Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Why Every Tech Company is Talking About OWASP for AI (and You Should Too)

AI is changing everything—but with innovation comes new risks. In this episode of AI on the Edge, we dive deep into OWASP's Top 10 for Large Language Models with security leader Steve Wilson (Exabeam). Discover why every tech company is suddenly talking about LLM security and how you can stay ahead. Inside this episode: Why traditional security doesn’t work for AI Learn from Steve’s new book The Developer’s Playbook for LLM Security and get actionable tips to protect your AI systems.

DPDP 2025: What Changed, Who's Affected, and How to Comply

India’s Digital Personal Data Protection Act, 2023 (DPDP Act) is finally moving toward activation. In January 2025 the government published the Draft Digital Personal Data Protection Rules, 2025 for public consultation to operationalize the Act. As of late 2025, the Act is enacted but core provisions still await final notification, so a phased rollout remains likely.

From Zero AI Background to GenAI Lead at Peloton #ai #shorts

Amar (Founder & CEO of Protecto) chats with Sabari Loganathan (Head of AI Strategy, Peloton) about how a chance project led to building world-class generative AI systems. From vector search to agentic AI and RAG, discover how Sabari turned technical breakthroughs into real enterprise outcomes.

Mastering LLM Privacy Audits: A Step-by-Step Framework

Language models now touch contracts, tickets, CRM notes, recordings, and code. That means personal data, trade secrets, and regulated content move through prompts, embeddings, caches, and third-party endpoints. If your audit still reads like a generic security review, you will miss the places where leaks actually happen. A modern LLM Privacy Audit Framework starts where the risk starts.

Essential LLM Privacy Compliance Steps for 2025

Large language models are no longer side projects. Sales teams rely on them for emails, support teams for ticket summaries, legal for first-draft reviews, and product teams for search and personalization. That ubiquity changes the risk math. Sensitive information flows through prompts, fine-tuning sets, retrieval indexes, analytics stores, and vendor logs. Regulators now expect the same discipline for LLM pipelines that they expect for core systems handling customer data.

Entropy vs. Encryption: Which Tokenization is Better?

The rapid scale of AI development and deployment has introduced a number of unprecedented privacy and compliance challenges for enterprises. IT and compliance teams are looking for solutions that address these concerns without affecting AI adoption. Tokenization has for long been the solution for protecting sensitive data. However, to implement it correctly, it is critical to understand which type fits best – both protect PII but differently.

How LLM Privacy Tech Is Transforming AI Using Cutting-Edge Tech

The promise of large language models is simple: turn messy text and data into instant answers, drafts, and decisions. The catch is simple: those models are hungry, and the most valuable data you own is also the most sensitive. If that escapes, you have legal, brand, and trust problems. This is where the story shifts. How LLM Privacy Tech Is Transforming AI is about making real deployments possible.