Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

7 Smart Ways to Improve Security Monitoring With Automation

Security can feel like a constant background concern for anyone who runs a startup or manages product delivery. One missed alert or late response can cause serious damage. Manual monitoring is no longer effective, as it doesn't scale well. Workers get tired, which causes logs to pile up and signals to be easily missed. That is why many businesses are turning to automation.

New Cloudflare report warns of a 'Technical Glass Ceiling' stifling AI growth and weakening cybersecurity

New research shows that organisations modernising apps are 3x more likely to see AI payoffs, while those clinging to legacy systems face rising security risks and developer talent shortages.

ServiceNow's Virtual Agent Vulnerability Shows Why AI Security Needs Traditional AppSec Foundations

The recent disclosure of what security researchers are calling "the most severe AI-driven vulnerability uncovered to date" in ServiceNow's platform serves as a stark reminder: securing agentic AI isn't just about new AI-specific controls; it requires getting the fundamentals right first.

Beneath the AI iceberg: The forces reshaping work and security

In conversations about AI, there’s a tendency to treat the future like a horizon we’re walking toward, always somewhere ahead, always a question of when. But if we look closely, the forces reshaping work, identity, and security beneath the surface are far more consequential than most people realize. More importantly, that reshaping is already happening.

Arctic Wolf and AWS: AI-Powered SOC and Security Incident Response

Discover how Arctic Wolf partners with Amazon Web Services (AWS) to deliver cutting-edge, AI-powered Security Operations Center (SOC) capabilities and advanced security incident response solutions. This video explores how Arctic Wolf leverages AWS cloud infrastructure and artificial intelligence to provide: Learn how this powerful combination enhances your organization's security posture, reduces response times, and protects against evolving cyber threats through intelligent automation and comprehensive managed detection and response (MDR) services.

How Agentic AI Creates Shadow APIs: Security Risks Explained

How Agentic AI Creates Shadow APIs: Security Risks Explained As businesses move from static applications to Agentic AI, the security landscape is shifting beneath our feet. In this clip from the A10 Networks webinar, "APIs are the Language of AI: Protecting Them is Critical," experts Jamison Utter and Carlo Alpuerto discuss a new frontier in cybersecurity: AI that builds its own APIs.

Stop buying niche tools to secure your AI. #cybersecurity #aisecurity #engineering

In his first prediction for 2026, Ev explains why that strategy is about to fail. We used to let microservices run anonymously because we had bigger fires to fight. But when all software becomes autonomous AI, anonymity is a risk you can't afford. If your software behaves like a human, why separate it from your human identity strategy? The future isn't "NHI." It's a Unified Identity Layer where humans and non-humans are managed as equals.

How Security Teams Can Tackle Information Overload and Work Smarter

The modern security professional drowns in data every single day. Between threat intelligence reports, compliance documentation, vendor assessments, and incident logs, there's simply too much to read and not enough hours to read it. This isn't just frustrating. It's a genuine security risk. When critical information gets buried under mountains of PDFs and reports, threats slip through the cracks. The good news? There are practical strategies and tools that can help security teams cut through the noise. Let's explore how to manage this avalanche of information without burning out your team.

Using LLMs, CVSS, and SIEM Data for Runtime Risk Prioritization

A recent University of North Carolina Wilmington study tested whether general-purpose large language models could infer CVSS v3.1 base metrics using only CVE description text, across more than 31,000 vulnerabilities. The results show measurable progress, but they also expose a hard limit that matters far more than model selection: Model quality helps, but missing context sets a ceiling on reliability.