InfosecTrain

Are we already living in the age of super-intelligence, or are we just scratching the surface? In this episode, we break down the three fundamental levels of AI: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI).
We explore why today’s most advanced tools, like ChatGPT, Gemini, and Claude, are still firmly in the "Narrow" category, representing only 20% of human cognitive capacity. We also discuss the "Data Decline" crisis, where authentic human data is being outpaced by AI-generated content, and what that means for the future of AGI. Whether you’re a tech enthusiast or an Infosec professional, this episode will help you categorize, evaluate, and ultimately decide which AI tools are worth your trust.
🔍 What You’ll Learn:
The 20% Reality: Why current AI (ANI) is still 80% behind the absolute capacity of human response, despite the global hype.

The 5-Minute Miracle: A comparison of data processing: How a 5-year-old child collects as much data in 5 minutes as an AI model processes in a year.

The Data Hunger Crisis: Why the decline in "authentic data" (down 17%) and the rise in AI-generated content (up 40%) might starve the next generation of AI.

Reactive vs. Limited Memory: Understanding the two core functions of current ANI tools and how "Conversation Memory" dictates the quality of your AI assistant.

Single-Task Limitations: Why current ANI can’t generate an email and an image simultaneously, and how that defines its "narrow" scope.

The AGI Threshold: Moving from "Calculators" to "Humans" what it takes for a machine to write a novel and make coffee with human-like intuition and emotion.

ASI: The Fictional Frontier: Beyond Human Imagination, discussing Nvidia’s yearly reports and the superstitious yet possible rise of Super Intelligence.

The Doctor Strange Test: A quick mental exercise to help you reject or accept new tools based on their actual intelligence category rather than marketing feedback.

🎧 Don't select a tool based on feedback alone. Understand its intelligence level. If you're using ANI for an AGI-level task, you're going to get frustrated. Current AI is just a specialized associate; treat it like one.

If you buy an HP laptop expecting to run Mac OS, you’ve missed the point. In this episode, we explore why the "Model" is the true soul of every AI system. We compare AI models to operating systems, explaining why tools like Microsoft Copilot and ChatGPT might share the same "DNA" but offer vastly different experiences through customization and "skinning."
More importantly, we dive into the Infosec side of the coin: How do global regulations like GDPR and India’s DPDP influence which AI models a corporation should trust? We also touch on the controversy surrounding models like DeepSeek and why the origin of a model's training can be just as important as its performance.

🔍 What You’ll Learn:
The OS Analogy: Why choosing the right AI model is exactly like choosing between Windows, Linux, or Mac OS - it defines the entire capacity of your system.

The Soul of the System: Understanding that the model is the "soul", and the application (like ChatGPT) is just the body.

DNA Sharing: How Microsoft Copilot utilizes OpenAI’s models (and recently Claude Opus 3) while customizing them for official productivity.

Official vs. Personal: Why we use Teams for work and WhatsApp for family, and how AI models are being "skinned" to fit these specific professional roles.

The Key to the Treasure: A cybersecurity perspective on why the model is the most valuable and vulnerable part of the AI stack.

Compliance & Regulations: The critical choice between a GDPR-compliant model vs. others, and why legal frameworks dictate corporate AI adoption.

The DeepSeek Controversy: Analyzing the "most suspicious model" in the market, how it outranked Nvidia but faced scrutiny over its origins.

🎧 The model defines the difference. It doesn't matter how pretty the interface is; if the underlying model doesn't follow your regional regulations, be it GDPR or DPDP, it isn't the right tool for your organization.

Is bigger always better? While Large Language Models (LLMs) like GPT-5 and Gemini 2.5 dominate the headlines, a silent revolution is happening on our devices. In this episode, we explore the rise of Small Language Models (SLMs) and why they are becoming the "Specialists" of the AI world.
We dive into the security risks of centralized cloud infrastructure, the demand for offline AI in corporate environments, and how gadgets like Apple AirPods and Meta Glasses are bringing real-time intelligence to our palms—without the privacy baggage. If you’re a security architect or an AI enthusiast, this session is a roadmap for understanding why "no internet" might just be the best security feature for the next generation of intelligence.

🔍 What You’ll Learn:
The Shift to SLMs: Why the future isn't just about generalists, but specialized "Small Language Models" that run on-device.

Real-Time Translation: A look at how Apple AirPods 3 Pro and Gemini Live are using integrated AI for seamless, offline communication.

The Privacy Responsibility: Asking the hard question: If a cloud breach happens to an AI provider, who is responsible for your data?

Meet the Giants: Identifying current LLMs—GPT-5, Gemini 2.5, Llama 3 (Meta), and Claude 4 (Anthropic)—and their heavy reliance on cloud servers.

The Security Case for Offline AI: Why an "onsite/offline" model is inherently more secure for sensitive company data than virtual machines controlled by third parties.

Models to Watch: Why Phi-3 (Microsoft) and Gemma (Google) are the future of deep learning research.

Budgeting for AI: How CISOs should evaluate AI tools based on specialized department needs rather than general-purpose infrastructure.

Efficiency & Accuracy: Why the output of an SLM is often faster and more accurate for specific tasks (like content generation) than a heavy LLM.

🎧 Nobody needs a heavy infrastructure just to write an email. While LLMs are powerful generalists, SLMs are the specialized workers that provide faster, cheaper, and more secure responses by focusing on exactly what you need and nothing else.

In a world of "Decision Paralysis," which SIM should you choose? In this episode, we dive deep into why Wazuh has become the go-to solution for SOC analysts in 2026. Moving beyond the "injection-based licensing crisis" of traditional tools like Splunk and QRadar, Wazuh offers a unified, open-source platform that combines the "brain" of a SIM with the "guard" of an XDR.
We provide a step-by-step practical look at Wazuh’s architecture, its XML-based detection engine, and a live demonstration of Active Response, where the tool doesn't just detect a brute-force attack but automatically blocks the attacker in real-time.
🔍 What You’ll Learn:
The Paradox of Choice: Navigating the crowded SIM market and why Wazuh is the best entry point for both learning and deployment.

The Licensing Crisis: How Wazuh eliminates the "cost vs. data volume" spike, allowing for unlimited ingestion without financial penalties.

SIM + XDR Unified: Understanding the hybrid power of log correlation, file integrity monitoring (FIM), and vulnerability detection in one pane of glass.

The 4 Pillars of Architecture: A breakdown of the Agent (The Guard), Server (The Brain), Indexer (The Library), and Dashboard (The Lens).

Noise to Signals: How Wazuh translates raw logs into actionable security events using decoders and rule matching.

Decoding XML Rules: Why Wazuh chose a standard XML format over a native query language to lower the barrier for security engineers.

LIVE DEMO: Active Response: Watch a real-world scenario where Wazuh detects an SSH brute-force attack from a Kali Linux machine and triggers a firewall drop.

Wazuh vs. CrowdStrike: Can you replace a tier-one EDR? Strategic advice on using Wazuh for subsidiary monitoring and compliance.

🎧 Wazuh is like the manual car of the security world. While other tools make you a 'clicking monkey', Wazuh gives you full control over the gears, helping you understand the underlying mechanics of an attack so you can be a better defender.

Transitioning from CISSP to the ISSAP concentration? The architecture of security isn't just about building walls; it’s about the visibility of what’s happening within them. In this deep-dive session, we break down the 2026 ISSAP syllabus changes moving from six domains to four and why the exam remains as rigorous as ever.
We focus on the backbone of security architecture: Identity and Access Management (IAM) and Audit Strategy. From defining the roles of an AI-driven SOC to implementing "Just-in-Time" (JIT) access and advanced log management with SIM and SOAR, this episode provides the technical roadmap needed to master Domain 1 of the ISSAP.
🔍 What You’ll Learn:
The New ISSAP Structure: Understanding the shift from 6 domains to 4 and what it means for your study plan.

IAM Architecture Overhaul: Managing digital identities with LDAP, Azure AD, and Identity-as-a-Service (IDaaS) like Okta and Ping Directory.

Role-Based vs. Attribute-Based Access: Why modern IAM relies on contextual attributes (location, device compliance, time) rather than just user IDs.

Mastering Just-in-Time (JIT) Access: How to automate privilege escalation for specific tasks (like VM snapshots) to minimize the attack surface.

The Architecture of Auditing: Determining accounting, forensic requirements, and the "Clipping Level" strategy for log management.

File Integrity Monitoring (FIM): Using tools like Tripwire to alert on unauthorized changes in critical system files and registries.

User Behavioral Analytics (UBA): Identifying "Top 10 Risky Users" by baselining historical activity and flagging anomalies in real-time.

SIM vs. SOAR: When to use traditional event management and when to deploy automated playbooks (Palo Alto, IBM Resilient) for incident response.

ISSAP Exam Practice: A walkthrough of sample questions on risk assessment, NIST frameworks, and the "Peace of Mind" exam retake offer.

🎧 In security architecture, transparency is the ultimate control. Don't just collect logs; curate them. By setting 'clipping levels' and automating response through SOAR, you transform raw data into architectural assurance.