Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Phishing used to be the easy alert. Bad grammar. A link to “Amazon.com.” A sender address off by one character. Analysts joked about it. You almost felt bad for the attacker. That era is over. AI killed it.

So, you want to be a cybersecurity analyst. With the rise in high-profile data breaches, privacy concerns and rapid technological advancements, there’s a greater demand for cybersecurity analysts now. And the demand for cybersecurity analysts is only expected to grow. But before you get too far into pursuing this job, let’s look into the basics of this profession. Below, we answer the most frequently asked questions about becoming a cybersecurity analyst.

For nearly a decade, the security industry has used machine learning to solve detection. By feeding it enough logs and determining abnormal behaviors, it found the threats that rules-based systems miss. This delivered sharper anomaly detection, fewer false positives, and UEBA is now essential. In fact, threat detection and analytics account for close to 44% of total SIEM spend, the single largest use case by far. Using machine learning for detection was only the start.

The banking industry stands at a critical intersection of technology, security, and customer experience. As financial institutions navigate massive data volumes and increasingly sophisticated threats, they’re also trying to survive the digital transformation that’s made customer expectations higher than ever and trust more fragile than before.

Artificial intelligence is rapidly reshaping how security operations centers (SOCs) function. Many organizations are now evaluating AI-native architectures to reduce workload and accelerate investigations. A new architectural narrative is emerging. A growing set of AI-native security vendors are proposing centralizing telemetry in a warehouse and deploying AI agents to replace the operational role of the SIEM. They want to centralize telemetry, apply AI, and automate the SOC.

Healthcare organizations are a primary target for cyberattacks. Outdated legacy tech runs rampant, and ransomware attacks are shutting down hospitals, forcing them to revert to paper records and cancel non-emergency procedures. The ripple effects extend beyond the targeted facility, overwhelming neighboring hospitals, putting lives at risk.

There’s a growing wave of “AI SOC” startups promising autonomous everything. They’ll triage your alerts, investigate threats, and even run your playbooks. Push a button, let the machine handle the mess, and enjoy the magic. It sounds great until the moment something breaks. Then everyone, not just security, asks the same question: “What exactly did it do?” And that’s when these systems turn into a liability.

You don’t need a 20-person SOC to protect your cloud-native environment. What you need is the right strategy: map your risk, embed security early, automate detection, and let smart tooling do the heavy lifting. Here’s how security and DevOps leaders with limited resources can achieve enterprise-level protection without enterprise-level headcount.

For years, we’ve drawn this artificial line that equates observability with uptime, performance, and SRE dashboards, while security is about threats, alerts, SIEMs, and “bad things.” While that separation was always convenient, it was never real. The same logs that tell you your service is slow are the same ones that tell you it’s compromised. We just routed them to different teams, different tools, and different budgets, then acted surprised when neither side had the full picture.

If you hear that a product is 92% effective, you’d assume it’s operating as intended. It seems like a success story. But dig a little deeper, and the picture changes; only 51% say that their security information and event management (SIEM) is very effective. What does it mean when a majority of security relies on a tool that works, but doesn’t work well enough? Not broken, not exceptional. It’s somewhere in between.