Meet Jnana Tekumudi, Security Analyst
As an Email Security Analyst on the First Responders Team, Jnana Tekumudi has made a habit of asking the deeper questions—ones that uncover patterns, strengthen detections, and stop attacks before they spread. Her instinct to zoom out, automate what slows teams down, and lean into AI shapes how she protects Abnormal’s customers every day.
January 12, 2026
Connecting Patterns
Some analysts start by learning patterns. Jnana Tekumudi started by questioning them. As she worked through complex email escalations, she kept noticing that certain attacks felt familiar even when they didn’t look identical. That instinct to zoom out and ask why became the foundation of her most impactful work at Abnormal.
For weeks, she fixed one-off cases only to see near-copies reappear — slightly tweaked subject lines, payloads moved into attachments, subtle variations of the same tactic. One day, while reviewing several escalations side-by-side, the pattern finally surfaced.
“I traced a few separate escalations and saw that they were basically variations of the same underlying behaviour. That’s when it clicked that looking at a single message at a time was too narrow.”
From there, her work changed. Instead of treating each escalation as an isolated problem, she began comparing early signals across customers, identifying shared behavioral patterns before campaigns grew.
“Because I caught those signals early, I was able to create detections before the attacks fully ramped up… catching thousands of attack messages a week.”
As her detections gained coverage, she experienced one of her proudest moments: contributing to 141,010 uniquely flagged messages in a single month, leading all of Abnormal.
Bonusly — the company’s real-time recognition tool where employees can reward each other for their great work — amplified that moment even more. It wasn’t just a metric; it was her teammates acknowledging the impact of her work as it happened. For Jnana, that recognition made the accomplishment feel deeply meaningful.
Changing Hours into Minutes
As Jnana refined her pattern-based approach, she spotted something else slowing analysts down: hours spent manually collecting the same behavioral signals across large campaigns. In escalations with hundreds of messages, identifying patterns could take nearly a full day.
So she built a tool to change that.
It automatically pulls the key signals analysts typically gather by hand, visualizes them, and highlights the overlaps that matter most. What used to take hours now takes minutes.
“For campaigns with 100+ messages, what used to take four hours to a full day now takes under five minutes to generate insights.”
That time savings adds up fast across weekly escalations. More importantly, it gives analysts room to focus on higher-value work: strengthening detections and protecting customers. Seeing teammates reach for the tool instantly is now one of her favorite parts of the job.
“It genuinely makes their work easier… knowing something I built removes friction is really motivating.”
AI as a Daily Multiplier
AI isn’t something Jnana uses occasionally — it threads through her day. She relies on LLMs and internally built AI tools to summarize long message threads, surface unusual behavior, and prevent critical details from getting buried in the noise.
One example stood out: a complex vendor fraud thread where the conversation spanned multiple replies and subtle shifts. An AI-generated summary immediately highlighted unusual behavior she may have otherwise had to track manually. It was faster, clearer, and reduced the risk of missing something important.
Because experimentation is encouraged at Abnormal, she treats AI less like a tool and more like a partner. Prototyping small ideas, gathering feedback, iterating — that’s the norm, not an exception.
Finding Meaning in the Metrics
For Jnana, the real impact shows up when a detection she built stops a campaign before it spreads.
“Any time a detection I’ve created stops a large campaign before it hits more customers is a reminder of why the work matters.”
She also values how people at Abnormal recognize each other’s work. Contributing to 141,010 uniquely flagged messages in a single month was a milestone — but the part that stayed with her was the real-time acknowledgment from teammates who saw the work and appreciated its impact.
Those moments show her how individual contributions compound into stronger protection for customers. They also reinforce the mindset she’s grown into: proactive, curious, and always willing to ask why.
A Mindset That Keeps Evolving
Over time, Jnana has shifted from solving individual problems to anticipating entire categories of them. She’s more experimental, more comfortable exploring new tools, and quicker to question assumptions.
Her advice to new analysts reflects that evolution:
“Stay curious and don’t just stop at the obvious details. Ask ‘why’ a lot and don’t hesitate to experiment with tools or ideas that you think will be helpful.”
It’s a mindset that helps her stay ahead of fast-moving threats — and one that continues to shape the team around her.
