|
By Mariyam Jameela
AI models are becoming essential to enterprise innovation, but the sensitive data that powers them is creating new security and privacy challenges. Even when raw training datasets remain inaccessible, attackers may still identify whether specific information was used to train a model through membership inference attacks.
|
By Shankar Rajamani
A privacy policy that only works in English is not a global privacy policy. It is an English-language policy that a global company happens to be using. That distinction matters more than most teams realize. Enterprises now centralize contracts, HR files, healthcare records, and support conversations from regional offices around the world into a shared AI platform, often assuming that whatever detection and masking logic works for their English-language content will work everywhere else. It does not.
|
By Amar Kanagaraj
Here is a document going into an AI assistant: A simple masking system produces: The information is protected, but the document has become almost impossible for the AI to reason about. It no longer knows who introduced whom, who approved the proposal, or whether the same person appears multiple times. By removing identity, we destroyed the relationships that give the document meaning.
|
By Shankar Rajamani
A few years ago, an Australian government health agency released what it believed was a fully de-identified dataset covering 10% of the national population. Names, addresses, and other obvious identifiers had been stripped out. Researchers showed individuals could be re-identified using nothing more than rare medical procedure codes and treatment dates cross-referenced with publicly available information. No names were needed.
|
By Amar Kanagaraj
You can build a masking demo in an afternoon. Run a regex for credit card patterns, swap the match for XXXX, and ship it. The demo works, the compliance slide says “no PII sent to the LLM,” and everyone moves on. That demo is fooling you by leaving things out. It works because the input is a) clean (card 4111 1111 1111 1111), b) because the only sensitive thing in it is a textbook PII pattern, and c) because nobody downstream ever needs to use the value again.
|
By Mariyam Jameela
Here is a number that should stop every CISO cold. Gartner projects that by 2028, 25% of enterprise GenAI applications will face five or more security incidents per year, nearly triple the 9% recorded in 2025. The acceleration is not slowing. Meanwhile, research by OpenText and the Ponemon Institute finds that 79% of organizations have not yet reached full AI maturity in cybersecurity, meaning most enterprises are deploying generative AI without the foundational controls needed to govern it.
|
By Mariyam Jameela
AI today has moved beyond experimentation. In the modern age, enterprises are embedding AI across various aspects of their businesses, including customer support, document processing, software development, healthcare, financial services, and decision-making workflows. According to a recent McKinsey report, 88% of businesses use AI in at least one business function. This reflects how AI is now becoming the center of several enterprise operations.
|
By Mariyam Jameela
Every AI application relies on data. From customer conversations and healthcare records to financial transactions, organizations process enormous volumes of sensitive information every day. As AI adoption grows, so does the need to protect that data from misuse, exposure, and compliance risks. This is why understanding what privacy by design entails has become a business necessity rather than just a compliance requirement.
|
By Amar Kanagaraj
A customer support agent needs a payment reference, a token or transaction ID, to issue a refund. A summarization agent reading the same ticket needs none of it. A billing agent needs only the last four digits to match a transaction. A fraud agent needs the full credit card number, but only when a case is open and only for the account it is reviewing. Traditional DLP sees one thing across all four: sensitive data, a 16-digit string that matches a card pattern. It makes one choice: block, redact, or allow.
|
By Mariyam Jameela
Enterprises today are looking to grow faster by adopting artificial intelligence. Teams are now building AI copilots, automating workflows with AI agents, and using Retrieval- Augmented Generation (RAG) to search internal knowledge bases. However, with every successful AI deployment, there is one very important question. How do you keep sensitive enterprise data from becoming a potential AI security risk?
|
By Protecto
Why Your Healthcare RAG Pipeline Is Leaking PHI Most healthcare organizations believe their AI assistant is secure once they restrict who can log in. Unfortunately, that's only part of the story. Modern healthcare AI applications rely on Retrieval-Augmented Generation (RAG), where patient records, physician notes, insurance claims, and medical documents are embedded into vector databases to power intelligent search.
|
By Protecto
Planning an enterprise AI rollout in 90 days? Establishing a robust AI Gateway Architecture is the critical first step to ensuring data compliance, enforcement, and security. Letting application traffic run straight to LLMs exposes your organization to severe security and compliance liabilities.
|
By Protecto
Understanding the nuances of AI Traffic Security is critical because traditional firewalls and API gateways are fundamentally ill-equipped to inspect unstructured natural language. In the past, enterprise data remained safely within the corporate perimeter. Today, employees are pasting highly sensitive information directly into external models, creating a massive vulnerability where the risk lies in the meaning and content, rather than syntax or connections.
|
By Protecto
Sensitive data used to live in predictable places database columns, known field names, structured rows. That changed when data moved into documents. And it changed again when AI workflows arrived. In this video, we walk through why detecting sensitive data in AI pipelines is fundamentally different from traditional data discovery, and why the old approaches break. We cover the four failure modes that make detection hard in AI workflows.
|
By Protecto
AI agents don't just answer questions—they access enterprise data, call APIs, interact with MCP servers, and trigger workflows. That means sensitive information like PII, PHI, HR records, pricing data, financial information, and confidential business data can flow through AI systems. In this YouTube Short, Amar Kanagaraj explains why AI governance, data security, and data sovereignty are essential for enterprise AI deployments—and how the NetScaler × Protecto integration helps organizations secure AI workflows.
|
By Protecto
As we move deeper into 2026, enterprise AI has shifted from simple chat interfaces to autonomous agents executing complex workflows. If you aren't routing this traffic through a dedicated AI Gateway, your sensitive data is already at risk.
|
By Protecto
ChatGPT is read-only, but AI Agents take action on your behalf. What happens when they go rogue? Discover the hidden cybersecurity risks of Agentic AI and unauthorized remote execution. AI gateways were built for a world where AI meant "prompt in, response out." That world is gone. Today, AI agents call APIs, trigger workflows, and take actions across your enterprise systems autonomously. This massive shift from passive data exfiltration to active, unauthorized execution requires a completely new security model where every input is treated as potentially hostile.
|
By Protecto
Are your autonomous AI workflows leaking sensitive customer data? In this comprehensive PII detection demo, we compare the traditional NER-based Microsoft Presidio with the advanced LLM-based Protecto DeepSight. Discover how to secure your enterprise AI, stop format drifts, and prevent severe compliance risks like GDPR and HIPAA violations.
|
By Protecto
While the new OpenAI privacy filter detects basic PII, true data protection requires a much deeper system. In this video, we expose the hidden security vulnerabilities inside modern AI workflows and explain why aggressive data redaction actually destroys your model's utility. What you will discover in this breakdown: The Redaction Trap: Why simply deleting sensitive data breaks your AI's contextual understanding.
|
By Protecto
Why AI security needs more than one tool Most teams believe a single cybersecurity tool—like WAF, EDR, or API security—is enough to protect their AI systems. But that approach is outdated. AI security is not one layer—it’s a full stack problem. Discovery – Identify Shadow AI and unknown AI usage Build-Time Security – Prevent data poisoning & model risks (MLSecOps) Runtime Security – Stop real-time AI attacks and agent misuse Governance (AISPM) – Ensure visibility, compliance, and policy control.
|
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.
|
By Protecto
Improve your organization's privacy and security posture by automating data mapping. Read on to understand some best practices for privacy compliance.
|
By Protecto
Protecto can help improve your privacy and security posture by simplifying and automating your data minimization strategy. Read on to know more.
- July 2026 (13)
- June 2026 (11)
- May 2026 (8)
- April 2026 (16)
- March 2026 (22)
- February 2026 (8)
- January 2026 (12)
- December 2025 (15)
- November 2025 (19)
- October 2025 (23)
- September 2025 (13)
- August 2025 (12)
- July 2025 (4)
- June 2025 (6)
- May 2025 (2)
- February 2025 (10)
- January 2025 (15)
- December 2024 (8)
- November 2024 (10)
- October 2024 (15)
- September 2024 (13)
- August 2024 (10)
- July 2024 (10)
- June 2024 (13)
- May 2024 (10)
- April 2024 (5)
- March 2024 (13)
- February 2024 (21)
- January 2024 (4)
- December 2023 (1)
- November 2023 (1)
- September 2023 (3)
- August 2023 (3)
- June 2023 (1)
- May 2023 (2)
- March 2023 (1)
- January 2023 (1)
- October 2022 (1)
- December 2020 (1)
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.