharing, or analysing data.

Synthetic Data

Synthetic data refers to the process of generating artificial datasets that mimic the characteristics and statistical properties of real-world data without containing any actual, sensitive, or personally identifiable information. Synthetic data is designed to be a privacy-preserving alternative to real data, allowing organizations to perform various tasks, such as research, testing, or analysis, without exposing individuals' private information.

Cryptographic data  collaboration

Cryptographic Pseudonymization and encrypted data collaboration are critical strategies for safeguarding sensitive information in data processing and sharing scenarios. It involves replacing or encrypting personally identifiable data with unique identifiers, protecting individuals' privacy while retaining data utility.

AI & LLM  Governance

With Rise of New GenAI and LLMs there is a new threat emerging of revealing sensitive or private data to these models. Using Tools like PrivaGPT users can carefully redesign prompts that prevent such revelations. Thus helping organizations to reap the benefits of AI and create a responsible, privacy-conscious AI solutions that respect individuals' privacy rights, comply with regulations.

Privacy Preserved  Insights Sharing

Privacy-preserved insights sharing is a crucial practice for organizations, especially those with international operations or handling cross-border data. It ensures that sensitive information remains protected, aligning with global data protection and cross-border regulations. Failure to comply with these regulations can result in severe legal and financial penalties. Therefore, organizations must adopt a comprehensive approach to preserve privacy while harnessing the valuable insights data can provide.

Responsible AI

Enterprise’s Privacy Journey with NIST

NIST, the National Institute of Standards and Technology, offers a comprehensive guideline for organizations to effectively manage and prioritize privacy risks. This structured approach aligns privacy practices with business objectives while upholding individuals' privacy rights. The NIST Privacy Framework encompasses key privacy principles, practices, and a robust risk management process, enabling organizations to build and maintain strong privacy programs. Embrace the NIST Privacy Framework to enhance transparency, accountability, and trust in your data privacy practices.

Data Collection & DPIA + Privacy Threat Modeling

  • Requirements: Safe, Fair & Privacy Aware
  • Technologies: Privacy Threat Modeling, Bias Assessment, Augmented DPIA, Mitigatory Recommendation & Regulatory requirements
  • PrivaSapien’s PERAI Products: Privacy X-ray (PTM), Prescriptron (AugmentedDPIA)

Data Preparation & Feature Engineering + PETs

  • Requirements: Safe, Fair & Privacy Aware
  • Technologies: Privacy Enhancing Technologies
  • PrivaSapien’s PERAI Products: EventHorizon (Statistical Anonymization), DataTwin (Synthetic Data), DifferentialInsight (Differential Privacy),CryptoSphere (Cryptographic Pseudonymization)

Model Training + PPML

  • Requirements: Privacy Preserving, Accountable, Transparent & Valid
  • Technologies: Privacy Enhancing Technologies, DPIA, testing
  • PrivaSapien’s PERAI Products: Prescriptron (Augmented DPIA), EventHorizon (Statistical Anonymization), DataTwin (Synthetic Data), DifferentialInsight(Differential Privacy), CryptoSphere (Cryptographic Pseudonymization)

Model Deployment + Model Security

  • Requirements: Secure, Transparent
  • Technologies: Adversarial Attack Detection & Mitigation
  • PrivaSapien’s PERAI Products: PrivaGPT – Model Security Module

Model Inference + User Safety

  • Requirements: Safe, Accountable, Transparent
  • Technologies: Risk Detection, Risk Summarization, Synthetic Prompt Engineering, Risk based query control
  • PrivaSapien’s PERAI Products: PrivaGPT – User Safety Module

LLM Governance

  • Requirements: Fair, Accountable & Transparent
  • Technologies: Risk summarization, Human Feedback, AI Governance Reporting
  • PrivaSapien’s PERAI Products: PrivaGPT – LLM Governance

Discover the future of
privacy protection

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Safeguarding personal and sensitive data in
today's evolving digital landscape
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+91  9035465400
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contact@privasapien.com
Office
Clayworks Create Campus, 11KM, Arakere Bannerghatta Rd, Omkar Nagar, Arekere, Bengaluru, Karnataka 560076