Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A device in your fleet encounters an issue. You try to SSH in only to discover that the IP changed overnight, the customer's firewall blocks inbound connections, and the VPN they set up six months ago stopped working when the device switched from Wi-Fi to cellular. The next several hours disappear into a Slack thread with the customer's IT team trying to get a port opened. Every engineer who has shipped hardware into a customer's environment has a version of this story.

Vulnerability remediation is the process of fixing and validating security flaws in systems, applications, or infrastructure using patches, configuration changes, or compensating controls after they are identified and prioritized.

HR teams manage every stage of the employee lifecycle, from hiring and onboarding to performance management and offboarding. Security teams manage data access, behavioral monitoring, and incident response. Insider risk lives at the intersection of both. When HR and security operate independently, the gaps between them are exactly where data loss happens, and the moments of highest exposure are almost always HR events, such as a resignation submitted, a role change processed, a termination decision made.

During identity verification, organizations typically have to decide between increasing security controls and improving user conversion. Tighter checks mean more abandonment, and smoother flows mean more risk. Most verification flow design is an exercise in finding the right tradeoff. Mobile driver's licenses (mDLs) are different. Because an mDL is cryptographically signed by the issuing DMV and presented directly from a user's device, it's both faster to verify and harder to fake.

Addressing these potentially competing priorities is difficult with today’s technology, and it's an active area of work for government agencies and private organizations alike. But we think there’s a potential path forward if regulations and organizations limit what you have to share, who you have to share data with, and how your data can be used.

It has been a week since we announced Forward Predict at our Innovation Day broadcast, and I'm still taking it in. Since the inception of networking, the industry has been working without a safety net, making changes in the production network without knowing their impact beforehand. The result has been outages and security breaches. This wasn’t a lack of diligence, it was because there was no way to know, with certainty, what a change would do to the production network before it was pushed.

The cybersecurity industry is sleepwalking. We are still captivated by the romanticized image of the hacker: a human in a hoodie manually typing code to breach a network. Wake up to the reality of 2026. The modern adversary is no longer human. It is algorithmic.

Two protocols are shaping the AI revolution: A2A for agent-to-agent delegation, and MCP for agent access to tools and external systems. A2A expands who can participate in a workflow by enabling agent-to-agent delegation. MCP expands what agents can reach by connecting them to data and systems. By the end of 2026, task-specific AI agents are expected to appear in 40% of enterprise applications, up from less than 5% in 2025. That shift changes where security has to live.

Cybersecurity has moved from isolated tools to continuous operations. Most environments already generate alerts and logs across systems, yet attacks still progress undetected. The problem is not visibility but the speed at which teams can detect, understand, and respond to threats. The gap is not caused by a lack of tools but by limited execution capacity. This is where managed security services providers (MSSPs) come in. Modern attacks increasingly rely on identity misuse and legitimate system tools.

As large language models (LLMs) become more embedded in business operations, the risks and attack methods targeting them are evolving just as quickly. The 2025 edition of the OWASP Top 10 for LLM Applications reflects this rapid evolution, addressing the current threats facing generative AI systems in production environments. For organizations investing in LLMs, understanding the risks is crucial for deploying these systems securely.