By Jeremy Ventura, Field CISO, Myriad360
Picture your typical Monday morning Starbucks, complete with baristas, impatient customers, laptop lurkers, and… a clown? Face paint, red nose, giant shoes—immediately, you know something’s off. It doesn’t have to be malicious; it’s just out of place. I had a CIA instructor once sum that up in a three-step model: Baseline, Anomaly, Decide—BAD for short. If you don’t know what “normal” looks like, you’ll never flag the true oddities. That simple principle has become one of my favorite ways to cut through the noise of modern cybersecurity.
Early in my career, I focused heavily on static signatures and exhaustive rule sets. But threat actors learned to tweak indicators just enough to skirt those rules. Signatures would grow stale, leaving me to chase false alarms while real threats slipped by. Then I sat in on this training session where the instructor walked us through raw CCTV footage of the Boston Marathon bombing. Hundreds of people in one snapshot, but the bomber was the only individual facing a different direction from everyone else—like the clown in Starbucks. It illustrated how powerful a single outlier can be if you train yourself to spot it in context. That’s the essence of BAD: identify your normal, notice what deviates, and then decide if you’re looking at something harmless or hostile.
Baseline: Know Your Normal or Get Breached
A baseline is not just logs or network maps. It’s the entire sense of how your environment operates in day-to-day conditions—peak traffic hours, typical login patterns, normal resource usage. I’ve walked into organizations that had thousands of “dormant” user accounts floating around or big cloud storage buckets left open to the internet for years. No one realized these configurations were bizarre because they never established what “normal” truly was.
It reminds me of the 2015 Office of Personnel Management (OPM) breach, where attackers compromised sensitive data from over 21.5 million federal employees. OPM had no comprehensive inventory of its own servers, databases, or network devices. This lack of a security baseline meant vulnerabilities went undetected for over a year, allowing attackers to move freely through the network. The breach underscores a critical lesson: if you don’t define normal, you won’t notice when something is dangerously off.
Once a baseline is defined, patterns emerge. If finance normally logs in between 9 and 6, an admin account suddenly logging in at 2 a.m. is your clown moment. But you can’t label something “odd” unless you know what ordinary looks like. That’s why I tell teams: your baseline is your biggest ally. It’s not fancy, it’s not hyped like AI, but it’s fundamental to everything else.
Anomaly: Attackers Don’t Follow the Rules
In the old signature-based mindset, an anomaly was whatever matched a “known bad” pattern. But attackers don’t always follow a known pattern. They use new domain names, novel infiltration paths, and random times to strike. So if you rely solely on references to a static dictionary of threats, you miss the Boston Marathon suspect turning the wrong way in the crowd.
One prime example is the SideWinder APT group, which specifically alters malware indicators to bypass signature-based defenses. In a campaign targeting officials in Pakistan and Turkey, SideWinder used polymorphic techniques to modify the appearance of their malware with each iteration, slipping through signature-based antivirus tools undetected. Their ability to disguise malicious payloads within seemingly benign documents demonstrates why behavioral anomaly detection is essential.
I’ve seen organizations hammered by stealthy exfiltration simply because the data movement didn’t trigger any signature-based rule. It was an odd spike in volume or a strange time of day for an outbound connection, but nobody spotted it. We can blame “alert fatigue,” but the deeper issue is they never said, “Hey, typically we see data egress on these five endpoints, so data pouring out of a marketing server is suspicious.” A big part of anomaly detection is resisting the impulse to blow up every little quirk—some outliers are benign. But if it’s weird and potentially damaging, it deserves quick triage.
Decide: Make the Right Call Before It Costs You
Let’s say you do see a clown: maybe an odd spike on a database. Is it a patch process that IT forgot to mention? Or is it an intruder siphoning private data? That’s where Decide comes in. Decide means analyzing how critical that resource is, how plausible a threat scenario might be, and what immediate actions you can afford to take.
I’ve been in war rooms where we learned the hard way that a “critical CVSS 8.4” vulnerability on an unused server was less urgent than a “7.5” on a revenue-generating e-commerce platform. That’s the difference between raw severity labels and true business impact.
Overreacting to an anomaly can be just as damaging as ignoring one. Shutting down the wrong system in response to a misidentified threat can cost millions in downtime and reputational damage. A study by IBM found that 98% of organizations experience costs exceeding $100,000 per hour of downtime, with 33% reporting losses between $1-5 million per hour.
I still remember an internal debate at a company that discovered a puzzling new login pattern. One side said, “Let’s isolate the system immediately!” The other side said, “We need more data.” We ended up deciding to segment the server, gather deeper logs, and keep a single read-only connection open for continued monitoring. BAD gave us a practical roadmap: we saw the baseline, found an anomaly, and then made a reasoned choice about next steps—without succumbing to blind panic or endless analysis.
The ROI of Proactive Anomaly Detection
When you treat BAD like a guiding philosophy rather than just an acronym, it transforms how your entire team thinks about threats. I’ve seen teams automate the first two steps, so the instant something breaks baseline—like an authentication request from a region never used before—an alert fires off. Then a human decides if it’s a false positive or an urgent event.
Automation isn’t just a convenience—it’s a force multiplier. A study titled "That Escalated Quickly: An ML Framework for Alert Prioritization" found that implementing machine learning frameworks for alert prioritization can:
Reduce response time by 22.9% Suppress 54% of false positives Maintain a 95.1% detection rate.Attackers love chaotic surfaces: sprawling SaaS deployments, random misconfigurations, or employees plugging in rogue devices. But if your baseline is strong, anomalies jump out like a clown suit in a sea of business-casual Starbucks patrons. Some will be trivial. Others will signal a breach in progress. The key is not just labeling them weird, but deciding how to act—fast. Because once you confirm a real threat, every minute counts.
In a world drowning in data, Baseline, Anomaly, Decide (BAD) cuts to the chase. It offers a human-centric way to detect the one malicious log entry in a million, the one reversed domain name that stands out, or the single malicious user reactivating a dormant account at midnight. BAD roots us in the fundamentals: define your normal, spot what’s off, and pick a course of action—it’s a disciplined approach to noticing the clown in the room before he does any damage.