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Why Judgment Will Matter More Than Intelligence in the AI Era

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As artificial intelligence makes raw analytical power abundant, the scarce – and decisive – executive asset will be judgment.

This piece was originally published in C-Suite Quarterly. 

By Elliott Broidy / July 10, 2026

For most of my career, intelligence was the quality businesses competed hardest to acquire. Firms chased the smartest graduates, assembled teams of specialists, and accumulated proprietary knowledge wherever they could find it. Information itself was a competitive advantage, and the organizations that knew more — about markets, about counterparties, about risk — tended to win.

Artificial intelligence is changing that equation.

Why Intelligence Is Becoming Abundant

Today’s systems can draft reports, summarize research, analyze financial statements, write code, and generate strategic options in seconds. As those capabilities spread to every desk in every company, access to information and analytical horsepower is becoming democratized. Intelligence, in the sense most organizations have prized it, is becoming abundant. What will remain scarce is judgment.

What Judgment Actually Is

Judgment is the capacity to determine which information actually matters, which recommendations to trust, and which course of action best serves an organization’s long-term interests. It is not the same as raw processing power, and it cannot be reduced to pattern recognition. It draws on experience, context, values, and a hard-won understanding of how people behave under pressure. It is, in the end, the difference between knowledge and wisdom.

Where AI Stops and Judgment Begins

Consider an acquisition. I have spent much of my career evaluating businesses and the people who run them, and I can tell you that a model is now perfectly capable of surfacing innumerable plausible targets, complete with comparable transactions and a defensible valuation range. What it cannot do is read the integrity of a founder across a negotiating table, sense whether two cultures will actually fit once the buzz of the announcement wears off, or know whether this is the right moment to move. It can generate options. It cannot own the consequences of choosing one.

The same is true of nearly every consequential decision an executive makes. Leaders are forever balancing competing priorities: growth against risk, near-term performance against long-term investment, shareholder expectations against employee morale, the pull of innovation against the value of stability. These calls rarely have an objectively correct answer waiting to be calculated. They require judgment — and the willingness to be accountable for it.

Judgment Is Earned, Not Automated

Judgment is also earned. It accumulates through successes and failures, difficult conversations, setbacks no one saw coming, and years of watching how decisions actually unfold once they leave the room. It is shaped by character as much as by experience. Integrity, humility, curiosity, and a genuine willingness to question one’s own assumptions all make for better judgment. There is no shortcut, and I have never met a leader who developed it any faster than the rest of us.

Why AI Raises the Value of Human Judgment

Here is the irony worth sitting with: AI may raise the value of these human qualities rather than diminish it. When everyone has access to the same tools and the same information, advantage no longer comes from producing answers. It comes from asking sharper questions, interpreting results wisely, and making sound decisions in conditions of real uncertainty. The technology levels the analytical playing field. It throws the weight back onto the things that were never going to be automated. I have seen this firsthand in my own work on innovation in public-safety technology, where AI can surface possibilities at remarkable speed, yet the decisions that carry real consequences still come down to human judgment.

Elliott Broidy on the Leaders Who Will Thrive

The leaders who thrive in this era will not necessarily be the ones who lean on artificial intelligence the hardest. They will be the ones who pair its capabilities with distinctly human strengths — wisdom, ethical reasoning, empathy, and the nerve to make a hard call when no algorithm can tell them what is right.

In the years ahead, intelligence will continue to commoditize. Judgment will not. For those of us entrusted with leading organizations through uncertainty, it may prove the most valuable asset of all.

Elliott Broidy is an entrepreneur who has used his extensive experience and talent to found, invest, and in some cases, manage as CEO, more than 160 companies over his four-decade career. Since 2014, he has focused on technology businesses (including, more recently, AI) in the defense intelligence, homeland security, public safety, and law enforcement sectors.

Humans are using laptops and computers to interact with AI, helping them create, code, train AI, or analyze big data with fast, cutting-edge technology.

Elliott Broidy on What an AI Assistant Can Do for Executives

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Elliott Broidy public safety technology

Elliott Broidy on Leadership, Investment, and Purpose-Built Innovation in Public Safety Technology

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This article was originally published on Tech Bullion.

By James Andrew

Elliott Broidy has been a leading investor and builder in the modernization of law enforcement and corrections technology, directing sustained capital and strategic focus toward the development of infrastructure and operational systems designed specifically for public safety environments. Through companies such as LEO Technologies (LeoTech) and Cynapse.ai, Broidy’s approach has centered on building reliable, scalable technology platforms that function as mission-critical infrastructure rather than experimental tools. These companies were established with a clear objective: to create purpose-built systems capable of supporting the complex operational demands of correctional facilities, law enforcement agencies, and national security partners.

Unlike many commercial technology providers that adapt existing enterprise solutions to public safety, these platforms were engineered from the ground up to operate in secure, high-risk environments where reliability, continuity, and performance are non-negotiable. Significant investment was made early in infrastructure, technology integration, and data architecture, the foundational elements required to ensure that artificial intelligence could later be deployed safely, effectively, and at scale. That disciplined, infrastructure-first strategy has enabled the development of systems that are operational today, trusted by agencies, and designed to support public safety missions for the long term.

Modern Technology Built the Foundation. Artificial Intelligence Is Accelerating the Mission.

The modernization of law enforcement and corrections did not begin with artificial intelligence. It began with the steady introduction of modern technology and digital infrastructure, systems capable of capturing, storing, and securing vast amounts of operational data. For decades, public safety agencies operated with limited connectivity, fragmented records systems, and manual investigative processes. Only in recent years have those foundational technologies matured to the point where advanced analytics and artificial intelligence can deliver their full value.

“Artificial intelligence is now playing a critical role in public safety,” says Elliott Broidy. But it is not the starting point of modernization. It is the accelerator. Broidy adds: “The real transformation began when agencies invested in digital communications systems, secure networks, cloud storage, and integrated operational platforms. Those investments created the infrastructure that now allows AI to surface insights, automate routine analysis, and support faster, more informed decision-making.”

In that sense, the evolution of public safety technology follows a clear and predictable sequence:

Infrastructure → Technology → Data → AI → Operational Impact

Each step is built upon the previous one. Without infrastructure, there is no technology platform. Without technology, there is no reliable data. Without data, artificial intelligence has nothing to analyze. And without AI, agencies remain constrained by the limits of human capacity to process information at scale.

A Technology Gap That Arrived Late to Public Safety

The delayed modernization of law enforcement and corrections is not the result of resistance to innovation. It is largely the result of funding constraints, legacy procurement cycles, and the mission-critical nature of public safety operations, where reliability must always take precedence over novelty. While industries such as banking, healthcare, and retail digitized decades ago, many public safety agencies continued to rely on manual processes and disconnected systems well into the 21st century.

Elliott Broidy explains: “That technology gap is now closing rapidly. Agencies are modernizing infrastructure, integrating data sources, and deploying platforms capable of supporting real-time operational awareness. Artificial intelligence is accelerating this transition by enabling systems to analyze information continuously, identify patterns, and prioritize investigative leads that would otherwise remain buried in massive datasets.”

The result is not simply improved efficiency. It is improved safety.

From Data Collection to Operational Intelligence

“Modern public safety environments generate enormous volumes of data every day,” Broidy says. “Correctional facilities record communications between incarcerated individuals and external contacts. Law enforcement agencies capture digital evidence, incident reports, and dispatch records. Surveillance systems produce continuous streams of video across thousands of cameras. Each of these data sources contains valuable intelligence, but historically, extracting that intelligence required extensive manual review.”

Technology solved the first part of the problem by making data collection reliable and scalable. Digital recording systems ensured that communications could be captured consistently. Networked storage systems ensured that data could be retained securely. Integrated platforms ensured that investigators could access information when needed.

Artificial intelligence is now solving the second part of the problem: making that data actionable.

AI systems can analyze communications, identify behavioral indicators, detect anomalies, and surface relevant information across multiple cases simultaneously. Instead of replacing investigators, these systems act as force multipliers, enabling personnel to focus on high-value decision-making rather than routine data review.

This shift from data collection to operational intelligence represents one of the most significant advancements in public safety technology in decades.

Operational Technology, Not Experimental Innovation

Public safety agencies do not deploy technology based on theoretical potential. They deploy systems that perform reliably under real-world conditions. In that environment, the distinction between emerging technology and operational capability is critical.

Modern public safety platforms are no longer experimental tools. They are operational systems integrated into daily workflows, supporting investigations, improving situational awareness, and reducing administrative burden. These systems operate continuously, often across multiple facilities and jurisdictions, providing consistent performance in high-stakes environments.

According to Elliott Broidy, “Artificial intelligence enhances these platforms by increasing speed, accuracy, and scalability. It allows agencies to process information in seconds that previously required hours or days of manual review. It enables early detection of risks that might otherwise go unnoticed. And it supports more efficient allocation of limited personnel resources.”

He adds: “Most importantly, it does so within established operational frameworks that preserve accountability, transparency, and human oversight.”

Read also: Examining Trends in National Security and Defense Tech with Elliott Broidy

Communications Intelligence in Correctional Environments

One of the clearest examples of infrastructure-driven modernization can be found in correctional communications monitoring. For years, correctional facilities recorded inmate communications for security and investigative purposes. The challenge was never accessing the information. The challenge was the volume of information and the time required to analyze it.

Elliott Broidy explains: “Modern technology platforms have transformed that environment by integrating communications recording, data storage, and investigative workflows into unified operational systems. Artificial intelligence now enables those systems to analyze communications continuously, identify patterns, and alert investigators to potential threats in real time.”

The operational impact is measurable. Investigations move faster. Staff workloads decrease. Risks are identified earlier. And facilities operate more safely.

These outcomes are not theoretical. They reflect the practical application of technology and infrastructure working together to support mission-critical operations.

Video Intelligence and Real-Time Awareness

A similar transformation is occurring in the management of video surveillance systems. Public safety agencies have invested heavily in camera infrastructure over the past two decades, resulting in widespread deployment of surveillance technology across correctional facilities, transportation hubs, and public spaces.

“Historically, video footage was reviewed only after incidents occurred, limiting its value as a proactive security tool,” says Broidy. “Advances in computing power, network connectivity, and storage capacity have made continuous video monitoring possible. Artificial intelligence is now enabling those systems to detect unusual activity, track movement patterns, and provide real-time alerts to personnel.”

This capability transforms video from a passive recording system into an active operational asset.

By combining infrastructure, technology, and AI, agencies can maintain continuous situational awareness across complex environments without increasing staffing requirements.

Accountability, Oversight, and the Human Role

As public safety agencies adopt advanced technologies, accountability remains a central priority. Artificial intelligence does not replace human judgment. It supports it.

Modern systems are designed to operate within established legal and ethical frameworks, ensuring that decisions remain subject to human review and oversight. Audit trails, reporting mechanisms, and performance monitoring tools provide transparency into system behavior and investigative outcomes.

These safeguards are essential to maintaining public trust and ensuring that technology is used responsibly.

The most effective deployments recognize that technology and personnel must work together. AI provides speed and analytical capability. Human professionals provide judgment, context, and accountability.

What Comes Next for Public Safety Technology

The trajectory for modernization in law enforcement and corrections is clear. Agencies will continue to strengthen digital infrastructure, integrate data systems, and deploy technologies capable of supporting real-time operations. Artificial intelligence will play an increasingly important role in accelerating those capabilities.

According to Elliott Broidy, the defining question for the next decade is not whether the technology works. It is how quickly agencies can adopt and scale proven systems to meet growing operational demands.

“Modern technology and digital infrastructure built the foundation for today’s public safety environment,” Broidy concludes. “Artificial intelligence is now unlocking the full operational value of that foundation, enabling agencies to respond faster, operate more efficiently, and protect communities more effectively.”

About Elliott Broidy

Elliott Broidy is an entrepreneur and investor in public safety and national security software companies. He is the Founder and Managing Partner of  LEO Technologies Holdings, LLC and Chairman and CEO of Cynapse Holdings, LLC.

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When AI Companies Know Too Much and Do Too Little

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This article was originally published on Grit Daily.

On the morning of February 10, 2026, Jesse Van Rootselaar opened fire in Tumbler Ridge, a northeastern district in British Columbia, Canada. Eight people were killed before Van Rootselaar turned the gun on herself. Almost immediately after, a question surfaced that cuts to the center of how AI systems operate behind the scenes: what, if anything, had OpenAI seen before the attack?

The company had surely seen something. Records show OpenAI identified concerning behavior in Van Rootselaar’s ChatGPT account, suspended it months before the shooting, and reviewed the content. It ultimately decided the material did not meet its threshold for referral to law enforcement, a judgment that is now drawing scrutiny.

British Columbia Premier David Eby said OpenAI may have had the opportunity to prevent the shooting, a statement that shifts attention away from the user and toward the system that flagged her behavior, yet decided against intervening. His comments have added urgency to a question policymakers have only recently begun to confront: what are companies expected to do when their own tools surface signs of potential violence?

That concern is not limited to policymakers. Elliott Broidy, who has worked at the intersection of national security, technology, and public safety, describes the issue as a breakdown in system design.

“Thirty-eight flags and no action isn’t an oversight,” he said. “It shows the system was never built to respond. If your AI can detect danger but there’s no protocol to act on it, that’s a structural failure.”

He argues that many companies have built systems that can identify risk without building the processes required to respond to it. Human oversight, often cited as a safeguard, is not consistently integrated into decision-making once a signal is generated.

This question has surfaced before, under very different circumstances. In 1969, a graduate student at UC Berkeley told his therapist he intended to kill a woman named Tatiana Tarasoff. The therapist notified campus police, who briefly detained and released him. No one warned Tarasoff. Two months later, she was killed.

The California Supreme Court’s 1976 ruling in Tarasoff v. Regents of the University of California established a standard that still governs parts of mental health law. When a professional determines, or should determine, that a patient poses a serious danger, there is a duty to act. Over time, that obligation spread across most U.S. states, embedding the idea that foreknowledge of violence can trigger legal responsibility.

AI platforms were, of course, not yet part of that framework. They are not licensed professionals, but they now process volumes of personal interaction that no therapist or institution could match. Their systems are built to detect patterns, including shifts in language and behavior that signal escalation. The question is what happens after those signals appear.

In Tumbler Ridge, the signals did appear. OpenAI’s systems flagged the account and took the step its policies required, suspending access. What did not follow was any escalation beyond the platform. There was no external referral, no independent review, and no clear standard governing whether further action was required.

Broidy sees that gap as symptomatic of an industry-wide failure to learn from sectors where the stakes are equally high.

“Every sector where lives are at stake has learned the same lesson: detection without escalation is theater,” he said. “The aviation industry didn’t stop at warning lights and nuclear facilities didn’t stop at alarms. Both built chains of accountability that force a human decision at every critical juncture. AI companies have the detection capability. What they are missing, and what the law has not yet demanded, is the chain.”

In industries where failure carries immediate consequences, that gap is not tolerated. Aviation systems route alerts to trained operators under defined procedures. Nuclear facilities tie automated warnings to escalation protocols that cannot be ignored. Emergency medicine relies on triage systems that trigger immediate human review. In each case, detection is only the first step in a chain that leads to action.

Consumer AI platforms operate at comparable scale, but without comparable requirements. Internal thresholds determine whether behavior is escalated, but those thresholds are set by the companies themselves and are not subject to external standards. Decisions not to act can remain internal, undocumented outside the platform, and shielded from scrutiny.

Existing law offers limited guidance. Section 230 has long protected platforms from liability tied to user-generated content. Traditional products liability frameworks do not map easily onto systems that continuously learn and adapt. And duty-to-warn doctrines were developed for licensed professionals, not systems that mediate millions of interactions at once.

Legal scholars have begun to outline possible paths forward, including extending a duty of care to platforms that possess documented evidence of credible threats and requiring defined escalation procedures when certain thresholds are met. Those proposals remain largely theoretical, even as the underlying technology is already in widespread use.

The response to the Tarasoff case took years to develop, eventually reshaping expectations around professional responsibility. AI systems have advanced on a much faster timeline. They are already embedded in daily life and already capable of identifying patterns that resemble foreknowledge of harm.

The gap between what these systems can see and what companies are required to do about it is no longer hypothetical. It is already visible in the record. The question now is whether the law will move quickly enough to close it.

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Anthropic’s Claude Mythos: A New Frontier In AI-Powered Cybersecurity

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This article was originally published in IT Security Guru.

In early April, Anthropic announced the release of Claude Mythos Preview, its most advanced AI model to date.

Though trained as a general-purpose large language model, Mythos quickly distinguished itself through a notable capacity for complex, multi-step cybersecurity work – autonomously parsing massive codebases, detecting critical-severity software defects and identifying vulnerabilities that have eluded human researchers for decades. Anthropic describes its cybersecurity capabilities as “substantially beyond those of any model we have previously trained,” including its prior flagship, Claude Opus.

The numbers back up that claim. When Anthropic ran the model against CyBench, a benchmark measuring autonomous vulnerability discovery and exploitation across sandboxed challenges, Mythos achieved a perfect 100% pass rate, a score Anthropic says no other AI model has achieved. On CyberGym, a separate cybersecurity evaluation site, Mythos scores 83.1% compared to 66.6% for Claude Opus 4.6. Across 17 of 18 total benchmark measures, Mythos led the field.

In practice, the model has already produced results that underscore its potential. During a preview period, Mythos found thousands of major bugs across every major operating system and browser, flaws that even the most skilled researchers had missed for years. Among the most striking findings was a vulnerability in OpenBSD that went undetected for 27 years.

“We are looking at an AI system that does not merely assist human researchers but autonomously outperforms them at scale,” said Elliott Broidy, Chairman and CEO of Broidy Capital Holdings, LLC, a seasoned entrepreneur and investor with extensive experience in national security technology and defense tech. “What Anthropic has built with Mythos is a tool that, in the right hands, could protect millions of people from attacks that would otherwise go undetected for years. The question is now whether our institutions are moving fast enough to deploy it responsibly before our adversaries find a way to use it against us.”

Given both the power and the risks that such a model entails, Anthropic decided to roll it out to a limited group of tech companies rather than the general public through a controlled defense initiative called Project Glasswing. Among the major tech companies forming the coalition are Amazon Web Services, Microsoft, Nvidia and Cisco, along with 40 other organisations that build or maintain critical infrastructure software. To support the effort, Anthropic committed $100 million in usage credits and $4 million in direct donations to open-source security organisations, enabling these partners to use Mythos exclusively for defensive security work, finding and fixing vulnerabilities before adversaries can.

Anthropic was direct about the stakes in its announcement, writing that “no one organisation can solve these cybersecurity problems alone: frontier AI developers, other software companies, security researchers, open-source maintainers, and governments across the world all have essential roles to play.”

However, the model’s very strengths are what make experts uneasy. Cybersecurity professionals have warned that if Mythos can identify and exploit vulnerabilities faster than companies can patch them, the consequences of misuse could be severe. A single AI agent scanning for flaws at this speed and scale, well beyond normal human capacity, represents what many describe as a sea change in the threat landscape.

The model has also become a flashpoint in the relationship between Anthropic and the U.S. government. The Pentagon designated Anthropic a “supply chain risk,” ordering companies working with the military to remove its software from their workflows.

The situation is evolving rapidly. White House Chief of Staff Susie Wiles and Treasury Secretary Scott Bessent met with Anthropic CEO Dario Amodei last month to discuss Mythos and pathways for renewed government collaboration. The White House is now reportedly developing an executive action that would allow federal agencies to work around the supply chain risk designation and onboard Anthropic’s models, including Mythos.

This push and pull reflects a deeper shift, raising broader questions about how emerging technologies are regulated as AI systems like Mythos begin to occupy a space between commercial tools and strategic infrastructure.

“Concerns related to government use of AI technology are reasonable,” said Broidy. “But it’s imperative to consider how technologies that today seem too dangerous or non-feasible may be life-saving tomorrow.”

Broidy added that while the safe deployment of these advanced capabilities must remain a top priority, heavy-handed regulations may prevent important technologies from developing as quickly as they may be needed.

“Solving such complex cybersecurity issues is a joint effort,” said Broidy. “Economic industries, government agencies and technology companies must work together to balance AI innovation and safety.”

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Israel Has Turned AI Into an Everyday Reality — And It’s Saving Lives

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This article was originally published on Technology.org.

I returned to the U.S. about two weeks ago, after spending the better part of a year in Israel covering Israel’s war with Hamas in Gaza and now the confrontation with Iran. A year is a long time to be embedded in a country at war. You see things that don’t make the evening news. You develop a sense of how a society actually operates under sustained threat — not in theory, but day to day.

One thing struck me more than anything else: artificial intelligence in Israel is not a buzzword. It’s not a pilot program or a Silicon Valley talking point. It’s infrastructure. As common and as load-bearing as the country’s electrical grid.

When I came home, I started calling sources to make sense of what I’d witnessed. I spoke with Elliott Broidy — entrepreneur, CEO of Broidy Capital Holdings, and founder and managing partner of LEO Technologies, LLC (LeoTech), a firm deploying AI tools for law enforcement and public safety agencies across the United States, and Chairman and CEO of agentic AI Video Intelligence company Cynapse AI. Broidy has spent years watching the intersection of AI and national security from the investment side, and he has a clear-eyed view of where Israel sits relative to the rest of the world.

“What Israel has done,” Broidy told me, “is to compress decades of technological development into a few years of operational necessity. When your survival depends on making better decisions faster than your adversaries, you don’t wait for a perfect product. You build, you test, you deploy — and you refine under pressure.”

That compression is exactly what I observed on the ground.

A Hospital That Functions Two Stories Underground

One of the more surreal moments of my time in Israel came during a visit to the Sheba Medical Center outside Tel Aviv — the largest hospital in the Middle East, with 11,000 employees, 1,700 doctors, and over 3,000 nurses. What I found was a functioning hospital operating almost entirely underground, out of reach of Iranian ballistic missiles.

Pediatrics, oncology, ophthalmology, cardiology — all of it had migrated beneath the surface. Ambulances unloaded patients on gurneys in underground bays while workmen fixed overhead cables nearby. “This place is not a field hospital. This is the hospital. It is just subterranean,” one pediatric doctor told me. She said the staff had assumed they might be working this way indefinitely.

After the 12-day Israeli-U.S. war with Iran last June, hospital leadership had studied what they’d learned and built a more distributed underground system — spread across five subterranean locations, each capable of running independently.

What made it possible was the AI-driven systems, which monitor threat levels, coordinate logistics, and help hospital command know when to move, how fast, and in what sequence. The military gave the hospital roughly a month’s notice before the latest air strikes, based on its own predictive modeling. That lead time saved lives.

Predicting Emergencies Before They Happen

Below the level of military operations, Israel has also wired its civilian emergency response system with AI in ways that feel almost sci-fi.

Members of Unit 8200, Israel’s elite signals intelligence unit, collaborated with the emergency response organization United Hatzalah to build an AI dispatch system trained on 18 years of historical emergency data. The system predicts where medical emergencies are likely to occur within the next 10 minutes, dynamically repositioning ambulances and first-response “ambucycles” ahead of actual calls. During a three-month pilot, it correctly predicted the location of the next emergency call — within a one- to two-minute response radius — roughly 85 percent of the time.

The implications are straightforward: faster response times, more lives saved. In one documented case, a volunteer was directed to a mall based on an AI prediction. Before he arrived, his app flagged a patient with chest pains 400 meters away. He reached the patient before the caller even hung up the phone.

Magen David Adom, Israel’s national emergency service, has built a similarly sophisticated system — a Computer-Assisted Design AI platform that simultaneously alerts and dispatches the nearest certified civilian responders, ambulance crews, police, and fire services the moment an emergency call comes in. It routes everyone using real-time traffic data and predictive algorithms, all without a dispatcher manually coordinating each agency.

“What Israel has built in the civilian sector is just as instructive as the military applications,” Elliott Broidy said. “The same logic applies: you reduce the time between a threat and a response, you reduce harm. Whether the threat is a cardiac arrest or a missile, the principle is identical.”

On the Battlefield: AI as Operational Doctrine

The IDF’s adoption of AI goes well beyond logistics. Deep inside a fortified command facility known as the “Fortress of Zion” — a bunker beneath IDF headquarters in Tel Aviv — military planners coordinate air, sea, and land operations from a nerve center built for high-tech warfare.

On its walls of screens: three-dimensional building diagrams, live aerial drone feeds, real-time maps of ground and air assets across the region. During combat operations over Gaza and Lebanon, roughly 300 to 400 personnel worked there around the clock, with that number swelling to the thousands during active campaigns. Intelligence analysts from Mossad, Shin Bet, the Foreign Ministry, and police all shared the same operational picture.

The IDF has used this infrastructure to target enemy assets with a precision that older command structures simply could not achieve. It has also grappled with the serious ethical questions that come with it — the distance between a commander and a target, the reduction of human lives to data points in a system, the risk of indifference that can creep in when war is conducted from a climate-controlled room. Senior IDF officers acknowledge this tension directly. It is, as one general put it, “part of the commander’s challenge.”

On the medical side, the IDF Medical Corps has been running its own AI-driven revolution. An R&D division within the corps reduced evacuation times from the Gaza battlefield to a hospital to an average of 60 minutes — well within what medics call “the golden window” for saving the critically wounded. In Lebanon, where distances were greater, the average was 107 minutes. Paramedics were pre-positioned close to front lines so they could reach wounded soldiers within two to three minutes.

The corps also developed a drone delivery system for whole blood — one of the most time-sensitive medical supplies in combat. Engineers spent years solving the physics: keeping blood at two to four degrees Celsius at altitude, designing landing systems that wouldn’t rupture bags on impact, ensuring drones flying at 200 meters wouldn’t be shot down by friendly forces who didn’t recognize them. By October 2025, a full-scale drill had worked. Operational use was expected shortly after.

AI has also entered the realm of mental health. Using EEG machines paired with machine learning, IDF mental health professionals can now map individualized “calming cues” for soldiers dealing with PTSD, identifying whether exposure to a particular song, voice, or image has a measurable neurological effect. Separately, AI tools are cutting the time clinicians spend on administrative record-keeping from an hour down to about 15 minutes per session, freeing up more face time with patients.

What the U.S. Can Learn

Elliott Broidy has thought carefully about the gap between where Israel operates and where the United States still aspires to be.

“Israel doesn’t have the luxury of bureaucratic timelines,” he said. “When you’re a country of 10 million people surrounded by adversaries on multiple fronts, every tool has to work. That’s created a culture where AI gets deployed, gets tested in the hardest conditions imaginable, and gets refined fast. The U.S. has enormous assets, but we don’t always move with that kind of urgency.”

His firm, LEO Technologies, is trying to apply that urgency in the American public safety context. Through its VerusAI platform, LeoTech brings agentic, semantic, and generative AI to law enforcement agencies — compressing what used to take investigators weeks of manual work down to seconds. The platform monitors communications for contraband coordination, gang activity, and suicide risk in correctional facilities, surfacing the intelligence that matters and filtering out the noise. It currently serves more than 224 agencies nationwide.

“The core insight is the same whether you’re in Tel Aviv or Tulsa,” Broidy said. “AI doesn’t replace human judgment — it gives humans better information, faster, enabling better decisions that can be deployed more quickly. The question is always: what does your team need to know, and how quickly can you get it to them?”

A Country That Builds Under Fire

What I keep coming back to, weeks after returning home, is the sheer normality of AI’s role in Israeli life. Emergency dispatchers rely on predictive algorithms the way American dispatchers rely on phone maps. Hospitals have AI-integrated contingency protocols baked into their operations. The military runs targeting and logistics through systems that would have seemed like science fiction a decade ago.

The pressure that produced this ecosystem — decades of conflict, constant threat recalibration, a small population that cannot absorb large losses — is not something any country would choose. But the technological culture it has built is worth studying seriously. Israel has figured out something that larger, more comfortable nations are still working through: that AI deployed under real conditions, with real stakes, matures faster and proves its worth more clearly than AI developed in peacetime labs.

As Elliott Broidy put it: “Israel is the world’s most advanced proving ground for AI in national security. What works there, works. And what works there today often becomes doctrine everywhere else tomorrow.”

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The Pentagon’s Fight With Anthropic Points To A Deeper Divide In The AI Economy

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This article was originally published in The Washington Independent.

By Tyreece Bauer

Washington’s clash with artificial intelligence startup Anthropic has quickly become one of the most closely watched technology disputes in the country. The Pentagon labeled the company a national security “supply chain risk,” effectively cutting it off from defense work. Anthropic responded with a lawsuit, arguing the move was retaliatory and unlawful. A federal court hearing is scheduled for later this month.

Much of the debate so far has focused on the immediate legal and ethical questions. Anthropic says the government punished it after the company refused to loosen restrictions on how its AI models can be used for surveillance and military applications. Defense officials say the military cannot rely on technology from a company unwilling to support lawful national security missions.

But the dispute also highlights a deeper divide inside the AI economy that is shaping how both government and industry approach artificial intelligence.

The debate is not only about who builds the most powerful models, but increasingly about who controls the systems and data that make those models useful in high stakes environments.

Over the past year, capable AI systems from companies such as Anthropic and OpenAI have fueled predictions that large language models will disrupt large parts of the software industry. Many investors now expect entire categories of software to be absorbed as AI systems replicate and automate core functions.

Those pressures are most visible in software as a service. Many SaaS (Software as a Service) products automate standardized workflows such as customer support, marketing analytics or document processing. These are precisely the kinds of tasks where general AI models can replicate existing tools with relative ease.

“A lot of the commentary assumes every new AI breakthrough wipes out entire sectors of software,” said investor and entrepreneur Elliott Broidy, who has been studying how artificial intelligence is being deployed in national security and investigative technology. “That tends to be true in horizontal markets where software is basically a bundle of interchangeable features. In specialized sectors, the advantage comes from domain expertise and proprietary data.”

In fields like intelligence analysis, financial crime investigations and regulatory compliance, AI systems operate inside operational platforms built on years of industry training data, investigative methods and analytical workflows. In those environments the AI model is only one component of a larger system.

“If you are operating in a complex investigative or national security environment you are not just plugging in a chatbot,” Broidy said. “You are integrating AI into a full investigative framework with proprietary datasets, analyst workflows and regulatory constraints. That is something generic models cannot replicate overnight.”

The distinction is already shaping how government agencies approach AI integration in sensitive systems. Defense agencies and intelligence organizations rely on software platforms that combine large proprietary datasets with specialized analytical tools refined through years of operational use. In those environments the AI model is only one component of a larger architecture.

Because these platforms are built around proprietary data pipelines and investigative workflows, they can integrate whichever AI model proves most effective. The infrastructure and domain expertise remain the core strategic asset.

“The model is important, but it is only one layer of the stack,” Broidy said. “In many cases the real moat is the data infrastructure and the domain expertise that surrounds it.”

The Pentagon’s decision to designate Anthropic a supply chain risk sheds light on another reality. Washington is increasingly willing to use regulatory authority and procurement power to shape how AI companies operate.

The designation applied to Anthropic is typically used against foreign companies that pose national security threats. Applying it to a domestic AI developer represents a striking escalation and forces government contractors to reconsider whether they can rely on the company’s technology.

The move also reflects growing tension between AI developers and government agencies.

Some AI companies have sought to place limits on how their systems can be used, particularly when it comes to surveillance or autonomous weapons. Government officials argue that decisions about national security capabilities ultimately belong to elected governments, not private technology companies.

At the same time, federal agencies are becoming increasingly dependent on the private sector for advanced AI capabilities. This dynamic is reshaping the relationship between Washington and Silicon Valley.

As artificial intelligence becomes embedded in sectors such as investigations, compliance and national security, companies that control specialized data and operational platforms are emerging as critical players in the technology ecosystem. In these environments, organizations prioritize reliability and institutional trust over novelty.

AI is integrated into long-standing workflows and regulatory frameworks, making the model itself only one layer of a much larger system. In that sense, the struggle between Anthropic and the Pentagon is not just about one company or one contract, but reflective of a broader contest over who ultimately controls how powerful AI systems are deployed.

 

 

 

 

 

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Anthropic Unveils AI-Vulnerability Scanner, Rattling Cybersecurity Stocks

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This article was originally published in IT Security Guru. 

By David Soffer

Anthropic has launched Claude Code Security, an AI-powered vulnerability scanner built into Claude Code on the web, now available in a limited research preview for Enterprise and Team customers.

The tool is designed to flag subtle logic flaws that are difficult to detect through manual inspection. Its launch triggered an immediate and sharp sell-off across the cybersecurity sector.

The announcement marks a turning point in how security teams find and fix software flaws. Until now, identifying hidden vulnerabilities meant relying on experienced researchers to comb through thousands of lines of code. That method is no longer the only option and, in many cases, it is no longer the most efficient one. Security tools like Claude Code Security are beginning to upend these traditional systems.

Elliott Broidy, who leads Broidy Capital Holdings and invests in AI-driven capabilities for public safety and defense, believes that Claude Code Security represents a fundamental shift in how we achieve accurate vulnerability detection. “Instead of waiting for a breach to expose a weakness, security teams can now get ahead of threats before they are ever exploited,” he said.

Anthropic’s February 20 blog post, “Making frontier cybersecurity capabilities available to defenders,” highlighted this evolution: “Security teams face a common challenge: too many software vulnerabilities and not enough people to address them. Existing analysis tools help, but only to a point, as they usually look for known patterns. Finding the subtle, context-dependent vulnerabilities that attackers often exploit requires skilled human researchers, who are dealing with ever-expanding backlogs. AI is beginning to change that calculus.”

Broidy also sees both sides of the equation clearly. “AI gives human researchers a force multiplier they’ve never had before, which is a game-changer for national security and the private sector alike,” Broidy said. “However, the same capabilities that can find a vulnerability in seconds can be weaponised to exploit one just as fast. Tools like Claude Code Security enable defenders to move at the speed of the threat for the first time.”

Unlike conventional scanners that flag known patterns, Claude Code Security uses reasoning to understand how software components interact with one another. Any vulnerability it identifies undergoes a multi-stage verification and reexamination process before reaching a human reviewer. Validated findings are then assigned a confidence rating to help security teams inspect and approve necessary fixes.

“Claude Code Security is meant to simulate the real process a human security researcher would follow when evaluating code,” said Broidy. “It can read the company’s software, flag potential issues and draft fixes. Ultimately, though, humans should make the final decisions.”

Markets responded swiftly following the announcement. According to Bloomberg, Crowdstrike Holdings fell 8%, while Cloudflare Inc. dropped 8.1%. Zscaler declined 5.5%, SailPoint tumbled 9.4% and Okta Inc. fell 9.2%. The broader selloff dragged down the Global X Cybersecurity ETF by 4.9%, leaving it at its lowest close since November 2023.

The selloff reflects a broader investor concern: that AI platforms capable of detecting and remediating vulnerabilities at machine speed could compress demand for legacy suites.

“The message from markets was hard to miss,” Broidy said. “Investors are now asking an important question. As AI companies like Anthropic and OpenAI continue rolling out tools that can write and scan software code, the pressure on traditional security vendors will only grow.”

Investor concerns have been brewing. The tool builds on over a year of internal research and testing. Claude Opus 4.6, released earlier this month, helped the team find hundreds of previously undetected vulnerabilities. It also stirred up panic and triggered broad selloff across enterprise software stocks as investors fear the sustainability of traditional enterprise software companies.

As an AI code vulnerability scanner, Claude Code Security is a signal of where the industry is heading. The models are only becoming more advanced as the race to give defenders the edge in an increasingly sophisticated threat landscape accelerates.

“There’s no question that AI is becoming the backbone of how we protect critical infrastructure,” Broidy said. “We should be excited to integrate these capabilities while remaining hyperaware of the continued risks it poses.”

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Anthropic launches Claude Opus 4.6 as AI market faces investor uncertainty

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This article was originally published in Tech Announcer.

By Hugh Grant

If anything was to be gleaned from last week’s Super Bowl ads, it’s that the AI war has officially become mainstream. Only days after launching new products (on the very same day, no less), both Anthropic and OpenAI aired primetime spots during the most important advertising moment of the year — a reminder that this race is now measured in hours, not quarters.

As artificial intelligence continues to reach and reshape global markets, the gap between rapid innovation and investor unease is becoming increasingly apparent. That tension was on full display last week as Anthropic unveiled Claude Opus 4.6, its most advanced AI model to date, even as concerns over AI disruption, valuation risk, regulation, and economic volatility ripple through public markets.

Anthropic describes Opus 4.6 as its “most capable” model for business and knowledge work, emphasizing improvements in reasoning, reliability, and the ability to handle complex, multi-step tasks. The launch draws attention to a broader trend, as AI development continues to gain momentum even amid market volatility and investor uncertainty

Still, the market backdrop has become harder to ignore. Investors have increasingly questioned whether escalating AI infrastructure spending will translate into near-term earnings, especially as companies guide to higher capital expenditures for data centers and AI workloads. At the same time, regulatory compliance costs and liability questions are coming into sharper focus, particularly in the EU, where the AI Act introduces tiered obligations and significant potential fines for certain violations, adding another layer of uncertainty to how quickly and broadly frontier models will be deployed in high-stakes settings.

For Elliott Broidy, who leads Broidy Capital Holdings and invests in AI-driven capabilities tied to public safety and defense, the moment is indicative of a larger trend of rapid technological progress moving ahead, even as markets reassess the risks.

“Every major technological shift brings uncertainty to markets,” Broidy explains. “But uncertainty has not led to retreat. The technology continues to reshape fundamental work processes.”

According to Broidy, models like Claude Opus 4.6 represent a maturation of AI from experimental tools into enterprise-grade infrastructure.

“Businesses have moved beyond seeking novelty,” he says. “They demand dependable systems that can reason, synthesize information, and support real decision-making at scale.” Anthropic’s positioning of Opus 4.6 squarely toward business and knowledge work reflects the industry’s move from demos to deployment. The company has increasingly prioritized enterprise customers over consumer-facing applications to enterprises that rely on large volumes of data, complex analysis, and high-stakes judgment. These include industries such as finance, law, logistics, government, and national security.

“When an AI model can assist analysts, operators, and leaders in making faster, better-informed decisions, it becomes a strategic asset,” Broidy noted.

The launch also spotlights a growing divide between short-term market reactions and long-term technological developments. While investors worry about overvaluation, job displacement, and regulatory risk, companies and governments are steadily embedding AI deeper into their core operations.

“In national security and public safety, hesitation is not an option,” Broidy says. “Adversaries continue to adopt AI aggressively regardless of market sentiment, compelling the United States and its allies to maintain pace.”

Broidy points out that many of the most impactful AI deployments occur far from public view. Advanced language models are already being used to assist intelligence analysis, process vast streams of open-source information, generate operational reports, and support training simulations. In defense contexts, reliability and controllability are central concerns because official guidance stresses responsible and secure deployment.

“Instilling confidence is essential,” Broidy explains. “Whether it’s a defense analyst or a corporate executive, the user needs to be certain that the system is accurate, explainable, and aligned with human intent.”

The broader lesson of Anthropic’s launch is that AI fears and AI adoption can coexist, and often do. Market volatility may dominate headlines, but behind the scenes, organizations are making long-term bets on platforms that can deliver durable advantages.

“We are witnessing the industrialization of artificial intelligence,” Broidy says. “Models like Claude Opus 4.6 represent a shift toward embedding intelligence into the fabric of institutions.”

Looking ahead, Broidy believes the companies that succeed will be those that balance innovation with discipline. “Success will come to those building systems that businesses, governments, and societies can rely on when the stakes are high,” he says.

The launch of Claude Opus 4.6 signals this transition. AI’s role as foundational infrastructure is no longer theoretical. It is here, evolving rapidly, and reshaping how decisions are made, regardless of how rattled markets may feel at the moment.

Agentic AI Core Technology Background

Agentic AI Cybersecurity: Navigating Privacy Threats and Defense Strategies in 2026

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This article was originally published in Smartech Daily. 

By Spencer Hulse

Agentic AI cybersecurity represents a fundamental shift in how organizations must defend against digital threats. Unlike traditional generative AI tools that require human direction, agentic AI systems independently plan, execute, and adapt their actions, creating an entirely new attack surface that challenges conventional cybersecurity defenses. These autonomous AI agents are reshaping both offensive and defensive strategies across the cyber landscape.

Historically, cyber threats have required significant manual effort and technical skill. AI has made the process much quicker by automating and accelerating various malicious activities, executing thousands of actions simultaneously and at machine speed. Now, attackers can go after everyday tools such as firewalls, browser add-ons, and smart TVs to turn small cracks into serious breaches. AI models can follow complicated instructions, understand nuanced context, and act as autonomous agents with minimal human input.

The Scale of Automated Attacks

AI-assisted data breaches have significantly increased in frequency. According to Fortinet’s FortiGuard Labs 2025 Global Threat Landscape report, the number of malicious scans conducted by attackers increased by 16.7% from 2024, with cybercriminals conducting approximately 36,000 malicious scans per second. The report recorded over 97 billion exploitation attempts during the year, reflecting “increased automation and broader targeting across industries.”

“The consequences of cyber attacks, including financial losses, reputational damage, and operational disruption, can be catastrophic for companies,” said Elliott Broidy, Chairman and CEO of Broidy Capital Holdings, LLC, a seasoned entrepreneur and investor with extensive experience in national security technology and defense tech. “Business leaders must pay close attention to the ways in which artificial intelligence will shape cybersecurity in 2026,” he said, citing a 2025 report from the World Economic Forum that found 1 in 3 CEOs consider cyber espionage and the theft of sensitive information or IP a top concern.

In November 2025, Anthropic reported that hackers believed to be sponsored by the Chinese government were able to access the AI chatbot Claude and have it carry out automated cyber attacks against approximately 30 global organizations, including:

  • Tech companies
  • Financial institutions
  • Chemical manufacturers
  • Government agencies

This marked the “first reported AI-orchestrated cyber espionage campaign,” which relied on AI features such as intelligence, agency and tools that are currently much more advanced than they were a year ago. The attackers bypassed Claude’s safety features by tricking the chatbot into thinking it was performing defensive cybersecurity tasks for a legitimate company and breaking down suspicious requests into smaller tasks. The campaign executed about 80-90% of its operations autonomously at an attack speed that would have been impossible for human hackers to match.

Regulatory Response and New Frameworks

In 2025, a new Data Security Program (“DSP”) went into effect, issued by the Department of Justice (DOJ). The DSP “prohibits or restricts the provision of U.S. bulk ‘sensitive’ personal data and U.S government-related data to ‘countries of concern.’” The National Institute of Standards and Technology (NIST) expects to finalize new AI cybersecurity guidelines in 2026. The preliminary draft, titled Cybersecurity Framework Profile for Artificial Intelligence, discusses how businesses can safely integrate AI into their workflows.

“As AI becomes more sophisticated by the day, companies should be aware of the third-party risks this technology can pose,” said Broidy. “Keeping up with new privacy and cybersecurity laws—intended to protect valuable data and individual privacy—is imperative.”

The Evolution of the Threat Landscape

Intrusions centered around social engineering and identity abuse are skyrocketing. Generative AI can create personalized, relevant phishing emails that become increasingly difficult for everyday internet users to distinguish between and flag. These AI cybersecurity threats manifest through:

  • Multifactor authentication bypass attempts
  • Employee-facing help desk impersonation
  • Sophisticated user consent manipulation

Attackers then trick LLMs into leaking internal data through prompt injection attacks, which the OWASP Top 10 2025 for LLM Applications ranks as the #1 critical vulnerability. These attacks appear in over 73% of production AI deployments assessed during security audits.

“With great innovation comes great risk,” said Broidy. “As AI advances by the day, leading to more opportunities for nefarious actors, finding impactful solutions to malicious AI use is paramount. Organizations cannot afford to treat AI security as an afterthought. It must be embedded into every layer of their cybersecurity strategy.”

Building AI Resilience

Cybersecurity has become a C-suite priority that dictates business success. Traditional defenses are no longer adequate in the face of machine-speed attacks and autonomous AI agents. Organizations must implement multi-layered defenses, continuously monitor for new attack vectors, and maintain realistic expectations about the current state of AI security.

“The organizations that thrive in 2026 will be those that effectively leverage the power of AI agents while proactively managing the associated risks,” said Broidy.  “Businesses must place identity at the center of their security strategies to build a resilient foundation that allows them to innovate with confidence.”

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