AI in Cybersecurity

Everyone's talking about AI in cybersecurity, but nobody's talking about the reality. I see vendors promising magic. Instant detection. Zero false positives. Perfect prevention. I watch CISOs nod along, writing checks they'll regret, chasing solutions that don't exist. And here's what keeps me up: The junior analysts drowning in alerts, thinking they're falling behind because they can't match the machine's speed. The seasoned pros questioning their worth because some startup promised to replace them with a chatbot and an API key. But I've been in the trenches. I've seen the AI failures. The missed incidents. The false confidence. Here's what nobody tells you: AI isn't replacing security teams. It's exposing how much we still need them. Because when the model fails silently, when the pattern looks wrong, when something just feels off - It's not the AI that catches it. It's the analyst who's seen this before. The engineer who knows the baseline. The architect who spots the impossible. So keep your human instincts sharp. Trust your gut when it whispers. Question the perfect solutions. Because in security, the most dangerous thing aren't the incidents. It's believing we can automate our way to safety. PS - If you enjoy posts like this, checkout my cybersecurity newsletter for more insights - https://lnkd.in/gXDEmmJ6

🚩 National Institute of Standards and Technology (NIST) publishes the Initial Public Draft of the "Transition to Post-Quantum #Cryptography Standards" report The report provides a brief background on the new #PQC standards and how they apply to different technology components. It also discusses migration considerations for different use cases, loike code signing, authentication, network protocols, email and document signing. The document supports hybrid cryptography, which has been a controversial topic. It advances that "NIST will accommodate the use of a hybrid key-establishment mode and dual signatures in FIPS 140 validation when suitably combined with a NIST-approved scheme". Security strength will be defined with the 5 categories used in the PQC standardization process, instead of with security bits. The final, possibly the most interesting, part establishes the transition timelines. In summary: 👉 Public key cryptography: 📆 112 bits of security strength is deprecated after 2030. 📆 All classical public key cryptography is disallowed after 2035. 👉 Symmetric cryptography and hashes 📆 112 bits of security strength disallowed in 2030 (SHA-1, SHA-224) ✔ All NIST-approved symmetric primitives that provide at least 128 bits of classical security are believed to meet the requirements of at least Category 1, hence they will remain valid in the long term.

Agentic AI and the Future of Autonomous Cyber Defense Cybersecurity is entering a new phase—one where the speed, scale, and sophistication of attacks have outgrown the limits of human response. From zero-day exploits to AI-powered phishing campaigns, today’s threat landscape is relentless. Traditional security tools may detect anomalies, but they still depend heavily on human analysts to interpret alerts and coordinate response. In a world where milliseconds matter, that delay can be fatal. Enter Agentic AI—a revolutionary form of artificial intelligence that doesn’t just detect threats, it acts on them. Unlike conventional AI models that operate within static rules and narrow tasks, Agentic AI is context-aware, autonomous, and adaptive. It doesn’t need step-by-step instructions—it understands its environment, learns continuously, and takes proactive security measures in real time. Think of it not as a tool, but as a tireless cyber defender with the intelligence to make split-second decisions. As attackers turn to automation and AI to amplify their offenses, defenders need more than reactive systems—they need a force multiplier. Agentic AI represents that leap. It doesn’t just scale your defenses—it transforms them, turning your security infrastructure into a living, learning, thinking entity that can hunt, analyze, and shut down attacks before they ever make the news. This isn’t science fiction—it’s the next frontier in cybersecurity, and it’s already here. #cybersecurity #AIinSecurity #AgenticAI #AutonomousSecurity #AIThreatDetection #CyberDefense #SecurityAutomation #AIvsCybercrime #Infosec #AITools #ThreatHunting

Automation isn’t a luxury in cybersecurity, it’s a necessity to stay ahead of attacks… The concept of the Autonomous SOC isn’t: ❌ A human-free fortress   ❌ An unreachable fantasy   ❌ Instantly operational   ❌ A replacement for experts   ❌ An all-or-nothing deal  But, it is: ✅ A journey of evolving automation   ✅ A partnership with AI   ✅ A way to empower your team  ✅ A way to amplify skills & augment work  ✅ A step-by-step progression  Pursuing an Autonomous SOC helps to answer these questions: How can we automate tasks?   How do we enhance response speed?   How does AI support security teams?  That said, let's bust 5 common myths about the Autonomous SOC: Myth 1: The Autonomous SOC Is Fully Automated The Myth: Machines will handle everything without human intervention. The Truth: It's a journey with stages, where machines increasingly augment human work. Example: AI assists in threat detection, but humans make strategic decisions. Myth 2: An Autonomous SOC Means Replacing Human Analysts The Myth: AI will replace jobs in the security field. The Truth: There’s a symbiotic relationship where AI & Automation work  enhances human capabilities, allowing focus on high-value tasks. Example: AI automates data analysis, freeing analysts for strategic planning. Human analysts guide and refine AI. Myth 3: The Autonomous SOC either exists or it doesn't. The Truth: The Autonomous SOC is a journey, ranging from rules-based operations to AI-Assisted SecOps to potentially high autonomy.  Example: Most of the industry today is in Level 2 of AI-Assisted Security Operations, using AI to accelerate hunting, investigation and response Myth 4: The Autonomous SOC Journey Is Only for Large Enterprises The Myth: Only big companies can afford or benefit from it. The Truth: Organizations of all sizes can implement and benefit from stages of autonomy. Example: AI is able to act as a force multiplier for every SOC analyst and for managed security services. Myth 5: Starting the Autonomous SOC Journey Is Years Away The Myth: It's a distant future concept, not applicable today. The Truth: Many tools and technologies are available to start the journey. Example: AI-enhanced detection and response systems are already in use. Don’t let these myths hold you back. Understanding the truth can help you make better decisions and achieve greater success. What's preventing you from starting your Autonomous SOC journey? Learn more here: https://lnkd.in/gu9pUmGN

AIM Research has just Launched its GenAI-Powered Cybersecurity Vendor Landscape Report. The cybersecurity landscape is undergoing a significant transformation with the integration of Generative AI. Here are some key Insights: ✢ Major cybersecurity providers are not just adding GenAI features—they're fundamentally rethinking their platforms to incorporate AI agents, copilots, and context-aware assistants. This shift is moving tools from private previews to public availability, signaling a readiness for broader implementation in 2024. ✢ The industry faces a skill-gap and burnout crisis. GenAI-powered tools are emerging as a solution to alleviate these challenges by handling repetitive and intricate tasks. ✢ Vendors are expanding beyond traditional solutions. We're seeing the rise of AI agents that autonomously monitor and respond to incidents, copilots that assist IT teams in real-time, and platforms that simulate attacks to test and strengthen security postures. ✢ The new wave of tools brings capabilities like intelligent summarization, natural language querying, multilingual conversational functions, proactive security measures, alert prioritization, decision-ready analysis, guided recommendations, and automation. ✢ Vendors are focusing on enhancing functionalities in autonomous threat detection and providing transparency in how AI systems reach conclusions. Access the complete report here: https://lnkd.in/gxj8vY3N Darktrace, Deep Instinct, Dropzone AI, ExtraHop, Fortinet, Mandiant (part of Google Cloud), Prophet Security, Torq, Radiant Security, ReliaQuest, SentinelOne, Simbian, Swimlane, Sysdig, Wiz, Stream.Security, Sysdig, CrowdStrike, Palo Alto Networks, Orca Security, Cisco, ZEST Security, Proofpoint, Aqua Security, Netskope, Dazz, Sweet Security, Zscaler, Sentra, Tenable, Mitiga, Rapid7, Trend Micro, Lacework, Uptycs

𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝗲𝗱𝘂𝗰𝘁𝗶𝘃𝗲. The promise of a hands-off SOC, where AI effortlessly sniffs out threats, is intoxicating. But relying solely on AI for detection and response? That's less "cybersecurity utopia" and more "digital disaster waiting to happen." Here's the harsh truth: AI's brilliance is tethered to its training data. Novel attacks often slip through its grasp. It's like expecting a history textbook to predict tomorrow's news. And let's not forget the deluge of false positives – AI mistaking perfectly normal activity for a full-blown breach. Beyond just identifying anomalies, true security understanding often requires context and nuance that AI struggles to grasp. Developers deploying a new application or pushing a large code update might trigger unusual network patterns and resource consumption. AI can flag these anomalies, but it lacks the insight into planned development cycles and authorized system changes to determine their legitimacy. Over-reliance on its alerts without human validation can lead to wasted resources chasing shadows or, worse, ignoring subtle indicators that a human analyst would recognize as malicious. And then there's the human element: intuition. AI can process data, but it can't think like an adversary. That's where human threat hunters come in, spotting the subtle anomalies that AI, in its data-driven arrogance, overlooks. So, while AI offers undeniable benefits, it's not a substitute for human expertise. It's a tool, a powerful one, but still just a tool. And tools, as we all know, can malfunction, misinterpret, and occasionally decide to stage a digital rebellion. #AISecurity #CyberSecurity

Ken Huang and Chris Hughes have delivered exactly what security professionals need right now. As AI agents move from lab experiments to production systems and new protocols like MCP and A2A are adopted, we’re facing unprecedented security challenges that traditional cybersecurity frameworks simply can’t handle. This book bridges that critical gap with practical, actionable guidance. From the innovative MAESTRO threat modeling framework to Zero Trust architectures for autonomous systems, Huang and Hughes provide the necessary technical foundations to understand how agentic AI works and an actionable tactical playbook every CISO and security architect needs to deploy these systems responsibly. The real-world strategies for critical sectors like finance and healthcare are particularly valuable. If you’re responsible for securing AI systems, this book isn’t optional reading, it’s essential preparation for what’s coming.

I used to think LLMs were just parrots. Then I watched one manipulate someone. I’ll admit it — I was in the “stochastic parrot” camp for a while. Yes, LLMs were impressive. But I saw them as surface-level: just fancy autocomplete with a confident tone. That changed once we started using them for social engineering simulations. Suddenly I was watching a language model adapt mid-call. Handle objections. Switch emotional tone. Create the illusion of understanding the target’s mindset — in real time. It didn’t “understand” in the human sense. But it mimicked it well enough to manipulate. And that’s the part most people are missing. It’s not just about rewriting emails or translating phishing pretexts. It’s about real-time conversation, tuned for pressure, urgency, or rapport — across voice, SMS, or chat. We’re seeing it every day at Arsen Cybersecurity. And every day, it feels one step closer to the way real attackers talk. The risk isn’t that AI gets too smart. It’s that it’s already smart enough. #ai #cybersecurity #socialengineering

Microsoft says cybercrime has reached a level of complexity that human security teams can no longer manage on their own—and AI agents are now stepping in to help. Microsoft is rolling out 11 AI-powered agents designed to help cybersecurity teams stay ahead. These agents can scan millions of emails for phishing attempts, block hacking efforts in real time, and even trace where attacks are coming from. These AI tools will work quietly in the background, focused entirely on keeping large organizations safe. Why now? ►  Last year alone, Microsoft tracked 30 billion phishing emails, which is far too many for human teams to manage. ►  The dark web is flooded with plug-and-play hacking tools—some even written by AI—fueling a $9.2 trillion underground cybercrime economy. Why this matters: Microsoft’s dominant position in enterprise software means this move will be closely watched—especially after last year’s CrowdStrike software glitch caused a global outage on millions of Windows systems. This isn’t just a tech upgrade—it’s a shift in how security will be managed going forward. When the same company that runs most of the world’s enterprise systems says human teams alone can’t keep up, it signals a deeper reality: cybersecurity at scale now depends on machines defending machines. #artificialintelligence #cybersecurity

AI Is Both A Tool And A Target In Cybersecurity. What Skills Do You Need to Stay Relevant in the AI Era ? 1 - AI Governance and Ethics ↳ Understand the ethical use of AI, privacy concerns, and governance frameworks like the EU AI Act or NIST AI Risk Management Framework. Organizations need professionals who can ensure AI systems comply with regulations and align with ethical principles. 2 - Adversarial AI Defense ↳ Cyber-criminals are weaponizing AI for attacks. Learn how to defend against adversarial AI techniques, like poisoning machine learning models or bypassing AI-powered defenses. 3 - Secure AI Systems ↳ AI systems have unique vulnerabilities, such as data manipulation, model extraction, and bias exploitation. Gain expertise in securing AI pipelines, from training data to deployment, and mitigating these risks. To stay ahead, you need to understand how to govern it, secure it, and leverage its capabilities. These skills will not only keep you relevant but position you as a leader in the AI era of cybersecurity. Check out the AWS Generative AI Security Scoping Matrix for more detail on this topic Good luck on your journey!