ExtraMile by SecureITWorld is a top-class interview series focused on the dynamic cybersecurity space. Here we feature cybersecurity experts, business tycoons, and innovators who share their leadership viewpoints and insights on security operations.
In this insightful session, we’re beyond excited to have Pete Martin, Co-Founder and CEO of Realm.Security joined us. Headquartered in Boston, Realm.Security is redefining how security teams manage and route security data through its AI-native Security Data Platform.
With over 15 years of experience, Pete holds an excellent track record of driving growth and innovation across the security ecosystem. Under his leadership, Realm.Security has developed an AI-native security data pipeline that helps businesses optimize the value of their security data, streamlines operations, and is simple to deploy.
In this conversation, Pete shares valuable insights about his leadership, the challenges security teams face, and the difference between the cybersecurity market and traditional GTM. He further highlights Realm.Security’s availability in Azure, and the future of an AI-native data security pipeline.
Welcome, Pete, welcome to today’s conversation!
1. Starting as a Business Development Representative at Rapid7 and now heading as Co-founder and CEO of Realm.Security, your career journey is impressive. What leadership principles have guided and kept you focused along the way?
Pete. My career has been anything but linear, which I think shaped how I lead today. I started as a BDR at Rapid7 in 2011, worked my way up through sales, managed teams at Carbon Black through their IPO, and eventually became VP of Revenue at Oort before co-founding Realm. Each role taught me something different about what actually moves the needle in this industry.
A few principles have stayed consistent throughout. The first is personal brand. Early in my career I made a point of ensuring anyone I worked with knew I gave everything I had, was willing to admit when I was wrong, and wanted to be a great teammate. That reputation compounded over time. My co-founder Jeff reached out to start Realm partly because of the relationship we'd built years earlier, and our first investor was a former CEO I'd worked for. Your current role is temporary. The impression you leave on people isn't.
The second is treating yourself as an asset that compounds with diverse experiences. Every time I took a role that made me uncomfortable or seemed like a strategic move, I came out with a broader perspective. Going from individual contributor to sales manager to VP of Revenue to CEO gave me a 360-degree view of what it takes to build and scale a business. I tell people: think two or three jobs ahead when you're in your current role.
2. What is your biggest motivation behind co-founding Realm.Security? What setbacks did you see in the cybersecurity data landscape?
Pete. When my co-founders and I decided to start a company, we actually set out to solve a different problem entirely - Tier 1 SOC automation using AI for faster incident triage. The market signals weren't encouraging. Prospects validated the problem but the economics of the solution are hard to justify. Strategically, AI-SOC looked like it would become a feature of SOAR and SIEM platforms rather than its own category, and with a low barrier to entry, we anticipated a saturated market of 50+ vendors within a few years.
What kept surfacing underneath all of that was a bigger, more fundamental problem: broken security data infrastructure. The economics of SIEMs were forcing security teams into impossible trade-offs on what data goes in and what stays out. AI models need complete data sets to work effectively. You can't build intelligent security operations on an incomplete foundation. Three weeks before officially forming Realm, we scrapped the original idea and started over. That decision turned out to be the right one.
3. From your point of view, what are the key differences between successful cybersecurity go-to-market strategies and traditional software GTM approaches?
Pete. The cybersecurity industry did a really good job for a long time of scaring people into buying things. Fear-based marketing worked until customers started realizing there was a significant gap between what was promised in a slide deck versus what actually happened in production. Now CISOs are weary. They assume the real-world result will be half as valuable because that's been their experience.
That changes everything about how you approach GTM in security. What works now is being ruthlessly specific about what you do and don't solve. Setting expectations upfront. Being willing to tell a prospect "this isn't for you right now", because if you're honest with them, they'll pick up the phone when you call back next year. If you oversell, you're done. Authenticity and directness aren't just nice-to-haves in this market. They're the only way through the noise.
The other thing I'd emphasize is self-education for anyone on the GTM side. You can give a sales team a script, but if they stop there it's going to feel canned. You have to have your own point of view. That means reading the cybersecurity trades, following the key influencers, and understanding the evolution of the markets you're selling into. The ecosystem moves fast. The best GTM people treat themselves like entrepreneurs. They understand the market they're in, not just the product they're selling.
4. Could you talk about the challenges security teams face today in managing high log volumes without losing data fidelity? How does Realm.Security address them?
Pete. Security teams are caught in an impossible trade-off. Modern environments generate enormous volumes of telemetry from EDRs, cloud providers, identity systems, SaaS applications, and networks. And this is only increasing with telemetry from AI tools and agents. As a result, SIEMs, which were built as the central hub for security analysis, have become prohibitively expensive at that scale. So teams are forced to decide what data goes in and what gets left out.
Realm addresses this with our AI-native Security Data Pipeline Platform, which simplifies the collection, normalization, and routing of security data. Using machine learning and LLM’s trained on frameworks like MITRE ATT&CK we route important telemetry data to the right destination while filtering out unnecessary logs. In addition to saving customers six-figures in data ingestion costs, this also accelerates incident response by eliminating non-security-relevant data, enabling security teams to reduce MTTR by 40%+.
5. Congratulations on your recognition within the New England Tech and VC ecosystem impact! How does it impact Realm.Security’s growth and innovation?
Pete. Boston, and more broadly New England, has a uniquely strong cybersecurity ecosystem, and being recognized as the Emerging Cybersecurity Company of the Year by the New England Venture Capital Association is a huge validation of the work we’re doing.
If you can make it as a cybersecurity company in Boston, you can make it anywhere. The city has produced some of the most important cybersecurity companies in the world, and we're proud to be building here.
That said, recognition like this is a milestone, not a destination. What it really does is validate that the problem we're solving resonates. Security teams are drowning in data noise and facing spiraling storage costs, and the market knows it needs a solution. We raised $22M in 15 months, grew headcount by 250% in 2025, and had customers like Vensure Employer Solutions cut firewall log volumes by 83%, saving $250,000 annually. That momentum is what drives us.
Looking ahead, our ambitions extend well beyond New England and North America. The recent strategic investment from Presidio Ventures is opening doors to the Asia-Pacific market, which is now the third-largest cybersecurity market globally. Boston will always be home base, but we're excited to bring what we're building here to a global stage.
6. In today’s AI-driven security operations, what does “good data” truly mean? What are the key factors that make high-quality and actionable data from noise?
Pete. Good data for AI is fundamentally different from good data for human analysts, and that distinction trips up most organizations. Data that works perfectly for human analysts running reports and investigations can be completely unusable for machine learning. For AI agents to work effectively in security operations, data needs three properties.
First, it needs to be accessible. Agents require low-latency, permissioned access to a complete body of evidence. If resolving an alert requires querying six different APIs, each with rate limits and custom authorization, the agent burns its compute budget on data plumbing instead of analysis.
Second, it needs to be normalized. A common schema across all sources eliminates the constant translation tax of re-learning vendor-specific formats. An agent that understands standardized fields makes faster, more reliable decisions.
Third, it needs to be clean and enriched – errors corrected, context added, metadata included so the agent has everything it needs to make confident decisions.
If your telemetry is fragmented, your schemas are inconsistent, or your context is missing, you won't get faster responses from AI. You'll just get faster mistakes.
7. Could you share insights on Realm.Security’s availability in the Microsoft Azure Marketplace and how this allows customers to access its AI-native security data pipeline directly within Azure?
Pete. We’re beyond excited to meet security teams where they already are. Being available in the Azure Marketplace removes the procurement friction that typically slows down how organizations get started with new vendors. They can initialize access to our platform directly through an environment they're already working in.
As a result, security teams dealing with spiraling SIEM costs and data sprawl can start addressing those problems faster, without changing their downstream tools or navigating a lengthy procurement process. For teams that are already stretched thin, removing that kind of friction matters.
8. Moving ahead, what does the future of an AI-native cybersecurity platform look like? How do you expect them to shape the industry?
Pete. The future is one where AI agents handle the volume problem that has overwhelmed human analysts for years – the alert floods, the Tier 1 triage, the routine enrichment work - freeing humans to focus on the high-judgment decisions that actually require expertise. That's not a distant vision. It's happening now, with 38% of organizations planning to deploy AI agents in their SOC over the next year.
But the platforms that win won't be the ones with the most sophisticated AI models. They'll be the ones that solved the data problem first. AI agents are only as good as the data they're fed. The organizations that invest in clean, normalized, accessible data infrastructure before deploying agents will see dramatically better outcomes than those bolting AI onto broken foundations.
The other critical piece is governance and observability. As AI agents gain more autonomy in security operations – making decisions, taking actions, accessing sensitive systems – the ability to audit what they did and why becomes non-negotiable. Regulators are paying attention. CISOs need defensible audit trails. The platforms that build trust through transparency will define the category. The ones that deploy powerful but opaque agents will create new risks faster than they solve old ones.
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