Cory Bergman
Over the last year, we’ve witnessed a wave of experimentation as corporate security, risk and supply chain teams embark on building their own internal tools. While most don’t involve code – for example, building agentic automation through existing AI platforms – a growing number of companies are building their own software, empowered by AI.
This shift mirrors a broader trend. In McKinsey’s State of AI survey published in August, 32% of respondents said their organizations had decided against buying at least one software product or feature because they could build it internally with AI coding tools. As you might imagine, technology companies are the early adopters, but the trend extends well beyond the tech sector, including health care, financial services, professional services and manufacturing:
At Factal, we’re collaborating with a broad range of companies leveraging our verified global event data. They’re building custom workflows and visualizations, creating unified risk scores and integrating intelligence into their existing tools. Some are lone engineers tackling a side project. Others are full-fledged efforts to build centralized systems. So far, most companies are adding to their existing systems, rather than replacing them. But a handful are pursuing far more audacious projects.
There are several drivers behind it. AI has made code development faster and cheaper, and many projects can be built by a single engineer. Teams want highly-customized risk data where they already work. Some companies are hesitant to feed their proprietary data into a third-party vendor, opting to knit together internal and external data themselves. And senior leaders at many companies are encouraging departments to experiment with AI – and putting dollars behind it.
That starts to change what companies want from vendors. When software is cheaper to build, high-quality intelligence data becomes more important than ever. As AI enables more automation, data accuracy and precision are critical. Errors, false positives and noise can wreak havoc in agentic workflows, cascading through your security stack.
That’s where Factal is different. Since our founding, our focus has been producing the highest-quality verified intelligence in real time. AI detects and triages potential incidents, and our editors quickly verify them. While other vendors lean further into automation and struggle with noise, we’re expanding our team, broadening our sourcing and investing in new AI tooling. We’re publishing 23% more verified updates this year alone, and we’re getting ready to roll out Factal Advance, a forward-looking intelligence product. For teams building their own tools, our API and data integrations bring that verified intelligence straight into the systems they already use.
Good data solves one part of the problem. Keeping what you build running is another. If you happen to work at a technology company, you’re already wired to make and maintain software. But most security, risk and supply chain teams don’t have their own staff engineers. When an engineer leaves, who fixes the tool when it breaks? When data feeds change formats? When AI services move? Spinning up new tools is easier (and more fun!) than operating them over time.
The future is unlikely to be entirely build or buy. Companies will increasingly do both, building the workflows that make sense for their operations while relying on vendors for capabilities that are difficult to replicate and maintain. For security and risk teams, the opportunity is to create more tailored tools without taking on the burden of building everything from scratch. The challenge is knowing where to draw that line.
If you’re experimenting with your own tools, I’d love to hear what you’re building. Email me at cory@factal.com.
Cory Bergman is the co-founder and chief product officer at Factal. He’s a frequent speaker on how global security and risk intelligence teams can safely put AI to work. You can reach him at cory@factal.com or send him a message on Linkedin.
Photo by Randy Fath on Unsplash.
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