The conversation around artificial intelligence in the security industry has become impossible to ignore.
Should we use it? Where does it fit? What are the risks? Who makes the decision?
These are valid questions, but they may also be the wrong ones to focus on.
Because if you've spent enough time in this industry, you understand a fundamental truth: Security is not black and white.
It's not about winning or losing. It's not about absolutes. And it's never truly finished.
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Security is about anticipating risk before it becomes reality, reducing impact when it does occur, and constantly adapting to an environment that refuses to stand still.
That's the job. Not perfection, progress under pressure.
At its core, the dynamic has always been the same: Us versus them.
"Them" isn't always a person. Sometimes it's a vulnerability. Sometimes it's a system. Sometimes it's opportunity in the hands of someone willing to exploit it.
This is why the debate around AI isn't as much of a debate as many think. Because in many ways, the decision has already been made, not by us, but by the world we operate in.
The Environment Has Decided
While security professionals and organizations debate how and when to implement AI, adversaries aren't waiting.
We're already seeing AI-generated phishing campaigns that mimic internal communication styles with unnerving precision; deepfake voice and video used to impersonate executives and authorize fraudulent transactions; automated vulnerability discovery and exploitation happening at machine speed; and AI-driven open-source intelligence collection that can map individuals, families, and organizations in minutes.
These aren't coming. They're here.
For executive protection professionals, this means the threat profile around principals has fundamentally changed. A sophisticated adversary no longer needs weeks of physical surveillance to build a detailed pattern-of-life assessment. AI-enabled OSINT tools can aggregate publicly available information, social media activity, travel patterns, and professional networks in hours, not days.
For corporate security directors, the implications are equally stark. Social engineering attacks that once required human intuition and cultural knowledge can now be automated at scale. Phishing emails that perfectly mirror internal communication styles, voice clones that replicate executive speech patterns, and video deepfakes convincing enough to fool trained staff in high-pressure moments.
A widely reported case in 2024 made this painfully clear. An employee at a multinational organization was convinced to transfer funds during a video call with what appeared to be company leadership. The executives on the call were not real; they were AI-generated deepfakes.
No malware. No system breach. Just trust, weaponized through technology.
That incident confirmed what many already suspected: The threat landscape has already changed.
A Familiar Pattern, Compressed
Every major technological shift in history has followed a familiar pattern: A new capability emerges. It is often adopted first by those looking to exploit it. Security then adapts, builds countermeasures, and integrates new tools.
We saw this with the internet, with mobile technology, and with social media. AI is not a deviation from that pattern. It's the next phase of it.
The difference now is speed. AI doesn't just expand capability, it compresses time. And in security, time often determines whether an incident is prevented, contained, or escalated.
Traditional security operations rely on the ability to detect anomalies, assess threats, and respond before damage occurs. That window is collapsing. Automated attacks can move from reconnaissance to exploitation in minutes. AI-generated social engineering can bypass traditional indicators that human analysts would catch. The lag between threat emergence and detection is shrinking to a point where human-only response cycles can't keep pace.
This creates a strategic imperative: organizations that fail to integrate AI-enabled detection and response capabilities aren't just falling behind in capability. They're falling behind in time itself.
AI can process massive amounts of data and identify patterns at scale. But it cannot interpret context in the way an experienced professional can.
Who Decides How It's Used?
If the adoption of AI is already underway, the more relevant question becomes: Who decides how it is used?
Is it security leadership? Technology teams? Legal and compliance? Executive stakeholders?
The reality is that no single group can own this decision in isolation. Effective integration requires collaboration across all of them.
Security leadership understands the operational requirements and threat environment. Technology teams know what's technically feasible and how systems integrate. Legal and compliance teams manage regulatory obligations and liability exposure. Executive stakeholders set risk tolerance and resource allocation.
Poorly integrated AI creates more problems than it solves. Security teams implement tools they don't fully understand. Technology teams deploy capabilities without clear governance frameworks. Legal teams react to incidents rather than shaping policy proactively. Executives approve budgets without understanding what they're actually authorizing.
AI integration done well looks different. Cross-functional working groups establish clear use cases, ethical boundaries, and accountability structures before deployment. Security operations staff receive training not just on how to use AI tools, but on how to interpret their outputs and when to override automated recommendations. Governance frameworks define what AI can decide autonomously and what still requires human authorization.
AI is not just a technical capability. It's a force multiplier with operational, ethical, and reputational implications.
When used responsibly, it can enhance situational awareness, accelerate analysis, and strengthen decision-making. Used carelessly, it can introduce noise, bias, and dangerous overreliance on automated outputs.
The question of whether AI belongs in security has already been answered. The environment has decided.
Context Still Requires People
This is where human expertise remains irreplaceable.
AI can process massive amounts of data and identify patterns at scale. But it cannot interpret context in the way an experienced professional can. It cannot weigh intent, cultural nuance, or complex human behavior.
An AI system can flag an anomaly in a principal's travel pattern. It cannot assess whether that anomaly represents a genuine threat or a last-minute schedule change driven by business opportunity. It can identify a potential social engineering attempt based on linguistic patterns. It cannot judge whether the unusual phrasing is a threat indicator or simply a senior executive typing quickly on a mobile device.
In dynamic environments, context is everything, and context still requires people.
The future of security is AI working alongside experienced professionals to strengthen decision-making and responsiveness. AI handles the volume, speed, and pattern recognition that humans can't match. Humans provide the judgment, contextual awareness, and ethical oversight that AI can't replicate.
The most important takeaway is this: The question of whether AI belongs in security has already been answered.
The environment has decided. The threat landscape has evolved. The tools are already in play.
The only remaining question is how we adapt, deliberately, responsibly, and quickly enough to matter.
Because in this field, standing still isn't neutral. It's falling behind.
By Nicholas Lake | Director, Family Security Programs at Crisis24
This article was published by The Circuit Magazine. For weekly intelligence briefings on the security and protection industry, subscribe to On The Circuit.