The Future is Now: Three Imperatives for Optimizing AI-Driven Video Monitoring

Artificial intelligence (AI) is no longer a buzzword or a “nice to have” — it’s redefining physical security. AI has gone from being viewed as a futuristic tool to a business enabler. The good news for our industry is that AI plays a tremendous role in advancing the goal of offering the best physical security solutions and strategies available on the market today.

I recently had the pleasure of participating in SDM’s webinar, Approaching and Optimizing AI as a Security Solution, alongside Shaun Pace, president of Pace Protection. Moderated by Christopher Crumley, former associate editor and current contributing writer of SDM, we explored actionable insights on how security professionals can harness the power of AI (internally and externally), how to build the right AI infrastructure, how to scale in an effective way, and much more.

Below are three of my top takeaways from this insightful discussion that I hope will help every integrator, end user, and technology partner as they consider the role AI is playing in transforming video monitoring.

AI as a Force-Multiplier: From Real-Time Detection to Predictive Insights

AI has moved well beyond object detection. Today’s AI models can distinguish a trespasser from an animal, but the real breakthrough is context: these models can correlate time of day, location, and historical patterns to decide why an object matters. In our own operations, we have seen a magnitude of improvements. This includes everything from flagging trespassers on customer sites to cutting false alerts dramatically.

These models help with internal operations as well. Rather than having a team sift through footage of incidents and draft summaries of what took place, AI can now handle most of the legwork and draft a timestamped incident summary, along with video clips, so that all stakeholders know what took place, when, and what action was taken. All this information is fed back into the model, making the solution smarter as it learns over time.

Lastly, predictive models can spot seasonal spikes or weekday vs. weekend risk, letting staff plan and take proactive crime prevention measures.

Cloud, Data, and the Edge: Building the Right Infrastructure

Setting up the right infrastructure to handle all the data that cameras collect is key. As camera counts climb into the thousands, the gravity of processing shifts to the cloud. Edge analytics still filter raw pixels, but the heavy lifting — model training, long-term storage, and cross-site correlation — now lives in Amazon Web Services, Azure, or Google Cloud. That shift forces three disciplines:

1. Treat data as a strategic asset and plan for its long-term retention and internal use. Label incidents, operator actions, and outcomes so future models learn faster.
2. Embed privacy by design — granular retention policies, role-based access, and transparent audit trails.
3. Plan the trade-off between cloud and edge early. Cloud processing and storage are higher recurring expenses but better for scalability, speed of deployment, and centralization. Edge is a higher initial capital expense but may prove to be a better cost trade-off in certain situations. It may also have other advantages, such as faster system reaction time and localized storage of confidential video information.

Organizations that carefully plan scalability, data security, and long-term trade-offs will unlock exponential value while staying on the right side of regulators and customers alike.

The Human Element

As described above, AI’s role in physical security operations cannot be understated. But neither can the role of human intervention and oversight. Ultimately, I believe physical security will remain human first. There are incredible things AI can do at a scale that humans cannot achieve on their own; however, AI is simply augmenting what we are capable of.

The first step to implement AI into your physical security solution is to identify what value you’d like the technology to provide, and at what scale. Even then, AI is not the complete solution. It’s a business enabler that takes a lot of the grunt work off human workloads, but the bottom line is providing oversight and delivering the results and levels of customer satisfaction required to do business today.

The Road Ahead

In video monitoring, AI’s trajectory is already visible: richer analytics, fewer false alarms, faster storytelling, and, ultimately, better protection for people and property. The road ahead is clear. By coupling algorithmic speed with human judgment, we can scale security to meet rising threats while preserving the personal touch our clients depend on.

To see the full discussion on this topic, please watch the on-demand webinar.