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By Rahul Sharma
Personally Identifiable Information (PII) is any data that uniquely identifies an individual. This can range from apparent details like names and Social Security numbers to more subtle information like IP addresses and login IDs. The growing volume of data collected in our digital age amplifies the significance of distinguishing between sensitive and non-sensitive PII, given their different handling requirements and associated risks.
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By Rahul Sharma
Personally Identifiable Information (PII) encompasses data that uniquely identifies an individual. Examples of PII include direct identifiers like full names, social security numbers, driver's license numbers, and indirect identifiers such as date of birth, email and IP addresses. The precise nature of PII can vary depending on the context and jurisdiction, but its defining characteristic is its ability to single out a specific person.
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By Rahul Sharma
Encryption is a fundamental procedure in cybersecurity that transforms data into a coded format, making it inaccessible to unauthorized users. It has evolved significantly from simple ciphers in ancient times to complex algorithms like AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman), which are used today. Encryption ensures data confidentiality, integrity, and authenticity, which is crucial in protecting sensitive information across various domains.
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By Rahul Sharma
Monitoring and auditing are critical components of cybersecurity, designed to detect and prevent malicious activities. Monitoring involves real-time observation of system activities, while auditing entails a systematic review of logs and interactions. Large Language Models (LLMs), such as GPT-4, are increasingly integrated into various applications, making them attractive targets for cyber threats.
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By Rahul Sharma
The National Institute of Standards and Technology (NIST) has announced the launch of Assessing Risks and Impacts of AI (ARIA), a groundbreaking evaluation program to guarantee the secure and trustworthy deployment of artificial intelligence. Spearheaded by Reva Schwartz, ARIA is designed to integrate human interaction into AI evaluation, covering three crucial levels: model testing, red-teaming, and field testing.
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By Rahul Sharma
API Management is a comprehensive process that involves creating, publishing, documenting, and overseeing application programming interfaces (APIs) in a secure, scalable environment. APIs are the backbone of modern software architecture, enabling interoperability and seamless functionality across diverse applications. They facilitate the integration of different software components, allowing them to intercommunicate and share data efficiently.
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By Amar Kanagaraj
When evaluating models or products for their ability to scan and mask Personally Identifiable Information (PII) in your data, it's crucial to follow a systematic approach. Let’s assume you have a dataset with 1,000,000 rows, and you want to scan and mask each row.
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By Amar Kanagaraj
Developers often use two prominent techniques for enhancing the performance of large language models (LLMs) are Retrieval Augmented Generation (RAG) and fine-tuning. Understanding when to use one over the other is crucial for maximizing efficiency and effectiveness in various applications. This blog explores the circumstances under which each method shines and highlights one key advantage of each approach.
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By Amar Kanagaraj
In the evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as pivotal technology, driving advancements in natural language processing and generation. LLMs are critical in various applications, including chatbots, translation services, and content creation. One powerful application of LLMs is in Retrieval-Augmented Generation (RAG), where the model retrieves relevant documents before generating responses.
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By Rahul Sharma
Zero Trust Security Models are a cybersecurity paradigm that assumes no entity, whether inside or outside the network, can be trusted by default. This model functions on the principle of "never trust, always verify," meaning every access request must be authenticated and authorized regardless of origin.
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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
Introducing Protecto SecRAG, the revolutionary platform that empowers you to launch your own AI assistants/chatbots. No coding is required. Simply connect your existing data sources to Protecto. Our intuitive conversation UI allows you to ask questions about your data in plain English, just like you'd talk to a colleague. SecRAG powers a Telco's contracts bot, a large service providers' talent acquisition co-pilot, a healthcare insurance provider's benefits bot and many more.
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By Protecto
Introducing Protecto's SecRAG, the game-changer for secure AI. SecRAG stands for Secure Retrieval Augmented Generation, a turnkey solution. No need to build complex rag or access controls from scratch. Protecto provides a simple interface and APIs to connect data sources, assign roles, and authorize the data. In a few minutes, your secure AI assistant will be ready. When users ask your Protecto-powered AI assistants, Protecto applies appropriate access control to find the right data and generate responses that don't expose other sensitive information that the user is not authorized to see.
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By Protecto
Worried your AI is leaking sensitive data? Stuck between innovation and data protection fears? Protecto is your answer. Embrace AI's power without sacrificing privacy or security. Smartly replace your personal data with tokenized shadows. Move at the speed of light, free from data leaks and lawyer headaches. Protecto enables Gen AI apps to preserve privacy, protect sensitive enterprise data, and meet compliance in minutes.
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By Protecto
GPTGuard - ChatGPT-like insights, zero privacy risk Want to chat with LLMs like ChatGPT without sacrificing privacy? GPTGuard keeps your interactions secure and private by masking sensitive data in your prompts. GPTGuard shields sensitive data through a unique masking technique that allows LLMs to grasp the context without directly receiving confidential information. Discover the power of safe AI with GPTGuard's special data masking technology.
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By Protecto
Discover how to anonymize your prompts, control your data, and avoid privacy, security, and compliance issues.
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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.