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By Amar Kanagaraj
“Just let the LLM write the SQL.” It sounds powerful. A user types a question in plain English, the model generates a query, the system runs it against the database, and the answer comes back. No SQL knowledge required. No BI tools. No waiting for the data team. It works beautifully in demos. And it is a serious engineering mistake in production. Direct SQL generation from LLMs combines two things that should never be combined: untrusted code generation and privileged execution.
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By Amar Kanagaraj
Every few weeks, a new story surfaces: an AI agent deletes a production database, an autonomous coding tool racks up a five-figure cloud bill, or a chatbot exfiltrates internal documents through a prompt injection attack. The reaction is predictable. “AI is dangerous.” “LLMs can’t be trusted.” “We need better guardrails on the model.” But if you look at the root cause of these incidents, the model is rarely the problem. The system around it is.
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By Amar Kanagaraj
If your first instinct when connecting an LLM to enterprise systems via MCP is to strip out all personally identifiable information, you’re building a system that is useless. The “block all PII” approach sounds responsible. It checks a compliance box. But it fundamentally misunderstands what MCP-based AI systems do and why they need data in the first place. The real engineering challenge is not blocking data.
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By Mariyam Jameela
Zero Trust AI Access (ZTAI) is a security framework that applies “never trust, always verify” principles to every interaction involving AI systems, including LLMs and AI agents, as well as the sensitive data they process. Traditional zero trust was built to protect people accessing applications. ZTAI extends those same principles to a new category of actor: AI itself.
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By Mariyam Jameela
A prompt injection occurs when an attacker manipulates input to your AI system, overriding its instructions. To prevent prompt injection, you need a layered approach: separate system instructions from user input, validate user input before it reaches the model, monitor model outputs for anomalies, enforce least-privilege access for AI agents, and protect the data layer so sensitive information never reaches the model in a readable form. No single fix is enough.
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By Amar Kanagaraj
Last week, OpenAI released Privacy Filter, an open-weight model for detecting and redacting PII in text. It is a thoughtful release: Apache 2.0 licensed, able to run locally, designed for high-throughput workflows, and built to go beyond regex-based detection. This is good news for everyone building enterprise AI. Privacy at the model layer is getting real attention. What we liked most was how clearly OpenAI described the role of the model.
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By Mariyam Jameela
Generative AI security is the practice of protecting the data that flows into AI systems, and the outputs those systems produce, from leaks, attacks, and unauthorized access. Every organization using AI today has the same blind spot. Sensitive data enters an AI pipeline, and most security teams have no visibility into where it goes next. An employee pastes a customer record into ChatGPT. A developer submits code containing API keys to an AI debugging tool.
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By Mariyam Jameela
Over two-thirds of enterprises are already running agentic AI in production, according to a 2025 industry survey on the state of agentic AI security. Fewer than one in four have the visibility to know what those agents are actually doing. That gap is live right now, in systems handling customer data, financial records, and protected health information.
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By Mariyam Jameela
The types of AI guardrails are input guardrails, output guardrails, security guardrails, ethical guardrails, and operational guardrails, each positioned at a different failure point across an inference pipeline. Gartner’s research found that 30% of generative AI projects don’t survive past the proof-of-concept stage, with weak risk controls cited as the leading reason. Most of those projects weren’t badly built. The models worked. The gaps were in what sat around them.
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By Amar Kanagaraj
Every enterprise wants to use AI on its most valuable data — customer records, financial documents, clinical notes, legal files, engineering IP. The problem is simple: the moment that data enters an AI workflow, traditional security stops working. Firewalls protect the network. Encryption protects data at rest. Access controls protect the database. But none of them protect what happens when an AI agent retrieves five documents, synthesizes an answer, and delivers it to a user.
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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.
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By Protecto
Most companies believe their security tools—WAF, EDR, API gateways—are enough to stop cyber attacks. But AI has changed the game. AI-powered attacks: –Learn your security patterns–Adapt in real-time–Bypass traditional defenses These tools were built for a predictable world. AI attackers are non-stop, intelligent, and evolving. That’s why even the best security systems are failing against modern AI threats.
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By Protecto
Is your security stack ready for the agentic revolution? As we move into 2026, Real-Time AI Security has become the new frontier for enterprise protection. In this episode of AI on the Edge, Amar (CEO of Protecto) sits down with security veteran and investor Anand Tangiraja to discuss why traditional "shift left" strategies and legacy tools are failing in the face of autonomous agents.
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By Protecto
Is your SOC ready for the 10-minute attack? In 2026, traditional Security Operations Centers are failing to stop Agentic AI Attacks. Why? Because agents don't follow the rules of legacy software. In this Short, we break down the three reasons your current defense is obsolete. The 3 Reasons Your SOC is Too Slow.
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By Protecto
AI agents just became production-ready overnight. With NVIDIA’s new NeMo Guardrails / NemoClaw-style agent control systems, AI agents can now operate in controlled environments with policies, sandboxing, and guardrails. Sounds safe… but there’s a catch. Agent safety protects what the AI does. But it doesn’t secure what the AI knows. And that’s where the real enterprise risk appears. In this video we break down the difference between.
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By Protecto
AI bias is a real problem. Bias can enter AI systems in many ways: That’s why governments and organizations are focusing on responsible AI policies to ensure AI benefits everyone equally, not just one group. Responsible AI means reducing discrimination and ensuring fairness across all communities. Watch The Full Podcast: Link Below.
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By Protecto
Many people are afraid of Artificial Intelligence. Questions like: The truth is simple: AI is not going anywhere. Instead of fearing AI, the smarter approach is learning how to use AI tools responsibly in your daily work and career. Just like the internet and smartphones changed industries, AI is the next big technological shift. Start small, learn AI tools, and adapt to the future. Watch The Full Podcast: Link Below.
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By Protecto
Your AI works perfectly during testing… but suddenly fails in production. Why? The problem usually isn’t the model — it’s the data. Synthetic data looks clean and structured. But real-world data is messy: typos, missing values, broken formats, and unexpected edge cases. When AI models train only on synthetic datasets, they never learn how to handle real-world complexity. In this video, we explain why synthetic data can break AI systems and how using real production data safely can make AI more reliable.
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By Protecto
AI skills are no longer optional. The US Department of Labor recently released an AI Literacy Framework, making AI knowledge a basic workforce skill for the future. This means every worker should understand: Basic AI principles AI use cases Prompting AI correctly Evaluating AI outputs Using AI responsibly AI literacy is quickly becoming a core job skill across all industries, not just tech.
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By Protecto
AI tools like ChatGPT, Gemini and other LLMs are powerful — but what happens when sensitive data gets sent to them? In this video, we demonstrate how Protecto AI prevents sensitive information from reaching LLMs using Masking APIs and Unmasking APIs. You’ll see a real workflow where user prompts containing credit card details and personal data are automatically masked before being processed by an AI model like Gemini 2.5 Flash.
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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.