Summary:

In the world of security, where human observation sometimes falls short, agentic AI is stepping in as a powerful ally, enabling professionals to detect hidden threats with remarkable accuracy. Drawing from the deductive brilliance of Sherlock Holmes, these AI systems use machine learning to identify behavioral patterns that signal potential threats, such as theft, trespassing, or insider breaches, before they escalate.

Key areas where AI shines include:

• Pattern Recognition: AI tracks subtle behavior patterns like loitering, marking potential threats like individuals casing a facility.
• Breach Detection: Unlike traditional motion sensors, AI reduces false alarms by 30%, accurately recognizing genuine intrusions and unauthorized access attempts.
• Anomaly Detection: AI excels in spotting behavior that deviates from the norm, alerting security to unusual crowd formations or employee activity.

Real-world examples demonstrate AI’s effectiveness:

1. LVT’s “Talk-Down”: Deterrence in action at Enel Green Energy, where AI combines thermal imaging, behavioral recognition, and audio warnings to prevent intruders.
2. Belle Fourche, SD: AI-enhanced surveillance reduced crime calls by 80%, automating license plate recognition and enabling near-instant arrests.
3. Winston-Salem Police: AI accelerates crime-solving by quickly scanning footage for vehicles, drastically reducing time spent on video analysis.

These systems not only spot hidden threats but also act proactively, transforming security from reactive to predictive. AI’s 24/7 vigilance ensures constant monitoring, offering precision in detection and coordination.

By integrating AI, businesses and law enforcement are empowered to detect threats four times faster than traditional methods, ensuring continuous vigilance and immediate response to emerging security risks.

Spotting the Invisible: How Agentic AI Reveals Hidden Threats

“The world is full of obvious things which nobody by any chance ever observes,” Sherlock Holmes noted in “The Hound of the Baskervilles.” Today’s security professionals face a similar challenge: detecting subtle indicators of threats before they escalate into incidents.

Enter agentic security AI—a revolutionary technology that enhances human observation capabilities. Like Holmes himself, these systems excel at spotting seemingly insignificant details that signal impending threats. From thermal imaging that reveals hidden intruders to behavioral analysis that identifies suspicious patterns, AI transforms raw surveillance data into actionable intelligence. Between 2022 and 2025, this technology has proven essential in preventing security breaches across the United States. 

Machine Learning Spots Potential Threats

Like Holmes himself, modern security demands both keen observation and intelligent analysis. Agentic security platforms excel at three critical tasks:

• Pattern Recognition: Just as Holmes noticed mud on a suspect’s boots, agentic AI analyzes patterns to differentiate between normal activity and potential threats, enabling proactive response.
• Breach Detection: Where traditional sensors trigger indiscriminately, AI distinguishes genuine threats from false alarms, integrating with access control to prevent tailgating and deliver instant mobile alerts.
• Anomaly Detection: AI spots what Holmes called “the curious incident”—behavior that doesn’t fit established patterns. At New York City’s subway system, this means identifying agitated movements or suspicious behavior before incidents occur.

Hidden Threats Revealed: AI in Action

Like Holmes’ most memorable cases, modern security solutions prevent crimes before they occur. The evidence lies in three remarkable cases where AI transformed threat detection from a reactive to a proactive approach. 

The Case of the Night Intruder: LVT’s AI-Powered ‘Talk-Down’

“It was a Friday night when my phone lit up with alerts,” recalls the head of security at Enel Green Energy’s Merrimack River facility. “The thermal streams showed figures testing our perimeter fence. One spotlight and automated warning later, they scattered like leaves in the wind. Mission accomplished.”

 The LVT Unit that thwarted these would-be intruders represents Holmes-level observation in action. Like the detective’s legendary attention to detail, these solar-powered sentinels combine:

• Weatherproof durability (IP-54 rated)
• Thermal imaging for crystal-clear night vision
• AI-driven behavioral analysis
• Automated deterrence through strategic lighting and audio warnings
• Instant mobile alerts via the SafeNow app

For Enel Green Energy, this intelligent defense of their Lowell, Massachusetts hydropower site proved as effective as having Holmes himself on watch.

The Adventure of the Vanishing Vandals: How AI Solved Belle Fourche’s Mystery

In Belle Fourche, South Dakota, a city of nearly 6,000 residents faced a perplexing challenge: over 1,000 vandalism calls overwhelmed their police force. The solution? A network of AI-enhanced cameras that, like Holmes’ legendary powers of observation, could spot patterns invisible to the human eye.

Within six months of deployment, vandalism calls plummeted to just 200. The key wasn’t just watching—it was observing. The system recorded footage, of course, but it also intelligently processed and analyzed those recordings in real-time. The system’s machine learning capabilities proved particularly effective at capturing crucial details, including license plates of getaway vehicles, patterns of suspicious behavior, and signs of impending trouble that even seasoned officers might miss. 

The Science of Deduction: Winston-Salem’s AI Observatory

While Belle Fourche officials demonstrated AI’s power to prevent crime, Winston-Salem, North Carolina, showed how it could crack cases at Holmes-like speed. Their Real-Time Crime Center transformed what once took investigators hours into a 15-minute exercise in digital deduction.

The secret? A network of AI systems working in concert, much like Holmes’ own web of informants:

• Instant vehicle tracking across multiple cameras
• Automated cross-referencing of evidence
• Real-time dispatch integration
• Smart filtering of false leads

“A more informed officer can make smarter decisions when able to use real-time intelligence to detect and deter potential threats, as well as solve crimes. The result? A system that, like Holmes himself, can reconstruct a crime scene’s story from the smallest details—and do it while the trail is still warm. 

The Sign of Four: New Orleans’ Network of Observers

Since 2010, Project NOLA’s network of 5,000 AI-enabled cameras has served as the city’s vigilant eyes, helping investigate more than 500 homicides and countless other crimes. Like Holmes’ own network of informants, these digital sentinels work tirelessly to spot potential threats before they escalate.

“We assist when bad things happen,” Bryan Lagarde of Project NOLA told WDSU, “but we’re very actively working in the background trying to prevent them from happening in the first place.” This preventive approach has proven crucial in a city that knows too well the cost of missing subtle warning signs.

The Power of AI Observation

“You see, but you do not observe,” Holmes once chided Watson in “A Scandal in Bohemia.” Today’s agentic security systems do both:

• Predictive Analysis: AI surveillance combines multiple data streams to detect comprehensive threats.
• Enhanced Coverage: Like Holmes’ network of informants, these systems create a web of intelligent observers monitoring multiple streams simultaneously.
• Connected Intelligence: The result is what Holmes would call “a connected chain of inference”—an approach now recommended by the U.S. Department of Homeland Security and the Cybersecurity and Infrastructure Security Agency’s 2024 AI guidelines. 

The Final Deduction: Elementary Truths

“I never guess. It is a shocking habit—destructive to the logical faculty,” Holmes declared. Like the great detective, agentic security relies on methodical analysis. Across the country, from energy facilities to city streets, these systems have proven their worth through verifiable results:

• Commercial environments where AI prevents theft and organized retail crime
• Industrial facilities protected by weatherproof units
• Public spaces monitored by Real-Time Crime Centers
• Critical infrastructure safeguarded against unauthorized access

The game is afoot—but now the advantage belongs to organizations that harness the power of machine learning’s observation. Ready to put AI’s deductive capabilities to work protecting your assets? Contact our team today.