Impact Of Technology On Defense

🗞️ A must-have for anyone teaching Russian disinformation tactics. A comprehensive yet highly pedagogical and illustrated catalogue of tactics with concrete examples. 👏🏼Well done @center for countering disinformation with the support of The European Union Advisory Mission Ukraine (#EUAM Ukraine) 🇪🇺 1️⃣ The first part is dedicated to the Mechanisms of destructive information influence: • Bots 🤖 • Fake accounts 🤳🏻 • Anonymous authority 👁️ • Appeal to authority 🔨 • Deepfakes 👾 • Potemkin villages 🤡 • Duplicating websites or accounts 👨🏻💻 • Framing 🖼️ • Information overload 🌧️ • Agenda-setting 📆 • Demonisation • Polarisation 🤯 • Confirmation bias 🧠 • Primacy effect 🪢 • Deceptive sources 🎭 • Information alibi 🥸 2️⃣ The second part offers an overview of the Tactics of destructive information influence. Particularly useful to identifies the perverse rhetorical tricks at play and counter them with the right arguments: • Clickbaiting • Rating • Information sandwich • Lost in translation • Presence effects • Contextomy • Gish gallop • Whataboutism • Conspiracy theories • Talking away • Mundanisation • Doublespeak • Sleeper effect • “Check it if you can” • False analogy • Trolling • False dilemma • Using jokes or memes • Stereotyping 3️⃣ The last part describes the various soft power tools weaponized to leverage influence : Soft power tools: Russia’s influence through… • films 🎦 • e-sports 🎮 • literature 📕 • music 🎶 • sports ⚽️ • churches ⛪️ • cultural centre networks 🤝🏻 • educational programmes and grants 🎓 • historical revisionism 🖊️ • loyal political structures🏰 👐🏻Many thanks to the authors for a reference document which deserves to be widely shared As someone who srudied humanities, I always longed for the ancient “class of rhetorics” which was, until the late 19th century, the penultimate year of secondary education in France before philosophy: students learned the full art of persuasion—finding ideas, structuring them, refining style, memorizing, and delivering speeches—through constant practice and study of classical models. The purpose was to train them in the art of eloquence—to speak and write clearly, elegantly, and persuasively. And to prepare future orators -lawyers, priests, politicians- as well as any educated citizen. Were this classical knowledge more widely shared today, we might be better equipped to resist the tactics outlined in part 2️⃣ as we would more spontaneously recognize the persuasion strategies used against us -even if they come in alluring video forms these days! - and be able to counter them with the tools of logic and structured argument.

None will ever be able to stop Shadow AI! When people find an AI tool that helps them work faster, think better, or save time, they use it. That is what good people do. They look for leverage. Banning this is not a strategy. It is just an invitation to hide it, which is worse. So, what can we do? In my discussion with Stephen Schmidt, Chief Security Officer at Amazon, one message came through very clearly: 👉 The role of security is no longer to stop Shadow AI—because it is impossible. 👉 The role of security is to make AI safe, visible, and controlled. 👉 To know what is being used, where it is installed, what it can access, and where the data goes. That is the real shift. Because the biggest danger with AI is often not the intelligence. It is the permission. The moment an agent gets broad access to your files, systems, or sensitive data, your risk changes completely. So the question is not: “How do we stop Shadow AI?” The question is: “How do we make sure AI does not operate in the shadows?” That means four things: 1️⃣ Visibility: Create an inventory of the AI tools and agents people are actually using. 2️⃣ Boundaries: Run agents in isolated environments, not freely on laptops or production systems. 3️⃣ Permissions: Give agents only the minimum access they need, nothing more. 4️⃣ Traceability: Log actions so you know what the agent did, what data it touched, and who triggered it. This is where many leaders get it wrong. They think control means restriction. It does not. Real control means creating an environment where AI can be used fast, safely, and in the open. The companies that try to ban AI will lose visibility. The companies that learn to govern it will gain trust, speed, and advantage. You cannot stop Shadow AI. But you can stop unmanaged AI. 💥 Curious to learn more: https://lnkd.in/eubr-VpH How is your organization dealing with this today? #AWSAmbassador #AI #AgenticAI #Cybersecurity #Leadership #FutureOfWork

FortiGate admins report active exploitation 0-day. Vendor isn’t talking. Vulnerability allowing remote code execution has been discussed since at least 9 days ago. Fortinet, a maker of network security software, has kept a critical vulnerability under wraps for more than a week amid reports that attackers are using it to execute malicious code on servers used by sensitive customer organizations. Fortinet representatives didn’t respond to emailed questions and have yet to release any sort of public advisory detailing the vulnerability or the specific software that’s affected. The lack of transparency is consistent with previous zero-days that have been exploited against Fortinet customers. With no authoritative source for information, customers, reporters, and others have few other avenues for information other than social media posts where the attacks are being discussed. According to one Reddit post, the vulnerability affects FortiManager, a software tool for managing all traffic and devices on an organization’s network. Specific versions vulnerable, the post said, include FortiManager versions: 7.6.0 and below 7.4.4 and below 7.2.7 and below 7.0.12 and below 6.4.14 and below Users of these versions can protect themselves by installing versions 7.6.1 or above, 7.4.5 or above, 7.2.8 or above, 7.0.13 or above, or 6.4.15 or above. There are reports that the cloud-based FortiManager Cloud is vulnerable as well. https://lnkd.in/ge692gH2 #cybersecurity #Fortinet #Fortigate #0Day

OpenAI is co-designing chips with Broadcom, aiming to mass-produce its first proprietary AI chip by 2026. At first glance, it sounds tactical - leverage against Nvidia in GPU negotiations. But it’s quite existential - vertical integration with a side of paranoia. Because whoever controls the silicon controls the future of AI. Google figured this out a decade ago. In 2015, they built the Tensor Processing Unit, or TPU, chips purpose-built for AI. Not graphics, not gaming. Just raw, industrial-scale matrix math. Over the years, TPU clusters have become Google’s quiet superpower: invisible, efficient, and deeply integrated with their software stack. As a result, Gemini runs on a proprietary engine no one else can touch. Benchmarks suggest TPUs deliver up to 3x better performance per watt vs. Nvidia GPUs. At hyperscale, this means billions saved and megatons less carbon. The signal landed. Amazon built Trainium and Inferentia. Microsoft rolled out Maia. Meta got Artemis. And now OpenAI - the company consuming more compute than God - wants to own its silicon future. Why custom chips make sense: ▪️ Economics: Each model generation costs more to train. GPUs are blunt instruments; custom silicon is a scalpel. Efficiency gains at scale don’t just improve margins - they decide whether you can afford GPT-6 at all. ▪️Control: Relying on Nvidia is like running your country on imported oil. It works … until it doesn’t. ▪️Integration: When the chip and the software stack are designed together, the system hums like a Porsche engine. Proprietary stacks create lock-in gravity fields. ▪️Geopolitics: Supply chains fray, export controls bite, and chips are the new oil fields. No hyperscaler wants its future hostage to Jensen Huang’s waitlist. This is why every hyperscaler is in the chip game. And why OpenAI has no choice but to join. Google’s TPU bet shows where this path leads. For years, TPUs have been velvet-roped behind Google Cloud. Want access? Bring your data, your workloads, and your spend - and surrender to the ecosystem. The chip wasn’t just infrastructure; it was the carrot pulling customers into GCP. Now comes the phase shift. Earlier this week, The Information reported Google has begun seeding TPUs into other cloud providers’ data centers. They’ve already struck a deal with London-based Fluidstack to host TPUs in New York, and are talking to others. The reason is simple: Google can’t build data centers fast enough to house its own silicon, and distribution has become the release valve. This puts Google in even more direct competition with Nvidia. NVIDIA remains king - but surrounded by revolutionaries hammering together guillotines in their garages. The market gets the hint: Broadcom stock spiked, Nvidia slipped, and the age of custom AI silicon just accelerated.

Micro drones are no longer niche tools — they are becoming a core pillar of surveillance, security, and tactical intelligence across defense, public safety, and critical infrastructure. Have you seen this one? What’s remarkable is not just the capability — it’s the speed of evolution. 📈 The Numbers Behind the Momentum • The global micro-drone market is growing at 16–19% CAGR, with forecasts projecting: • From ~$10B in 2024 to over $24B by 2029 • Small UAV market expected to exceed $11B by 2030 • Defense and surveillance account for one of the largest and fastest-growing segments due to: • Border security expansion • Urban surveillance demand • ISR (Intelligence, Surveillance, Reconnaissance) modernization 🧠 What Changed the Game? Modern micro drones now combine: • AI-powered navigation & object recognition • Real-time video transmission • Autonomous flight and obstacle avoidance • Swarm coordination capabilities • Ultra-miniaturized thermal + optical sensors Some nano-drones weigh under 20 grams, fly for 20–25 minutes, and transmit encrypted HD video over 1.5–2 km, all while operating with extremely low acoustic signatures. This level of capability was military-exclusive just a few years ago. Today, it’s rapidly becoming standard Micro surveillance drones are now actively used for: • Tactical reconnaissance in conflict zones • Law enforcement situational awareness • Crowd monitoring & perimeter security • Disaster response in collapsed or dangerous environments • Critical infrastructure inspection (energy, transport, telecom) At the tactical level, they allow frontline units to “see first” before entering hostile or uncertain environments — reducing risk and improving decision speed. 🤖 The Rise of Swarm Intelligence One of the most disruptive developments is coordinated micro-drone swarms: • Multiple drones operating as a single intelligent system • Real-time terrain mapping • Autonomous target identification • Dynamic mission adaptation This shifts surveillance from isolated viewpoints to distributed intelligence networks in the air. ⚠️ The Strategic Challenge With power comes responsibility. Micro drone surveillance forces critical conversations around: • Privacy and civil liberties • Airspace governance • Ethical deployment • Counter-drone defense systems • Digital sovereignty At the same time, governments and enterprises are investing heavily in anti-drone and RF-neutralization technologies, signaling that the drone vs counter-drone race has already begun. #Drones #SurveillanceTechnology #DefenseTech #AI #AutonomousSystems #SecurityInnovation #FutureOfSurveillance

I'm convinced the Forward Deployed Engineer role will continue to grow. The hardest part of Enterprise AI isn't the model. It’s the "Head Knowledge" transfer. As a Forward Deployed Engineer, my job isn't just to write code; it’s to extract the implicit rules living in the heads of Subject Matter Experts (SMEs) and turn them into logic an agent can actually act upon. Here’s the reality: You can’t just quote "LLM benchmarks" to a guy running a 1000° aluminium smelter. Why? Because they know the stakes. You can't trust an LLM to decide the quality of an anode when a mistake means a catastrophic failure. They don't want to hear your "bullish" predictions; they want to see your proof. SMEs will call out your bullshit in seconds. The only way to win in this role is to build trust through consistency and hard data. You prove the work, you show the edge cases, and you respect the "head knowledge" that’s kept that factory running for 30 years. I’m bullish on the FDE role because we are the bridge. We don't just talk about AI but we make sure it survives in real factories. PS: If you're interested about this role and what tradecrafts make a good FDE, I'm thinking about collecting all this knowledge together. Of course, I'm still learning but this will come out soon.

𝗨𝗸𝗿𝗮𝗶𝗻𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝗻𝗲 𝗱𝗿𝗼𝗻𝗲 𝗶𝗻𝘁𝗲𝗿𝗰𝗲𝗽𝘁𝗼𝗿. 𝗜𝘁 𝗶𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻 𝗮𝗶𝗿-𝗱𝗲𝗳𝗲𝗻𝗰𝗲 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺. 🛩️ Brave1 CEO Andrii Hrytseniuk has described a Ukrainian interceptor-drone ecosystem that is moving far beyond a single “anti-Shahed” design, with more than 150 companies reportedly working on interceptor solutions inside a defence-tech cluster that now includes thousands of firms. The important signal is architectural diversity. Ukraine is not betting everything on one platform, one supplier or one technical answer. It is building a layered family of small FPV-derived interceptors, fixed-wing designs, larger loitering systems, X-wing hybrids, high-speed variants, endurance-focused platforms and specialised systems for different target sets, from reconnaissance UAVs and decoys to heavy Shahed-type attack drones. That matters because #DroneWarfare is now a cost-curve fight. A Shahed should not always require an expensive missile, and a decoy should not always consume a premium interceptor. Ukraine’s answer is to build many cheaper layers that can match the threat more intelligently, preserve scarce air-defence missiles and turn industrial speed into defensive depth. ⚙️ The autonomy debate is just as important. Hrytseniuk reportedly points to a human-on-the-loop model, where a human retains the authority to cancel or block action but does not necessarily approve every intercept in real time. That is a major shift, driven by reaction speed against mass drone attacks, but it also raises the central question every military will face: how much autonomy is acceptable when seconds decide whether a city, power plant or airbase is hit? For #Ukraine, the lesson is brutally practical. Air defence is no longer only a question of radars, launchers and missiles; it is becoming a software-defined, mass-manufactured, continuously updated kill web where startups, soldiers, volunteers and state platforms iterate together under fire. In #ModernWarfare, the country that can adapt the interceptor faster than the enemy adapts the drone begins to change the economics of the sky. 𝘛𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦 𝘰𝘧 𝘢𝘪𝘳 𝘥𝘦𝘧𝘦𝘯𝘤𝘦 𝘮𝘢𝘺 𝘯𝘰𝘵 𝘣𝘦 𝘰𝘯𝘦 𝘱𝘦𝘳𝘧𝘦𝘤𝘵 𝘮𝘪𝘴𝘴𝘪𝘭𝘦. 𝘐𝘵 𝘮𝘢𝘺 𝘣𝘦 𝘢 𝘵𝘩𝘰𝘶𝘴𝘢𝘯𝘥 𝘪𝘮𝘱𝘦𝘳𝘧𝘦𝘤𝘵 𝘥𝘳𝘰𝘯𝘦𝘴 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘯𝘨 𝘧𝘢𝘴𝘵𝘦𝘳.

𝗛𝗼𝘄 𝗜 𝗨𝘀𝗲 𝗧𝗵𝗿𝗲𝗮𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘁𝗼 𝗦𝘁𝗮𝘆 𝗔𝗵𝗲𝗮𝗱 𝗼𝗳 𝗔𝘁𝘁𝗮𝗰𝗸𝘀 🔍⚡ Last quarter, we almost missed it. It didn’t start with an alert. No high-severity incident. No obvious malware. Just a single line in a log — a failed login attempt from an IP that looked ordinary. But something felt off. 🔍 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗿𝗲𝗮𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴... Earlier that day, I had read a deep-dive from a security researcher 🧠 about a new attack pattern: 👉 Low-and-slow credential spraying 👉 Geo anomalies that bypass basic rules 🌍 👉 Minimal noise, maximum stealth That “normal” IP? It matched a freshly reported indicator. 🧠 𝗦𝗼 𝗜 𝗳𝗼𝗹𝗹𝗼𝘄𝗲𝗱 𝘁𝗵𝗲 𝘀𝗶𝗴𝗻𝗮𝗹... 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗻𝗼𝗶𝘀𝗲 Instead of waiting for alerts: 👉 Pulled logs across VPN, IAM, endpoints 🖥️ 👉 Enriched the IP with threat intel feeds 📡 👉 Mapped behavior to MITRE ATT&CK 🧩 👉 Built a hypothesis: early-stage access attempt Then I started hunting 🎯 And found more… Same pattern. Multiple users. Silent attempts. ⚙️ 𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗹 𝗰𝗮𝗺𝗲 𝗳𝗿𝗼𝗺 This wasn’t luck 🍀 — it was a system: 👉 Open-source intel (blogs, GitHub, researcher reports) 🌐 👉 Commercial feeds (real-time IOCs & adversary infra) 📊 👉 Dark web monitoring (credential leaks & chatter) 🕶️ 👉 Industry groups & sharing communities 🤝 Each source = a piece of the puzzle Together = the full picture 🧠 🚨 🚨 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗱𝗶𝗱 𝗻𝗲𝘅𝘁 👉 Blocked malicious IP ranges 🚫 👉 Forced password resets 🔑 👉 Tuned detections based on TTPs ⚙️ No breach. No escalation. No damage. 💡 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 Attackers don’t kick the door down 🚪 They test it quietly first… If you rely only on alerts, you’re already behind ⏳ Threat intelligence helps you move from: ➡️ Reactive → Proactive ➡️ Alerts → Anticipation 𝗦𝗶𝗻𝗰𝗲 𝘁𝗵𝗲𝗻, 𝗺𝘆 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: 👉 Focus on behavior (TTPs), not just IOCs 🎯 👉 Build continuous intel feedback loops 🔄 👉 Hunt with context, not guesswork 🔍 You can use ANYRUN to Speed up and simplify alert triage, incident response, and threat hunting with Threat intelligence Lookup -> https://lnkd.in/gFD8DPJ3 Have you ever stopped an attack early because of threat intel? 🤔 #CyberSecurity #ThreatIntelligence #SOC #ThreatHunting #BlueTeam #InfoSec #CyberDefense For daily cybersecurity updates, follow: Kaaviya Balaji

The Navy just signaled the biggest organizational shift in unmanned warfare. A dedicated Robotic Autonomous Systems Commander. CNO Adm. Daryl Caudle dropped the concept at WEST 2026 on February 10. Not a formal program yet. A strategic signal. The problem is clear. Billions flowing into USVs, UAVs, and UUVs. No standardized command structure to deploy them at scale. Current state: fragmented unmanned ops spread across strike groups with no unified authority. Future state: a RAS Commander who packages autonomous capabilities across air, surface, and undersea domains for combatant commanders. The 500-ship hybrid fleet vision depends on this. Three implications for defense contractors. 1. Multi-domain integration wins. Systems that talk to each other across domains get priority. Stovepiped solutions lose. If your USV can't coordinate with aerial drones and undersea platforms through a common architecture, you're building for yesterday's Navy. 2. AI-driven autonomy is mandatory. The Navy wants to flip from "one UxS, multiple operators" to autonomous systems that reduce manpower requirements. Contractors still pitching operator-heavy solutions are pitching to the wrong era. 3. The workforce signal matters. The new Robotics Warfare Specialists rating means the Navy is building organic expertise to evaluate, operate, and demand better unmanned systems. Informed buyers are harder to impress with marketing. The timeline is undefined. But CTF 66 is already launching RAS operations in 6th Fleet. Experiments are transitioning to operations. When the RAS Commander role formalizes, it won't be a concept anymore. It'll be a customer with authority and budget. ---------- Like this content? Join our newsletter. Link located below my name 👆