Securing critical institutions with AI

Method Security was founded to deliver cyber resilience to critical institutions including financial services firms, government agencies, and operators of critical infrastructure. As cyber conflict intensifies, many of these organizations still rely on tools designed for compliance rather than real-world threat detection and response. Method takes a different approach by building a platform that combines defensive and offensive security capabilities with AI-driven automation.

The platform is designed for operators on the front lines including CISOs, security engineers, and teams protecting critical infrastructure, financial systems, and government environments. These are high-stakes environments where performance, reliability, and security must be guaranteed at all times. “Our customers are dealing with real adversaries, not theoretical risks,” says Sean Hacker, Co-Founder and CTO of Method Security. “They need systems they can trust completely, and that means we have to deliver both speed and reliability.”

This responsibility extends to how Method approaches AI. The team emphasizes transparency, strict rules of engagement, and ethical use. “AI expands what security teams can do, but it also raises the stakes,” Hacker explains. “You need deep visibility into your systems to use it safely and effectively.”

“Our customers are dealing with real adversaries, not theoretical risks. They need systems they can trust completely, and that means we have to deliver both speed and reliability.”

From the start, Method embedded observability into its engineering practices. Every service, system, and data flow was instrumented early, giving the team complete visibility into how the platform behaves. “We built with observability from day one,” says Hacker. “That gave us 100% coverage across our systems, which is critical in the environments we operate in.”

Scaling quickly while maintaining control

As a young company building AI-driven security systems, Method needed to move quickly without sacrificing control. With a small, highly selective team, there was no room for fragmented tooling or unclear ownership. Engineers work closely with customers and are expected to operate with a high degree of accountability, which makes shared visibility essential.

Datadog provided that foundation through the Datadog for Startups program. With minimal setup, the team was able to instrument their platform immediately and begin building with full visibility in place. “The Startups program was a force multiplier for us,” says Hacker. “We had a very small team and a very large mission. Datadog gave us enterprise-grade observability from day one without slowing us down.”

That early investment shaped how the company operates. Instead of retrofitting observability later, it became part of the system and the culture. Engineers could instrument freely, learn from real data, and make better decisions as the architecture evolved. “We didn’t have to guess or limit what we instrumented,” Hacker explains. “We could see everything, which meant we could move faster with confidence.”

“The Startups program was a force multiplier for us. We had a very small team and a very large mission. Datadog gave us enterprise-grade observability from day one without slowing us down.”

Because Datadog was adopted early and used across teams, it quickly became a shared standard. Today, it is used across both engineering and security. “We have 100% adoption across the team,” says Hacker. “Everyone is working from the same data, which makes collaboration faster and decisions much clearer.”

End-to-end visibility across systems and environments

Today, Datadog is the central platform Method uses to monitor its systems across a multi-language, event-driven architecture. Their platform spans services written in Java, Python, Go, and Node.js, with complex data flows across cloud and hybrid environments.

With Application Performance Monitoring and Data Streams Monitoring, engineers can trace requests end-to-end across services, queues, and databases. This helps engineers understand how systems interact and where issues originate. “Datadog gives us a complete picture of how our system behaves,” says Hacker. “When something slows down, we can immediately see where it is happening and why.”

That visibility has a direct impact on incident response. Instead of piecing together signals from different tools, engineers can quickly isolate issues and resolve them. “We can go from detecting an issue to understanding the root cause in minutes,” Hacker says. “That has significantly improved our MTTR.”

Additional capabilities like Continuous Profiler and Real User Monitoring help the team optimize performance and validate user experience across the stack. Method uses RUM to test feature-flagged releases in development environments, replacing anecdotal feedback with real user data and identifying issues before they reach production. Continuous Profiler, combined with proactive alerts, helps engineers quickly pinpoint inefficiencies and focus optimization efforts where they matter most.

Method also supports customers with strict infrastructure requirements, including government deployments, and needs to maintain consistent visibility across environments without added complexity. “Datadog’s commitment toward its FedRAMP High authorization was critical for us,” says Hacker. “It’s allowed us to support government customers using the same platform we use everywhere else.”

By avoiding separate tooling for commercial and regulated environments, Method maintains a single, consistent operating model across its customer base, improving both efficiency and reliability.

Unifying security, efficiency, and growth

Security is tightly integrated into Method’s platform and workflows. With Datadog Cloud SIEM and Cloud Security Management, the team has a centralized view of activity across its infrastructure, with the context needed to investigate and respond quickly. “Datadog’s Cloud SIEM gives us a single place to monitor and investigate security signals across our environment,” says Hacker. “We can correlate events and respond quickly with the full context we need.”

This shared visibility across engineering and security teams improves collaboration and reduces time to resolution. It also reinforces Method’s focus on transparency and control, which is essential for building trust with customers in high-risk environments.

“Datadog's Cloud SIEM gives us a single place to monitor and investigate security signals across our environment. We can correlate events and respond quickly with the full context we need.”

Method also prioritizes operating efficiently as it scales. By using Cloud Cost Management, the team gains visibility into usage across cloud providers and Kubernetes environments, helping identify inefficiencies and optimize spend. “We can see exactly how resources are being used and make smarter decisions,” Hacker explains. “That helps us stay lean while still delivering high performance.”

These capabilities support Method’s culture as much as its technology. The company prioritizes building a small, high-performing team where engineers are empowered to move quickly and take ownership of outcomes. Having a shared platform for observability and security gives them the context they need to operate at that level. “Having everything in one place makes us faster, but it also makes us better,” says Hacker.

“Having everything in one place makes us faster, but it also makes us better.”

Looking ahead, Method is expanding its use of AI-driven observability, using Datadog’s AI capabilities to automate triage and enrich alerts with meaningful context before engineers engage. “We are moving toward systems that can surface issues and provide meaningful context before an engineer even looks at them,” Hacker says.

Ultimately, Method Security is building a new standard for cyber resilience in environments where failure is not an option.