Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Security Starts With Context: The 3 Signals That Actually Drive Change

It's always a pleasure to sit down and chat with Ed. Good security decisions don’t start with alerts. They start with context. We rarely do anything in life without understanding some baseline of context. Otherwise, we're essentially "flying blind." Garrett breaks down the three signals that actually drive meaningful change:⇢ A clear baseline of how your environment really operates⇢ What’s happening in the outside threat landscape⇢ What your own history is already telling you in the context of your business.

Best AI SOC Platforms for 2026: How to Choose the Right One

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo If you are evaluating security platforms in 2026 based on which one has the best chatbot or can write a slightly better Python script for you, you’re fighting the last war. Attackers are already using AI to scale their operations with speed and precision. If your “AI SOC platform” is just a co-pilot that summarizes tickets while humans do all the work, you’re behind.

Stop Ignoring This AI Bug! (Safety Security) #shorts

Are you confusing AI Safety with AI Security? In this clip, we break down why AI is a "Socio-Technical" system and why that matters for your code. We ask the expert: How do you handle "Safety Bugs" (like bias) versus traditional "Security Bugs" (like hacks)? The answer might save your next project. Subscribe for more AI Security insights! @protectoai.

LLM Red Teaming: Threats, Testing Process & Best Practices

LLM red teaming is a proactive security practice that involves systematically testing large language models (LLMs) with adversarial inputs to find vulnerabilities before deployment. By using manual or automated methods to probe for weaknesses, red teamers can identify issues like harmful content generation, bias, or security exploits, which are then addressed through a continuous “break-fix” loop to improve the model’s safety and reliability.

AI Deepfakes Are Impersonating Religious Figures to Solicit Donations

WIRED reports that deepfake attacks are impersonating pastors and other religious figures in order to scam congregations. Father Mike Schmitz, a priest who hosts a podcast with over a million followers, warned his listeners in November that AI-generated deepfakes were using his likeness to fraudulently solicit donations. WIRED found that several of these fake accounts are still active on TikTok, and they appear when a TikTok user searches for Father Schmitz.

AI in the SOC

Gartner frames the AI SOC landscape as a dichotomy: providers pursuing full SOC replacement versus those building AI products to augment existing staff. Of these two approaches, only augmentation aligns with real-world security operations. It helps analysts triage alerts, investigate faster, enrich context, and summarize incidents with better consistency, all while keeping humans in the loop, even if their day-to-day efforts change.

From Dugouts to Data Lakes: Applying Moneyball to the AI SOC

In AI-powered security, advantage comes not from automation alone, but from clear insight into how decisions are made. At Arctic Wolf, home to one of the world’s largest commercial security operations centers (SOC), we process over 10 trillion security events weekly. Rather than chasing automation for its own sake, we build AI that scales human expertise – preserving judgment where it matters most. But what is the optimal combination of humans and machines for security operations?

Sensitive Data Is the Common Thread Across Most OWASP Top 10 Issues. Here's Why

The OWASP Top 10 is usually presented as a list of technical failures. Broken access control. Injection. Insecure design. Misconfiguration. Each category points to something that went wrong in the application. What it doesn’t say explicitly is what was actually at risk when it went wrong. In most real incidents, the answer is not “the application.” It’s the data inside it. Sensitive data is the reason attackers care about OWASP failures in the first place. Credentials.