AI in Security, Prediction vs. Preparation, the No Bell podcast and More.
Profiles in Preparedness #52
Welcome back to The CP Journal, where we break down what it takes to get left of bang.
You can’t prevent an attack you never see coming.
For a long time, that limitation defined the largest and most significant constraint on threat prevention programs. Organizations were almost entirely dependent on people being in the right place, at the right moment, looking in the right direction, and trained to notice the right things if they wanted to proactively recognize threats. When something in that chain of requirements broke, the opportunity to intervene often broke with it.
Today, that constraint is changing.
As I wrote about in this week’s article, Seeing the Threat Sooner: Getting Left of Bang with AI, advances in camera-based gun detection systems are expanding what organizations are able to see and how early they can see it. In doing so, they shift security cameras from tools that primarily document what already happened into resources that actively support prevention. These systems don’t get tired, distracted, or pulled away by competing demands, and they aren’t constrained by having to choose which camera feed deserves attention in any given moment.
To me, the significance of these advancements lies in how they expand an organization’s situational awareness and bring critical information forward in time.
But there’s a reality that often gets missed in conversations about any new technology. While these tools dramatically improve detection, they also concentrate responsibility for downstream actions. The moment a notification appears, the organization—the school, the office building, the venue, the community—is still responsible for deciding whether that signal is important, what they will do about it, and taking action.
The organizations that will benefit most from these systems are those that treat the technology as a force multiplier and recognize what also needs to change in their security and violence-prevention programs as a result of the new technology. They will need to adapt plans, clarify decision authority, train for compressed timelines, and ensure that the people receiving alerts are prepared to translate the notification into informed action. Without those corresponding improvements, even the best detection capability risks becoming noise—or worse, hesitation.
The good news is that the challenges this creates are knowable and manageable. The same four steps that have always defined getting left of bang still apply, even if they are being performed by different groups.
I unpack this in more detail in the article, and I’ll be going even deeper in a webinar I’m presenting with ZeroEyes in January. We’ll focus less on the technology itself and more on how organizations can effectively integrate early detection capabilities into real decision-making systems and take steps to get further left of bang.
If preventing the next attack depends on recognizing threats and acting on that information, this is a conversation worth joining.
Inside The CP Journal
Here are some of the articles that were added to the site this week.
The introduction of AI into security operations doesn’t just change how much an organization can see. It may also change how decisions are made once something is seen.
This article looks at how AI fits into the four steps needed to get left of bang and how violence-prevention capabilities will need to adapt to keep pace.
Podcast: Episode with the No Bell Podcast
I had a chance to chat with Sam Alaimo, co-founder of ZeroEyes, former Navy SEAL, and writer of the amazing What then? newsletter to talk about Left of Bang.
This Week‘s Reads
Here are a few standout reads from this week with insights, ideas, and perspectives that caught my attention.
Article | High Intensity Urban Combat Part 1: How is it Different? “How must urban combat tactics change if the United States and its allies find themselves in a high-intensity conventional war against a modern, combined arms force like Russia, China or North Korea? While many tactics and general principles will remain the same, there are some very important differences between high-intensity conventional urban combat and the sort of precision Close Quarters Battle (CQB) that the U.S. military adopted while fighting low-intensity conflicts in Iraq and Afghanistan.” This article was uniquely written as it offers a balanced view of possibilities instead of absolute statements or guaranteed predictions. Just things to consider, stated assumptions, and something to generate ideas from—a list of characteristics for articles not as common as they once were.
Article | Prediction vs. Preparation. “Prediction is about trying to be right while preparation is about setting the right expectations. And investing has a lot more to do with setting reasonable expectations than being right all the time because it’s hard to be right in the markets.” I appreciated this breakdown about the the distinctions between prediction and preparation, written for investments, but directly applicable to many fields.
Article | African freelancers behind anti-US and anti-French disinfo campaigns. How do disinformation networks work? Here is a break down about how a network of freelancers, hired via job-seeking platforms like Upwork, work to spread disinformation. Operating independently, likely coordinated through centralized tasking, using a variety of text obfuscation methods, and mimicking legitimate news outlines to push a narrative. Understanding the methods can help to identify when it’s being used.
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The distinction between prediction and preparation cuts deep when thinking about threat detection. AI shifts the bottleneck from noticing threats to decidng what to do once detected, which actually raises harder questions about authority and response protocols. I've seen situations where better detection just meant faster paralysis because nobody prepped for compressed timelines. The freelancer disinfo example shows the same gap between spotting patterns and actually distrupting operations.