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By Rahul Sharma
Data security is a critical concern for organizations worldwide. Cyberattacks and data breaches have put sensitive information such as customer data, payment details, and user credentials at constant risk. Techniques like tokenization vs hashing provide essential tools to safeguard this information effectively. Understanding the distinctions between these methods is crucial for selecting the right approach.
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By Amar Kanagaraj
In today’s AI-driven world, where data powers everything from personalized recommendations to advanced business analytics, safeguarding sensitive information is more critical than ever. As data breaches and regulatory requirements grow more complex, organizations face mounting pressure to protect personal and confidential information with a data privacy vault that ensures security and compliance.
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By Rahul Sharma
Large language models (LLMs) have transformed various industries by enabling advanced natural language processing, understanding, and generation capabilities. From virtual assistants and chatbots to automated content creation and translation services, securing LLM applications is now integral to business operations and customer interactions. However, as adoption grows, so do security risks, necessitating robust LLM application security strategies to safeguard these powerful AI systems.
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By Amar Kanagaraj
In the evolving landscape of artificial intelligence, Large Language Models (LLMs) have become essential for natural language processing tasks. They power applications such as chatbots, machine translation, and content generation. One of the most impactful implementations of LLMs is in Retrieval-Augmented Generation (RAG), where the model retrieves relevant documents before generating responses.
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By Vaibhav
As the adoption of large language models (LLMs) continues to surge, ensuring their security has become a top priority for organizations leveraging AI-powered applications. The OWASP LLM Top 10 for 2025 serves as a critical guideline for understanding and mitigating vulnerabilities specific to LLMs. This framework, modeled after the OWASP Top 10 for web security, highlights the most pressing threats associated with LLM-based applications and provides best practices for securing AI-driven systems.
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By Rahul Sharma
Protecting PII has never been more crucial. In today’s digital world, where data breaches are rampant, ensuring PII data security is essential to maintain trust and compliance with regulations like GDPR and CCPA. PII protection safeguards sensitive personal information, such as names, addresses, and social security numbers, from cyber threats, identity theft, and financial fraud.
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By Amar Kanagaraj
As Large Language Models (LLMs) continue to advance and integrate into various applications, ensuring LLM data privacy remains a critical priority. Organizations and developers must adopt privacy-focused best practices to mitigate LLM privacy concerns, enhance LLM data security, and comply with evolving data privacy laws. Below are key strategies for preserving data privacy in LLMs.
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By Rahul Sharma
Protecting sensitive information is critical in healthcare. Personally Identifiable Information (PII) and Protected Health Information (PHI) form the foundation of healthcare operations. However, these data types come with significant privacy risks. Advanced de-identification techniques provide a reliable way to secure this data while complying with regulations like HIPAA.
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By Rahul Sharma
Protected Health Information (PHI) contains sensitive patient details, including names, medical records, and contact information. De-identification of PHI is a critical process that enables organizations to use this data responsibly without compromising patient confidentiality. The Health Insurance Portability and Accountability Act (HIPAA) establishes strict rules to ensure the privacy and security of PHI, making de-identification essential for compliance.
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By Protecto
Cyber insurance, also referred to as cyber liability insurance, is a specialized insurance product designed to help businesses mitigate financial losses resulting from cyber threats. In today’s digital landscape, cyber risks such as ransomware attacks, malware infections, and data breaches can lead to severe financial and operational damage.
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By Protecto
Discover how Protecto secures data within Ivanti ITSM APIs to prevent data leaks, privacy violations, and compliance risks. In this video, we’ll show how Protecto acts as a data guardrail, ensuring that sensitive information like PII and PHI is identified, masked, and handled securely before it reaches AI agents. Participants: Amar Kanagaraj, Founder & CEO of Protecto Kalyan Vishnubhotla, Director of Strategic Partnerships, Ivanti.
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By Protecto
Discover how to build secure, compliant, and privacy-preserving AI applications with Protecto. In this video, we explain how Protecto's simple APIs protect sensitive data, ensuring compliance with regulations like HIPAA. Learn how a healthcare company used Protecto to create an AI-based fraud detection application while safeguarding millions of patient health insurance claims. Protecto's API masks sensitive information, preserving context and meaning without exposing personal identifiers like names or social security numbers.
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By Protecto
Welcome to the Protecto Snowflake Integration Demo, where we show you how to safeguard sensitive data using Protecto’s advanced AI-powered masking tools! In today’s world, businesses using Snowflake for AI and analytics face significant risks with sensitive information hidden within unstructured data like comments and feedback columns. Protecto provides a unique solution, precisely masking only the sensitive parts of your unstructured data while leaving the rest untouched, ensuring your datasets remain valuable for analysis.
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By Protecto
Discover how our intelligent data masking solution ensures secure, compliant, and privacy-preserving analytics for your data lakes. Protecto maintains data integrity while empowering your organization to leverage analytics or enable AI/RAG without compromising privacy or regulatory compliance.
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By Protecto
Generative AI is often seen as high risk in healthcare due to the critical importance of patient safety and data privacy. Protecto enables your journey with HIPAA-compliant and secure generative AI solutions, ensuring the highest standards of accuracy, security, and compliance.
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By Protecto
But in the world of gen AI applications, translating and maintaining roles in a vector database is exponentially complex.
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By Protecto
Don't miss out on the critical insights from this exclusive discussion on Gen AI Security and Privacy Challenges in Financial Services brought to you by Protecto!
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By Protecto
Unlock the full potential of Gen AI in finance, without compromising security and privacy. Watch this video for expert advice and cutting-edge solutions.
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By Protecto
Tired of inaccurate LLM (/RAG) responses because of data masking? Generic masking destroys data context, leading to confusion and inaccurate LLM responses. Protecto's advanced masking maintains context for accurate AI results while protecting your sensitive data.
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By Protecto
Know the challenges associated with managing data privacy and security, and the capabilities that organizations need to look for when exploring a data privacy and protection solution.
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By Protecto
Improve your organization's privacy and security posture by automating data mapping. Read on to understand some best practices for privacy compliance.
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By Protecto
Protecto can help improve your privacy and security posture by simplifying and automating your data minimization strategy. Read on to know more.
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Easy-to-use API to protect your enterprise data across the AI lifecycle - training, tuning/RAG, response, and prompt.
Protecto makes all your interactions with GenAI safer. We protect your sensitive data, prevent privacy violations, and mitigate security risks. With Protecto, you can leverage the power of GenAI without sacrificing privacy or security. If you are looking for a way to make your GenAI interactions safer, then Protecto is the solution for you.
Data protection without sacrificing data utility:
- Achieve Compliance And Mitigate Privacy Risks: Preserve valuable information while meeting data retention regulations.
- Embrace Gen AI Without Privacy or Security Risks: Harness the power of Gen AI, ChatGPT, LLMs, and other publicly hosted AI models without compromising on privacy and security.
- Share Data Without Sacrificing Compliance: Comply with privacy regulations and data residency requirements while sharing data with global teams and partners.
- Ensure The Security Of Your Data In The Cloud: Protect your sensitive and personal data in the cloud. Gain control over your cloud data.
- Create Synthetic Data: Harness real-world data for testing without compromising on privacy or security.
- Achieve Data Retention Compliance with Anonymisation: Simplify compliance efforts and safeguard sensitive data.
Protect your enterprise data across the AI lifecycle.