Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Predict and Prevent: How AI is Changing Insider Risk Management

Insider risk has become one of the most urgent and financially consequential cybersecurity challenges for today’s organizations. Insider Risk is a top concern for the C-Suite and Boards, and organizations must be prepared to detect and respond to insider risks. In fact, according to IBM’s Insider Threat Report, 83% of organizations reported at least one insider-related security incident in 2024 (IBM, Insider Threat Report, 2024).

Atlassian Data Center to Cloud Migration: Why miniOrange Is Your Trusted Partner

Migrating from Atlassian Data Center to Cloud is a major step toward modernization. With miniOrange, the process becomes seamless, secure, and fully automated — ensuring no data loss or downtime. Our solutions help you manage users, licenses, and compliance effortlessly while enhancing security and performance. Move smarter and faster with miniOrange to unlock the full potential of Atlassian Cloud.

The Secret Backdoor in Your Firewall... How Attackers Get In WITHOUT Hacking!#cybersecurity#InfoSec

Your WAF is Providing a False Sense of Security Improper network configuration can completely nullify the effectiveness of your Web Application Firewall. If attackers can discover your origin server's direct IP address: They can bypass your expensive security controls entirely. Your "internal" services become externally exposed. You have a massive, unknown gap in your defenses. This animation is a clear example of why security doesn't end with buying a tool. Proper integration and a zero-trust mindset are non-negotiable.

Investigate Amazon EKS Audit Logs with Teleport Identity Security

In Teleport 18, we’ve added official support to import Amazon EKS Audit Logs into Teleport Identity Security. This capability allows teams to have visibility into actions performed on Amazon EKS clusters when those actions were not executed via Teleport. Amazon EKS Audit Logs in Teleport Identity Security will be generally available in Teleport 18.3, coming November 2025. Your browser does not support the video tag.

Seven Bibliography Mistakes SparkDoc Catches, Plus How to Keep Them Out of Your Drafts

Good writing can wobble at the finish line when the references go wrong. Reviewers notice. Teachers notice. Readers who care about sources notice first of all. Bibliography mistakes do not only weaken credibility, they slow down the whole process because every small error leads to another round of checking. This guide looks at the errors that appear again and again, plus how an AI-aware workflow reduces them without turning the page into a sales pitch. The goal is a clean, verifiable bibliography that supports the argument rather than distracts from it.

How Responsible AI Governance Strengthens Cybersecurity Defenses

Here's something that should keep you up at night: cybercrime might cost the global economy $10.5 trillion every year by 2025. That's not a typo. Traditional security measures? They're already struggling to keep pace. Attackers have figured out how to weaponize artificial intelligence, launching sophisticated campaigns that waltz right past conventional defenses like they're invisible.

Reach Security Recognized as a CRN® 2025 Stellar Startup!

Reach Security announces that CRN , a brand of The Channel Company, has included Reach Security on its 2025 Stellar Startups list in the Security category. This prestigious list highlights fast-rising technology vendors that are driving innovation and fostering growth in the IT channel with groundbreaking products.

Language Switching Attacks: The New Threat Vector in LLM Security

Language Switching Attacks: The New Threat Vector in LLM Security In this clip from "Securing AI Part 4: The Rising Threat of Hidden Attacks in Multimodal AI," Diptanshu Purwar discusses the growing trend of language-switching attacks. These techniques exploit the ongoing development and training gaps in Large Language Models (LLMs). Diptanshu explains how attackers can evade an LLM's built-in filters and guardrails by rapidly shifting between different languages, particularly less common ones, to find weaknesses where the model's safety data is sparse.