The Case for a Context Engine at the Heart of Your Cybersecurity Business

Welcome to this week’s edition of Cyber Motion, tailored for cybersecurity business leaders. In this newsletter, you’ll find practical strategies, cutting-edge insights, and fresh thinking designed to help your security-focused brand break through a crowded market. My goal is to equip you with the tools and ideas needed to thrive amid shifting threats, buyer skepticism, and evolving industry standards.

– Tobias

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A Series B endpoint security company has a strong product, a growing sales team, and a decent pipeline. But their threat intelligence lives in Confluence, their competitive positioning is in a Google Doc three people have access to, their CRM notes are half-populated, and their onboarding docs are on a shared drive no one has opened since Q3. Their sales engineers keep the real product knowledge in their heads. Their marketing team rewrites the same messaging from scratch every quarter because no one can find the last version.

Companies like these are not short on data. They're short on context.

And that distinction is about to matter more than almost anything else they're building.

Most early-stage cybersecurity companies accumulate tools the same way: reactively. Slack for communication. Google Drive or SharePoint for documents. A CRM for pipeline. A project management tool for tasks. Email for everything else. Each tool gets adopted to solve an immediate need. No one is thinking about the system they're creating.

The result is predictable. Decisions, conversations, and institutional knowledge end up fragmented across a dozen platforms, used inconsistently across departments, and accessible only to the people who happen to know where to look.

For humans, this is exhausting. Context switching dominates the workday. Tribal knowledge becomes the default operating model. When someone leaves, critical understanding walks out with them. New hires spend weeks piecing together how things actually work, not from documentation, but from asking around.

For AI, it's crippling. Every AI tool, from a simple chat assistant to a complex autonomous agent, is only as good as the context it can access. When your company's knowledge is scattered across ten platforms in inconsistent formats, connecting it all becomes an expensive integration project. And even when you do connect it, the outputs are mediocre because the inputs are fragmented, incomplete, and poorly structured.

This is the part that most leadership teams haven't internalized yet: the bottleneck isn't AI capability. It's the quality of the context you can feed it. The models are already good enough. The question is whether your organization's knowledge is accessible enough for those models to do anything meaningful with it.

A Context Engine is the structured knowledge layer at the center of your business. It's the single environment where your team's work, decisions, documents, and data converge in a way that both humans and AI can access, understand, and act on.

This isn't a data warehouse. It's not a search tool bolted onto your existing stack. It's the operational layer where work actually happens and knowledge compounds over time. Projects, tasks, meeting notes, documents, client information, competitive intelligence, product knowledge, processes. All living in one structured, interconnected system.

The critical insight here is simple but widely overlooked: your data layer is your AI strategy.

Most companies think about AI as something you add on top of existing tools. A chatbot here. A copilot there. An automation that connects two systems. But the real unlock isn't in the AI layer. It's underneath it. A poorly structured foundation makes every AI initiative slower, more expensive, and less effective. A well-structured one turns every AI tool you adopt, today and in the future, into something dramatically more useful.

This is true for cybersecurity companies, but it's equally true across every function in the business. The marketing team that can give an AI agent access to the full history of positioning decisions, campaign performance, and competitive analysis will get categorically better output than the team whose agent is working from a half-empty Google Drive folder. The sales team whose CRM, call notes, competitive intel, and product documentation all live in one connected system will run circles around the team that has to manually assemble context before every call.

The Context Engine doesn't just serve one department. It elevates all of them.

Akshay Kothari, who leads te AI team at Notion, put this well in a recent LinkedIn post:

This is the design principle. The goal isn't to replace humans with AI agents. It's to build a layer where both can work together, on the same material, in a format that's legible to each.

Think about what this looks like in practice. A human starts a task. An agent picks up a component of it, operating on the curated context available in the system. The agent produces a draft, an analysis, or a recommendation. The human reviews it, makes a decision, and continues. Or hands the next step back to the agent. This handoff model only works when the underlying context layer is rich, structured, and shared. Without it, agents hallucinate, humans lose trust, and the whole system breaks down.

The hybrid model, humans and AI agents working in conjunction, not in replacement, is where the real productivity unlock lives. But it demands a foundation that serves both sides.

I can speak to this from experience. I started using Notion several years ago as a way to store notes and track knowledge. Over time, that evolved into a database-driven system for managing every client project, task, document, meeting, CRM record, idea, and piece of content for my fractional CMO business, and eventually for my clients as well.

I didn't plan it this way. But what I'd been building, incrementally and somewhat accidentally, was a unified context layer. When Notion AI and custom agents arrived, the stored potential unlocked in ways I hadn't imagined. Suddenly, an AI assistant could draw on years of structured work (project histories, meeting notes, strategic documents, content libraries) and produce outputs that were actually useful, because the context was already there.

The lesson isn't about any specific tool. It's about the discipline. The companies that will benefit most from AI aren't the ones with the best models or the most automation. They're the ones that have been building a rich, structured, accessible knowledge base, whether they realized that was what they were doing or not.

A Context Engine isn't a product you buy. It's a system you build. And the earlier you start building it intentionally, the more it compounds.

If you're leading an early-stage cybersecurity company, the tool decisions you're making right now aren't just operational. They're architectural. You're building the foundation your business will run on for years, and increasingly, the foundation that will determine how much value you can extract from AI.

Three questions worth putting in front of your leadership team:

The companies that treat their data layer as infrastructure, not an afterthought, will be the ones that unlock real value from AI. Not because they have better models. Because they have better context.

And in a market as competitive, fast-moving, and consolidation-prone as cybersecurity, that advantage compounds fast.

Stay sharp,
Tobias