Security teams can now eliminate container vulnerabilities at the source without developer effort or version upgrades. At Seal Security, we believe vulnerability management should start with secure foundations.That’s why we’re excited to share that Seal’s pre-patched packages to harden base and secure images are now officially integrated in Wiz. This partnership brings together Wiz’s best-in-class cloud visibility with Seal’s remediation-first approach to container security.

Many healthcare organizations want to move workloads to AWS but stall because they’re uncertain how to maintain HIPAA compliance in the cloud. The good news: AWS provides the tools and certifications needed for HIPAA-eligible services. The challenge is implementing them correctly. AWS has been HIPAA-eligible since 2013 and currently offers over 150 services that can be used in HIPAA-compliant architectures. But eligibility doesn’t equal compliance.

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.

Nowadays, it’s not uncommon for enterprise IT leaders to find themselves in a situation that seems like a catch-22. On one hand, they’re expected to make data-driven decisions that improve productivity and profitability in a business. On the other, they’re preoccupied with their core responsibilities such as protecting critical systems, maintaining network security, and accelerating investigations when a security event occurs. Traditional tooling won’t keep up with modern systems.

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.

Late last week, GreyNoise published one of the clearest signals we have seen that AI systems are no longer just research targets. They are operational targets. Their honeypot infrastructure captured 91,403 attack sessions between October 2025 and January 2026, revealing two distinct campaigns systematically mapping AI deployments at scale. This is a meaningful inflection point.

Just-in-Time access is widely accepted as a best practice for reducing standing privilege. The challenge for most organizations is not deciding to use JIT, but designing access policies that actually reduce risk without slowing engineers down. Security teams want tighter controls, stronger auditability, and less standing access. Engineering teams need fast, predictable access to do their work. When approval policies are too rigid, teams get blocked or work around controls.

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.