Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

What Is Firewall Configuration and Why Is It So Important?

Firewall configuration is the set of rules, policies, and settings that define how a firewall behaves. Without configuration, a firewall is hardware and software waiting for instructions. With configuration, it becomes a control that determines what traffic is permitted, what is blocked, and what gets inspected before a decision is made. Get the configuration right, and the firewall does its job. Let it drift, and you have the appearance of protection without the substance of it. This is not an edge case.

AI Governance on AWS: Discover, Observe, and Control AI in Production

AI adoption within AWS environments is accelerating faster than most security and governance programs. AI agents, APIs, MCP servers, and model integrations are entering production across cloud environments, often without centralized visibility or runtime controls. In this webinar, you’ll see how teams can discover AI workloads across AWS accounts, understand what AI systems are actually doing at runtime, enforce policy in real time, and generate continuous governance evidence without slowing engineering teams down. The session focuses on practical operational capabilities for AI systems already running in production.

Why You Must Still Review AI Code

In this video, we break down why skipping code reviews is a massive mistake that will ultimately slow you down, leave you vulnerable, and compromise your system's accountability. We dive into three concrete reasons why reviewing AI-generated pull requests actually makes you a faster, safer developer, including a real-world story of a production bug caught in under 90 seconds. Resources Chapters.

How Organizations Can Assess and Manage AI-Related Risks

Organizations assess and manage AI-related risks by establishing a cross-functional governance framework, mapping risks based on impact and financial likelihood, and instituting continuous monitoring that connects AI asset discovery to risk quantification, compliance, and enforcement. The most effective programs treat AI risk management not as a one-time assessment but as a continuous, data-driven discipline that evolves alongside the AI systems it governs. ‍

What Server Do You Need to Run an AI Language Model?

Running your own AI model sounds exciting, but one question appears almost immediately: what kind of server do you actually need? A small language model can run on a personal workstation, while a large 70B parameter model may require enterprise-grade GPUs and expensive infrastructure. Choosing the wrong hardware can lead to wasted money, unnecessary complexity, or disappointing performance.

What AI Governance Tools Exist in the Market Today

‍AI governance tools are software platforms designed to help organizations manage AI risks, ensure regulatory compliance, and enforce responsible AI use across the machine learning lifecycle. The market has expanded rapidly, and in 2026 it includes tools spanning compliance automation, model observability, data governance, infrastructure security, and integrated risk quantification.

The Safety Problem Nobody Warns You About When You Start Training a Language Model

There's a version of the LLM safety conversation that stays comfortably abstract - AI alignment, existential risk, theoretical failure modes that matter at a scale most organizations will never reach. That conversation is important, but it's not the one most product and technology leaders need to be having right now. The one they need to be having is more immediate and considerably more practical: how the specific decisions made during llm training services directly shape whether the model you deploy is one your organization can actually stand behind.

Latency Lessons From Building a ReAct AI Agent for Agentic Search

Egnyte AI surfaces insights from an organization's documents for regulated industries—life sciences, financial services, architecture, engineering, and construction—and does that within existing permissions and compliance controls. Our AI Assistant is the conversational front door. Ask a question about your documents in plain language, summarise a contract, find the latest version, pull a compliance clause, and get an answer grounded only in the files you're permitted to see.

Best Tools for Securing MCP and LLM Integrations

Shadow IT used to mean employees spinning up unsanctioned software-as-a-service (SaaS) apps that stored company data without approval. Today, shadow MCP and unsanctioned LLM integrations represent the next evolution, and they're more dangerous. Model context protocol (MCP) servers don't merely store data; they act on it, executing code, calling APIs, and accessing internal tools on behalf of AI agents that developers connect with a config file.