AI and Machine Learning in Security: Building Smarter, Integrated Systems for a Safer Future

Introduction – The Convergence of Intelligence and Security

For decades, the smartest security systems were simple, discrete analog systems whose brains wore uniforms. Typically, a security officer, guided by training and experience, would monitor a screen, interpret a scene change or alarm, and correlate information between two or more systems to decide whether to act and, if so, how. The system’s intelligence was based on human judgment: the ability to read context, weigh risk, and follow the correct procedure at the right moment. Today, in many ways, that remains the benchmark for what we call a “smart,” or Artificial Intelligence (AI), security system.

The drawbacks to human-only system operations are numerous: fatigue, inconsistency, cost, and limited reliability, among others. And while human supervision and intervention in AI-enabled systems are still necessary, that necessity is decreasing, and in no small part due to the machine learning (ML) function.

In both physical and IT/Cybersecurity domains, two goals of AI-based systems are to improve our situational awareness and to detect, decide, and initiate action quickly and accurately while continuously learning from every interaction. At the core of intelligent security technologies lies machine learning. It’s the mechanism that an AI-based system uses to improve with experience, recognizing complex patterns, adapting to new contexts, and refining its decision-making through parameter adjustment and up-training without altering its core program logic. In practical terms, it’s what transforms simplistic, preprogrammed logic into adaptive intelligence.

Every “smart” video platform, access control system, or situational awareness application develops better capabilities through continual exposure to data and the feedback that validates its judgments. Each detection confirmed, dismissed, or corrected by an operator becomes part of a learning loop that sharpens accuracy and context awareness over time. Whether built directly into the platform or added as a third-party analytic layer, AI’s “true intelligence”[1] emerges only when machine learning is reinforced by consistent, real-world feedback.

Seeing More, Seeing Smarter: ML in Video Analytics and Anomaly Detection

In legacy analog and many digital systems, it’s people who have provided the intelligence to spot the unusual amid the routine. But attention and vigilance fade, and fatigue dulls performance. AI-based security systems (especially video) can now carry out those tasks while continuously improving their accuracy, learning from new data, and improving performance, literally with every frame analyzed.[2].

In the physical domain, AI machine learning transforms video analytics from previously simple, rules-based scene modeling (e.g., ObjectVideo’s 1st Generation VEW) to context-aware analytics based on convolutional neural networks (CNNs), as seen in current AI-technology platforms such as Ambient.ai, AXIS, and BriefCam (to name a few). Modern systems can interpret both behavioral and scene cues, including crowding and dwell time, specific body actions and behaviors, and subtle contextual shifts that might indicate stress, intent, or risk.

And, as proposed by Don Morron of HighlandTech, when paired with multi-agent architectures, purpose-built AI agents that coordinate across networks of cameras, security and non-security sensors and analytic nodes, each device becomes part of an interconnected platform that improves situational awareness, accelerates threat evaluation, and streamlines a response. And edge analytics pushes that intelligence processing closer to the data source, allowing a camera to flag a loitering vehicle or an abandoned object in near real time, long before an operator might notice. Whether edge-based or centralized, the result is measurable: faster detection, fewer false-positive alarms, and reduced operator fatigue.

It’s the emergence of the concept of the “neural network of the built environment”, where artificial neural networks are applied to analyze, model, and optimize elements of the built environment, including buildings, neighborhoods, and urban infrastructure. The same type of adaptive AI is also found in the cyber domain and can improve cross-domain cyber/physical security functionality. AI/ML models can ingest network telemetry to identify unusual login patterns, traffic anomalies, or subtle exfiltration attempts. 

Increasingly, physical security system data is being integrated into IT SIEM and SOAR platforms, unifying physical and IT security information into a single operational picture. For instance, a notable Cloud-based security systems manufacturer now integrates camera and access-control data directly into enterprise SIEMs like Splunk and NetWitness. A suspicious badge event may trigger a network scan; a cyber alert may cue cameras to a specific zone. The boundary between physical and cyber situational awareness is dissolving, giving rise to an integrated system of adaptive capabilities and more context-rich situational intelligence.

Machine learning can exacerbate this risk because its confidence is statistical rather than contextual. A model may assign a high probability to an event simply because it has observed similar patterns in past data, rather than because it understands the situational nuance. Without human oversight, those statistical “certainties” will become operational blind spots. The safeguard is not less automation, but rather human oversight, where SOC operators are trained to recognize when the machine’s learning precision exceeds its situational understanding. The operator can manually overrule an automated decision when the statistical confidence doesn't align with the real-world operational context.

At its best, the partnership between an operator and AI systems expands perception and situational awareness rather than replacing it. AI/ML excels at scale, potentially analyzing hundreds to thousands of events in seconds and recognizing patterns that no person could perceive unassisted. On the other hand, we humans excel at nuance, reading intent, motive, and consequence. Together they form a layered defense that is scalable, extensible, and self-correcting. The future of operational security lies in integrating this synergy: machines that learn from human oversight and humans who learn to question machine logic and behavior critically.

Practical Guidance for End Users

AI/ML for security isn’t a turnkey technology; it’s an evolving connection between data, algorithms, and human judgment. For CSOs and security directors, the challenge isn’t merely whether to deploy AI/ML, but also how to do so responsibly and effectively. When evaluating AI/ML-enabled solutions, a crucial first step is to start with data provenance. Where does the training data come from, how representative is it of your operating environment, and how is it refreshed over time? Models trained on narrow or outdated datasets tend to perform well in controlled demonstrations but can fail when exposed to real-world variability. Therefore, ask AI/ML manufacturers for model transparency. What features drive decisions, how are confidence scores generated, and what mechanisms exist for auditability and override?

Equally important are the update cycles. Machine learning models, trained on historical data, tend to degrade over time if not retrained to reflect new conditions, a phenomenon known as model drift. Manufacturers should be able to explain their refresh intervals and the human validation process. A strong human-in-the-loop framework ensures that the model’s “learning” remains aligned with the organization's ethics, context, and operational expectations.

When pilot-testing a new AI security platform, we recommend following a structured pilot / calibrate/ measure / expand approach. Begin with a narrow use case (e.g., anomaly detection in access control events), gather metrics on accuracy and false alarm rates, and then calibrate parameters before scaling deployment. Establish baselines for performance and operator workload, then measure both technical precision and human usability. Engaging the vendor’s participation in this process is highly recommended.

Implementation and operationalized success require cross-functional collaboration. Physical and InfoSec, IT, and legal teams (among others) must work together to define acceptable risk boundaries and ensure that security data sources remain governed and auditable. This is intended to prevent a common pitfall: ML projects owned entirely by IT or an implementation vendor, with little input from the practitioners who will rely on them when implemented.

Finally, integrators and consultants have an expanded role to play. Their value extends beyond installation to helping clients understand core logic behavior, providing insights from other deployments, interpreting confidence levels, and helping shape decision-making workflows that keep humans in control. The next generation of integrators will succeed not by being just technologists, but by helping connect a client’s operational goals with the AI system’s capabilities, with a line of sight to the company’s environment.

Looking Ahead

Over the next three to five years, AI/ML security systems will redefine what it means to “monitor” and “respond” in security operations. ML models are already evolving into foundation models—large-scale, pre-trained architectures capable of adapting to diverse visual and behavioral tasks with minimal additional data. These will enable context-aware analytics that evaluate the entire environment rather than isolated events.

At the same time, edge-deployed AI will become standard practice. Instead of streaming all data to the cloud, smart cameras and IoT sensors will run the intelligence locally, delivering faster results while reducing bandwidth and storage demands. Agentic AI introduces an orchestration layer that connects analytics engines, access control, video systems, and workflow tools. Instead of triggering a single alert, it can reason across systems, select appropriate actions, and execute responses under operationally defined parameters.

Yet the central truth will remain unchanged: machine learning amplifies human foresight - it does not replace it. The most effective organizations will cultivate a new kind of security professional, part analyst and part systems thinker, who can interpret both human and organizational intent and AI actions and behaviors.

The sophistication of algorithms alone will not define the future of security, but by the wisdom with which they are used. Those who learn to blend technical precision with human judgment will not merely adapt to the age of intelligent systems—they will lead it.