There has been a lot of discussion in Executive Protection circles about the responsible use and application of Artificial Intelligence (AI). Recently, at the Board of Executive Protection Professionals’ Executive Security Operations Conference in Orlando, Florida, I had the pleasure to sit down with some of the industry’s biggest leaders in AI advancement and discuss its impact on the career field. This article is the result of those discussions.
In the progressively advancing field of Executive Protection (EP), technological advancement is both needed and challenging. AI has emerged as a transformative force in some areas of protective operations, enabling proactive threat detection, predictive analytics, and faster, smarter resource deployment. But as we continue to integrate AI into EP workflows, it introduces new risks ranging from operational blind spots and flawed information returns to ethical and legal concerns.
The Transformative Role of Artificial Intelligence in Executive Protection
We operate daily in a zero-fail industry. We plan, anticipate, and prepare for every threat we can think of. It’s what we do: we prepare for the worst and hope our work eliminates or slows our adversaries. Our mission may seem simple and clear, but it is by no means easy, and we continually look for resources to safeguard our protectees from targeted violence. As the threat landscape grows more complex, so must the tools and tactics used to mitigate the risks we plan for. AI has emerged as a disruptive force in EP, but its application has been mired in misuse and misunderstanding.
Today, AI has been embraced by many as a force multiplier that is rapidly redefining how protection professionals operate, plan, and respond. If you aren’t using it, you may be losing business to those who do. Don’t get me wrong, we still need knuckle-dragging truck monsters that wear tailored Brooks Brothers suits, but they’d better start paying attention to technology or walk the short path of the Neanderthals. Currently, AIs have made the biggest impact in the following EP sectors:
Proactive Threat Detection and Intelligence Analysis
Traditional intelligence gathering often relies on static, time-consuming methods, news scanning, manual social media monitoring, and analyst interpretation from known data resources. AI has revolutionized this process by processing vast quantities of open-source intelligence (OSINT), dark web activity, and social media content data in real-time. Intelligence companies are now routinely using algorithms to flag indicators of targeted violence, doxxing attempts, and emerging protest activity before it manifests into physical attacks.
Machine learning models applied to crowd-sourcing resources continually adapt, improving their predictive accuracy while learning and filtering out irrelevant noise, allowing protective teams to focus on credible, actionable intelligence. Many teams today have forgone the use of traditional Security Operations Centers (SOC’s) for real-time protective intelligence cellular telephone apps and analytics that alert protectors based on geofenced locations and user-defined alerting criteria. Notifications are increasingly faster, can provide detailed link analysis, and are more accurate without human review. There will always be a need for human intelligence resources, but the management and application of sourced AI information is the future of protective intelligence.
Enhanced Situational Awareness
AI-powered video analytics, weapons detection systems, and geospatial tools provide EP teams with unprecedented situational awareness that was previously only available to government agencies. Facial recognition, crowd density analysis, and behavioral anomaly detection systems embedded in surveillance networks offer real-time alerts that support protective operational decision-making.
The use of AI recently aided federal investigators in scouring social media to accurately identify Compton resident Elpidio Reyna, a masked protester who attacked federal agents in Los Angeles by throwing rocks at their vehicles. Whether you’re monitoring a corporate event or a high-risk travel route, AI systems can help you detect potential risks such as loitering behavior, unattended packages, or license plates associated with known threat actors (LPRs), providing early warning that would be nearly impossible to achieve through human observation and recognition alone.
Smart Routing and Travel Risk Mitigation
AI-driven navigation and geolocation platforms now offer dynamic route planning resources that account not just for traffic, but for crime data, geopolitical alerts, and even protest activity scraped from social media.
This technology is also currently used in autonomous vehicle systems and crowd-sourced mapping apps. Everyone reading this has used AI-powered travel risk mitigation when they were alerted via “Waze” of a crowd-sourced police officer on their route, and hopefully, you slowed down. AI enables safer, more efficient travel for protectees, especially in unfamiliar or unstable environments. When combined with AI-enhanced travel risk assessments, protective teams can better anticipate chokepoints, protest areas, or regions with emerging criminal trends.
Personalized Risk Profiling and Operational Resource Allocation
Every executive has a unique threat profile influenced by their industry, public persona, online presence, and/or recent corporate decisions. AI tools can compile and analyze data points to produce individualized risk assessments for these protectees. These AI-generated assessments help teams identify who may be targeting your protectee from specific groups or ideologies, activists, hacktivists, and even disgruntled former employees, enabling the development of tailored protection strategies that are both targeted and scalable.
Predictive modeling tools also help EP teams allocate manpower and resources more efficiently by identifying when and where threats are most likely to occur. AI can assist in staffing decisions, asset deployment, and logistical planning by analyzing historical data alongside real-time threat information. The result is a leaner, more responsive operation that maximizes protective resources while minimizing unnecessary visibility or disruption.
As information is collected over time, these analytics become more fine-tuned by identifying historically relevant threat modelling. Statistical analysis and predictive modeling may never overcome the human condition, but they are exponentially progressing toward the best possible answer to every question.
Red Teaming and Simulation Training
Advanced AI systems are now being used to simulate threat scenarios, providing red-teaming exercises that help EP units rehearse their responses to everything from active shooter events to cyber attacks. These simulations are not only more realistic than traditional tabletop exercises, but they can also incorporate real-world threat actor behavior, improving preparedness in a rapidly evolving risk environment.
So all of this sounds great, right? What’s not to like about AI?
Plenty.
But to be clear, it’s not AI’s fault, it’s ours.
Some in our industry are trying to correct this with Standards, but you can’t teach integrity. With all its potential benefits, AI will never replace human intuition, physical training, and the personal experiences that define why you were hired as an Executive Protection professional. Unfortunately, some in this field are misusing AI, abandoning their honor for quick profits from subpar products that AI creates using faulty data sets and flawed user input. Fortunately, most EP professionals recognize this, at least from the conversations I’ve had, because AI-produced products are easily recognized by experts and scream, “I was made using AI.” (People were named and examples given.)
I’m from Ohio, where we talk like people sound on American television. We don’t add unnecessary “O’s” and “U’s” or end words with extra “E’s” like my British friends (yes, I understand it’s called English for a reason). But just like you recognize when someone from Great Britain is writing an article, you also recognize when someone uses AI to write one. The industry isn’t stupid.
We recognize when you create “Frameworks” or produce for-profit training from clearly recycled (and dated) EP information. Old principles with new formatting don’t make new ideas. No one writes like machines do, and when you produce work products using AI, it’s very obvious. In addition to the questionable use of AI by some, there are other negative consequences of AI use as it applies to EP.
Overreliance on Technology at the Expense of Human Judgment
One of the most dangerous pitfalls of AI integration in EP is the temptation to substitute critical thinking with easy automation. Intelligence sourcing and analysis using AI is a double-edged sword. AI can process volumes of data at scale, but it lacks context, intuition, and real-world experience, the very things that protectors rely on during dangerous or high-stakes protective operations. Blindly following AI-generated intelligence alerts or risk assessments can result in poor decision-making, missed threats, and even preventable security incidents.
Example: A facial recognition system might flag an individual as a match to a known threat actor. Without human verification, this could lead to an unnecessary escalation, or worse, a physical confrontation.
False Positives and False Negatives
AI tools, especially those used for facial recognition, behavioral analysis, or social media sentiment monitoring, are not foolproof. AI processes “ALL” data. Not correct data or verified data, but “ALL” data. This means that the AI-generated information you used to write that training manual, book, or framework probably has incorrect information in it because the writer lacked the experience to know the difference between correct and incorrect information.
These false positives can cause unnecessary confusion and erode trust among protectees, protectors, and other stakeholders. In the use of AI for protective intelligence, false negatives may result in genuine information or threats going undetected. The risk of either scenario is extremely dangerous and increases significantly when AI systems are deployed without human oversight or proper calibration. Again, nothing outperforms experience. In the words of Wyatt Earp, “Fast is fine, but accuracy is everything.”
Example: If a behavioral analytics system misinterprets body language due to cultural differences or a contextual misunderstanding, it could cause a disproportionate security response or ignore a genuine threat.
Data Privacy and Legal Exposure
AI relies on data, large volumes of personal, behavioral, and biometric data, to function effectively. The collection, storage, and analysis of this data can raise serious privacy concerns. EP teams and companies collecting or storing AI resources and operating in regions with strict data protection laws (such as the EU’s GDPR or California’s CCPA) could face negative legal consequences if AI-driven systems are not legally compliant and protected.
Additionally, the perception of surveillance can create reputational harm for both the protectee and the company, especially if AI tools are used in a manner that invades employee, public, or client privacy. Stored biometric data has already raised significant privacy questions for protective operations teams, and at least one well-known intelligence application company has been sued for permitting access to questionable PII information.
EP Training and AI
After long discussions with professionals about AI, the most common concern was “The use of AI in EP training has the highest potential for negatively impacting the industry.” Here were some of the key points of that conversation:
The Erosion of Critical Thinking and Decision-Making and Simulation Bias
Overreliance on AI-driven simulations, threat detection, or scenario generation may reduce a trainee’s ability to develop independent judgment and situational awareness. Protectors may become conditioned to rely on AI cues rather than their instincts or training under ambiguous or real-world conditions.
An AI-generated scenario checklist is great for documentation purposes, but if it’s used to grade responses in real time, students may overlook, react inappropriately, or ignore obvious or potential threats to meet the needs of the AI-developed training standard. Likewise, instructors with limited experience may become over-reliant on AI-produced information and inadvertently leave out critical information and skills needed for effective protective coverage.
Example: In a Firearms Training Simulator (FATS), the AI-generated scenario presents two people arguing loudly on a subway platform. When audibly confronted by the human performing the training, the AI-driven technology cues one of the digital people on the platform to turn suddenly and present a “Cellphone” in a “furtive movement.” The human completing the training will most likely draw their weapon during the sudden movement, thinking the AI assailant has a weapon. Whether they fire at the cellphone-wielding person is a combination of experience and fast weapons recognition. If students become desensitized to this scenario through continuous training, they may respond slowly, or not at all, when an assailant exhibits this behavior and has a weapon in real life. We perform as we are trained.
Depersonalization of Soft Skills Training
Currently, AI struggles to replicate human emotion, unpredictability, and social nuance. “Maybe” and “uncertainty” haven’t been calculated by AI to precision yet. This may not be the case in 5 years, but until Skynet becomes active, and we are slaves to our mechanical overlords, we are all safe in saying that human social interactions are unique to humans.
Critical soft skills like conflict de-escalation, human behavioral reading (body language and attack cycle indicators), and interpersonal communication can be neglected if training relies too heavily on AI-generated interactions.
Overreliance on AI Information and Physical Skill Atrophy
EP is an inherently physical career field. At the macro level, we plan elaborate protective advances and logistics to mitigate the possibility of physical confrontations and attacks. At the micro level, if those planned mitigations fail, we may have to physically confront an attacker while covering and evacuating our protectee.
If you’re sitting in your mom’s basement right now reading this, that $500 you just spent on that online “Essential EP Skills Course” from the heavily tattooed social media character was money you’ll never get back because this isn’t the job for you. This is a job of “doing” and experience in “doing” matters. You’d be better off training for EP by putting on a suit and standing in your backyard for 12 hours.
The use of AI for training may encourage those with limited experience or skillsets to neglect the extensive foundational skills and training needed to be in this field (e.g., AOPs, physical security assessments, advance planning, and even manual route planning). It may also give unqualified instructors the impression they can “fake it till they make it” by using AI-created information to supplement their incomplete skillset.
Cybersecurity Vulnerabilities
As complex as AI systems are, like all digital platforms, they are susceptible to hacking, spoofing, or data manipulation. A compromised AI platform could be weaponized by threat actors to manipulate threat assessments, jam signals, or even create false alerts. Without robust cybersecurity measures, these vulnerabilities could become major points of exploitation in EP operations. AI hacking toolsets are already available for purchase on the dark web, but let the buyer beware. Nation-state hacking tools routinely have backdoors.
Example: A deepfake video or synthetic voice generated with AI tools could easily impersonate a protectee or executive, triggering confusion, misinformation, or loss of assets and brand damage.
Bias in Algorithms
As mentioned, AI results are only as accurate as the data they have access to. If the data set has false information in it, the resulting AI response will include that data. Historical data sets, especially in law enforcement and security areas, can reflect societal biases that, if unchecked, become embedded in algorithmic decision-making. This can lead to the misidentification or false narratives of individuals based on race, gender, or geography, with serious ethical and operational implications.
Example: An AI tool used for benchmarking that disproportionately identifies certain demographic groups as threats may skew protection resources or create legal and reputational consequences.
Loss of Flexibility in Dynamic Environments
I’ve said it twice already, but it doesn’t hurt to say it again: “Nothing beats experience.” Executive Protection is an adaptive and learned discipline. Decisions often need to be made in milliseconds, with incomplete information, changing variables, and evolving threats. This is where experience routinely outperforms AI. AI thrives in structured environments with defined parameters, not in the unpredictable, fluid, and often chaotic realities often seen in protective operations. Relying too heavily on data-driven outputs can lead to rigidity in planning and slow responses.
Conclusion
While AI offers significant advantages to Executive Protection, it is not a silver bullet. The key to leveraging AI effectively lies in balancing and augmenting human capability, not replacing it. Protection professionals must remain vigilant about the technology’s limitations, audit systems for accuracy and bias, and maintain human oversight at every step. In a field where lives, reputations, and corporate integrity are on the line, the consequences of misplaced trust in automation can be severe. The smartest EP operations will treat AI as a tool, not a decision-maker.
Thank you to the participants of the ESOC who contributed thoughts and big words to this article. I am grateful and smarter for having listened.
*AI was not used in the production of this article; however, several bar napkins met their end in the annotation of comments recorded for this feature.
