Protecto

Standard PII detection was built for names, emails, and phone numbers. It was never designed for what comes after. A medical code. A blood type. A national tax identifier. All just as identifying, and far less likely to be caught. In agentic AI workflows, those fields move through prompts, retrieved documents, and tool outputs the same way a name does. Most tooling was never built to catch them. Protecto Vault June 26 release adds 13 new entities across healthcare and regional compliance. Scroll through to see what's covered. No custom regex. No breaking changes. Which of these entity types does your AI workflow handle today? Drop it in the comments. #ProtectoAI #ProductRelease #PII

Traditional security tools inspect traffic. AI security has to understand it. That's the shift many organizations are just beginning to realize. As AI becomes part of everyday workflows, the biggest risks aren't always hidden in ports, protocols, or APIs - they are hidden in the content itself. 🎙️ In this conversation, Amar Kanagaraj (Founder & CEO, Protecto) joins Komal Bhardwaj and Brian Huhn from NetScaler to discuss why traditional security approaches fall short for AI and what enterprises need to rethink as AI adoption grows. Watch the discussion and let us know: Do you think traditional security tools are enough for AI workloads, or does AI require an entirely new security model? Why? 👇 #ProtectoAI #NetScaler #AISecurity

AI agents now reach every enterprise system. Sensitive data travels with every tool call. Engineering teams are making real architectural decisions about this right now. Some are building. Some are buying. Some have not gotten there yet. Curious where teams actually stand in 2026. Suppose you picked A: Curious how you handled entity detection across healthcare data and country-specific identifiers. That is usually where custom builds start breaking. Drop your stack and approach in the comments. #ProtectoVault #DataPrivacy #AgenticAI

Is your AI governance strategy tied to one model provider? As enterprises adopt multiple LLMs, one question becomes increasingly important: Should your AI security and governance be tied to a single ecosystem or work consistently across all of them? 🎙️ In this conversation, Amar Kanagaraj (Founder & CEO, Protecto) joins Komal Bhardwaj and Brian Huhn from NetScaler to discuss one of the biggest challenges enterprises face as AI infrastructure evolves, and why flexibility matters just as much as security. Watch the discussion and share your perspective: If your organization uses multiple AI models today (or plans to), what's your biggest concern: vendor lock-in, governance, security, or performance? 👇 #ProtectoAI #NetScaler #AIGovernance

Most agent architectures have a sensitive data exposure that the security team has not mapped yet. It sits at the MCP boundary. When an agent calls an external tool, it sends full context. Customer records. Financial details. Internal contracts. Everything it has access to travels with the request. There is no inspection point. No policy enforcement. No audit trail. Your AI gateway does not see it. Your DLP was not built for it. And your agents do not know what they should not send. Swipe through to see exactly where the gap is, why existing tools miss it, and how Protecto Vault enforces a data control plane between your agents and every MCP tool call. Trusted by Automation Anywhere, Inovalon, Dell Technologies, Citrix, Bank Muscat, and more. If your team is shipping agents in a regulated environment, this is the architecture question you need to answer before go-live. What does your current setup do at the MCP boundary? Drop it in the comments. #ProtectoVault #MCPSecurity

Rolling out enterprise AI in 90 days? The technology isn't usually the hardest part. The real challenge is building the right foundation before AI reaches production. 🎙️ In this conversation, Amar Kanagaraj (Founder & CEO, Protecto) joins Komal Bhardwaj and Brian Huhn from NetScaler to discuss what enterprises should be thinking about before deploying AI at scale, and why those early decisions matter more than most teams realize. Watch the discussion and let us know: If you had 90 days to roll out enterprise AI, what would be your first priority? #ProtectoAi #NetScaler #EnterpriseAI

The biggest risk in enterprise AI isn't the model. It's the data flowing through it. When AI agents access enterprise systems, they don't just process prompts—they interact with APIs, MCPs, and workflows that often contain sensitive business data. The challenge isn't simply protecting that data. It's ensuring the right data reaches the right AI agent, at the right time, while staying compliant with privacy and data sovereignty requirements. So here's the question: Is your AI architecture built to govern data or just move it? 🎙️ In this conversation, Amar Kanagaraj (Founder & CEO, Protecto) explains why data governance is becoming a foundational layer for enterprise AI and how the NetScaler × Protecto integration helps organizations secure AI workflows. Watch the discussion and let us know: What do you think is the biggest challenge in securing enterprise AI data privacy, governance, or data sovereignty? #ProtectoAI #NetScaler #EnterpriseAI

Most healthcare AI teams are HIPAA-compliant on paper. They are not HIPAA compliant at runtime. Data storage is secured. Cloud infrastructure is HIPAA-eligible. BAAs are signed with vendors. What is not covered is what happens inside the AI pipeline itself. When your RAG system queries a medical document, PHI goes directly into the LLM context window. No masking. No access check. The model sees it, reasons over it, and may output it in plain text. Your compliance posture was built for data at rest. Not for data in motion through an AI stack. This carousel maps what HIPAA actually requires in AI, where the retrieval layer breaks, what a compliant architecture looks like, and what happens when you skip the masking layer. Swipe through all 7 slides. One question for the comments: does your healthcare AI pipeline currently have a masking layer between your document store and your LLM? Drop a Yes or No below. #ProtectoAI #HIPAA #HealthcareAI #AICompliance

The biggest AI security shift isn't the model. It's what the agent does next. Chat AI and agentic AI are not the same security problem. Not even close. And most enterprises are still treating them like they are. Quick question for the security and AI folks here: When an AI agent takes action inside your enterprise systems, are you confident your current governance layer can catch it before damage is done? Amar Kanagaraj (Founder & CEO, Protecto), Komal Bhardwaj, and Brian Huhn (NetScaler) break down exactly why this gap exists and what closing it actually looks like. Link in comments. Watch it and tell me: does your current AI gateway cover this? #ProtectoAI #NetScaler #AgenticAI

Most teams test whether their RAG returns the right document. Very few test whether it can tell the difference between "the policy applies" and "the policy does not apply." Cosine similarity measures the angle between two compressed vectors. It does not reason. The word "not" is one signal among thousands, and it frequently gets diluted when the model compresses a sentence into a single point. For legal, compliance, and medical use cases, this is not a theoretical edge case. It is a production failure mode. Amar Kanagaraj breaks down the three most common cosine similarity misconceptions and why your retrieval system needs more than one mathematical signal. Blog link in the first comment. Have you ever hit a retrieval failure in production that turned out to be a negation or exact-match problem? What did you find? #ProtectoAI #RAG #AICompliance