An engineer known for designing AWS security systems that protect millions of customers and whose educational content reaches over 150,000 learners, shares what resilient cloud defense really requires
Cloud environments have become more powerful and more complex, and that complexity is now one of the biggest security challenges companies face. Modern infrastructures evolve so quickly that gaps appear not from malicious intent, but from simple oversight — an unpatched service, an outdated policy, or an automated workflow that no longer reflects current behaviour. This growing visibility problem was confirmed in the 2025 State of Cloud Security Report, which found that 32% of cloud assets are in a “neglected state,” each containing an average of 115 vulnerabilities.
At Amazon, where he works as a Software Engineer in the AWS Security organization, Praveen Ravula addressed this issue by rebuilding the legacy allowlist logic. The result was a noticeable drop in false positives and more accurate threat-hunting across AWS services. He also helped create the WebThreat allowlist with added madpot detection, internal sensors that intentionally mimic vulnerable endpoints to capture early signals of malicious IP behavior. And developed Script Hunting automation, which flags suspicious scripts so engineers don’t have to review them manually. By moving security datasets closer to where they’re used, he made the system gain faster access to critical information and reduced expensive cross-region transfers. Such improvements directly support quicker and more reliable detection. Beyond his role at Amazon, he shares practical cloud-security knowledge with an audience of more than 150,000 learners.
In this interview, Praveen breaks down the architectural decisions, detection-pipeline design, and data-placement strategies that actually determine whether cloud systems stay secure at scale.
Praveen, in your experience, what are the primary challenges organizations face in keeping security logic aligned with actual system behavior in a dynamic cloud environment?
One challenge I see is fragmentation: different teams evolve their services at different speeds, and their security assumptions drift apart faster than anyone expects. Another is the quality of the signals themselves — cloud systems produce huge amounts of telemetry, but only a small portion of it reflects real behavioral change, and the rest can easily confuse detection logic. And even when organizations update their rules, rolling those changes out consistently across regions and services is harder than it sounds. Put together, these small gaps in coordination, signal clarity, and propagation create the biggest disconnect between how the system behaves and how the security logic thinks it behaves.
Your rebuild of the AEA Allowlist Policy into a workflow-based TypeScript system reduced false positives and improved detection accuracy. Why is this transition from manual logic to automated workflows so critical for cloud-native security today?
The biggest difference is that workflows let the system make decisions based on real context, not fixed assumptions. With manual rules, once the environment changes, the rule quickly becomes outdated. That’s why false positives pile up. When we moved to a workflow model, every step was clear and testable. If something behaved unexpectedly, we could adjust one part of the flow instead of rewriting the entire rule set. That made the logic more accurate almost immediately. And honestly, automation is the only way to keep up with the volume of signals we handle. It doesn’t replace engineers, but it gives us a stable framework so we can focus on the situations that actually require human judgment.
In large cloud platforms, even a small delay in accessing the right dataset can slow down detection. Your Dogfish regionalization project tackled this issue directly. How do optimizations like this affect security in hyperscale environments?
In large cloud systems, every security decision depends on how quickly you can access the right data. If that data sits in another region, even small delays add up. The detection logic still works, but it responds a little slower, and when you multiply that across millions of evaluations, the impact becomes noticeable. By moving the Dogfish datasets closer to where they’re used, we essentially removed that friction. The system didn’t have to wait for cross-region calls, and the cost drops were a natural byproduct of the same change. Faster access means faster analysis, and faster analysis means threats are identified and acted on sooner.
So even though the project looks like a performance or cost optimization on the surface, the real benefit is that it strengthens the reliability of the entire detection pipeline. Security depends on speed, and speed depends on where your data lives.
During the 2025 AWS outage, parts of the platform experienced service disruptions requiring engineering intervention. You helped resolve the incidents and received appreciation from internal customer teams for your support. What do people usually misunderstand about real-time incident response in hyperscale environments?
They imagine an incident response as a single team jumping in to fix a problem. In reality, an outage at this scale involves many systems failing or degrading in different ways, and a coordinated response depends on dozens of teams working in parallel. Each group owns a small but critical part of the picture, and progress comes from keeping those pieces aligned. Another misunderstanding is the pace. From the outside, it may look like engineers have time to analyze everything in depth, but in practice, you make decisions with the information available at that moment. The priority is to stop the impact from spreading, and then refine or correct as new data comes in. It’s controlled, but it’s not slow. People are also surprised by how structured the process is. Even under pressure, we follow strict guardrails because a rushed fix can cause more damage than the original issue. That discipline is what allows hyperscale systems to recover without creating new failures along the way.
Fast, reliable internal tools matter in everyday operations, too. Your migration from OpenAPI clients to Coral/Boto Python clients improved that reliability by cutting dependency overhead and streamlining communication. How much of cloud security depends on this kind of foundational work?
A lot more than people think. When the internal tools are slow or overly complex, every security workflow built on top of them inherits those problems. It shows up as delays in detection, inconsistent results, or extra effort from engineers just to keep things running.
The migration to Coral/Boto clients was a good example of how a small technical change can have a broad impact. Once the clients became lighter and more reliable, everything upstream became easier to reason about. We spent less time dealing with dependency issues and more time improving the actual security logic. Security work often focuses on threats, but the foundation underneath that work determines how quickly and accurately you can respond. Clean, efficient systems don’t eliminate risk, but they remove friction. And that makes every layer of security more effective.
Much of that foundational work is hard to see unless you’ve dealt with it. You built an educational community of 150,000+ learners focused on cybersecurity and AWS threat mitigation. Why do so many developers still find cloud security fundamentals difficult?
A big part of the difficulty is that cloud security is actually a combination of understanding identity, networking, automation, and how different services interact. Developers often learn these pieces separately, but in real systems, they all overlap, and that’s where the confusion begins. Another challenge is that many people try to apply on-prem thinking to the cloud. They look for fixed boundaries or predictable traffic patterns, and those assumptions don’t hold up in a distributed environment. When the mental model is off, even straightforward concepts feel complicated. Also, a lot of the important work in cloud security happens behind the scenes. Developers don’t always see how detection pipelines function or why certain decisions are made, so they underestimate the amount of context involved. Once they understand how the pieces fit together, the fundamentals start to make more sense. That’s what I try to cover in my educational content.
Given your hands-on experience, where do you think cloud security is heading by 2030? Will AI-driven detection models replace manual engineering, or will human-designed workflows remain essential?
AI will definitely take on a larger role, especially in spotting patterns that are hard for humans to see and processing the huge amounts of data modern systems generate. But I don’t think it will replace the core engineering work. The hardest decisions in security still come down to judgment: understanding what should be blocked automatically, when to slow down and ask for human review, or how much risk is acceptable in a specific situation.
Some teams may start to over-trust AI and treat it as a complete solution. That can lead to a false sense of security. If the underlying logic isn’t designed carefully, or if the model behaves unpredictably, the consequences at cloud scale can be serious. We still need engineers who understand the systems well enough to notice when something “looks wrong,” even if the model says it’s fine.
So in the future, I expect a hybrid model: AI will surface insights quickly, workflows will handle many of the routine decisions, and engineers will focus on shaping the frameworks and guardrails that keep everything safe. Automation will grow, but the responsibility will still sit with people who can interpret context and make the difficult calls. That mix — not full automation — is what will make cloud security stronger.
