Many people, in many fields, think Machine Learning won't replace their analysts because their humans are better than an algorithm. But it's not just about side-by-side comparisons. The bigger question is, "what percentage of the data can humans actually look at?", and the answer to that question (a tiny fraction) is the reason ML will be so helpful.

Many people, in many fields, think Machine Learning won't replace their analysts because their humans are better than an algorithm. But it's not just about side-by-side comparisons. The bigger question is, "what percentage of the data can humans actually look at?", and the answer to that question (a tiny fraction) is the reason ML will be so helpful.

Unsupervised Learning is about ideas and trends in Cybersecurity, National Security, AI, Technology, and Culture—and how best to upgrade ourselves to be ready for what's coming.

Unsupervised Learning is about ideas and trends in Cybersecurity, National Security, AI, Technology, and Culture&mdash;and how best to upgrade ourselves to be ready for what's coming.

Exploring the fascinating intersection of security, technology, and humans.

Unsupervised Learning

The Biggest Advantage in Machine Learning Will Come From Superior Coverage, Not Superior Analysis

➡ Secure what your business is made of with Martial Security: 
https://material.security/

In this episode, I speak with Patrick Duffy from Material Security about modern approaches to email and cloud workspace security—especially how to prevent and contain attacks across platforms like Google Workspace and Microsoft 365.

We talk about:

• Proactive Security for Email and Cloud Platforms
How Material goes beyond traditional detection by locking down high-risk documents and inboxes preemptively—using signals like time, access patterns, content sensitivity, and anomalous user behavior.

• Real-World Threats and Lateral Movement
What the team is seeing in the wild—from phishing and brute-force attacks to internal data oversharing—and how attackers are increasingly moving laterally through cloud ecosystems using a single set of compromised credentials.

• Customizable, Context-Aware Response Workflows
How Material helps teams right-size their responses based on risk appetite, enabling fine-grained actions like MFA prompts, access revocation, or full session shutdowns—triggered by dynamic, multi-signal rule sets.

Subscribe to the newsletter at:
https://danielmiessler.com/subscribe

Join the UL community at:
https://danielmiessler.com/upgrade

Follow on X:
https://x.com/danielmiessler

Follow on LinkedIn:
https://www.linkedin.com/in/danielmiessler

Chapters:

00:00 - Welcome & High-Level Overview of Material Security
02:04 - Common Threats: Phishing and Lateral Movement in Cloud Office
05:30 - Access Control in Collaborative Workspaces (2FA, Just-in-Time, Aging Content)
08:43 - Connecting Signals: From Login to Exfiltration via Rule Automation
12:25 - Real-World Scenario: Suspicious Login and Automated Response
15:08 - Rules, Templates, and Customer Customization at Onboarding
18:46 - Accidental Risk: Sensitive Document Sharing and Exposure
21:04 - Security Misconfigurations and Internal Abuse Cases
23:43 - Full Control Points: IP, Behavior, Classification, Sharing Patterns
27:50 - Integrations, Notifications, and Real-Time Security Team Coordination
31:13 - Lateral Movement: How Attacks Spread Across the Workspace
34:25 - Use Cases Involving Google Gemini and AI Exposure Risks
36:36 - Upcoming Features: Deeper Remediation and Contextual Integration
39:30 - Closing Thoughts and Where to Learn More

➡ Secure what your business is made of with Martial Security:&nbsp; <a href="https://material.security/">https://material.security/</a>
In this episode, I speak with Patrick Duffy from Material Security about modern approaches to email and cloud workspace security&mdash;especially how to prevent and contain attacks across platforms like Google Workspace and Microsoft 365.
We talk about:
&bull; Proactive Security for Email and Cloud Platforms How Material goes beyond traditional detection by locking down high-risk documents and inboxes preemptively&mdash;using signals like time, access patterns, content sensitivity, and anomalous user behavior.
&bull; Real-World Threats and Lateral Movement What the team is seeing in the wild&mdash;from phishing and brute-force attacks to internal data oversharing&mdash;and how attackers are increasingly moving laterally through cloud ecosystems using a single set of compromised credentials.
&bull; Customizable, Context-Aware Response Workflows How Material helps teams right-size their responses based on risk appetite, enabling fine-grained actions like MFA prompts, access revocation, or full session shutdowns&mdash;triggered by dynamic, multi-signal rule sets.
Subscribe to the newsletter at: <a href="https://danielmiessler.com/subscribe">https://danielmiessler.com/subscribe</a>
Join the UL community at: <a href="https://danielmiessler.com/upgrade">https://danielmiessler.com/upgrade</a>
Follow on X: <a href="https://x.com/danielmiessler">https://x.com/danielmiessler</a>
Follow on LinkedIn: <a href="https://www.linkedin.com/in/danielmiessler">https://www.linkedin.com/in/danielmiessler</a>
Chapters:
00:00 - Welcome &amp; High-Level Overview of Material Security 02:04 - Common Threats: Phishing and Lateral Movement in Cloud Office 05:30 - Access Control in Collaborative Workspaces (2FA, Just-in-Time, Aging Content) 08:43 - Connecting Signals: From Login to Exfiltration via Rule Automation 12:25 - Real-World Scenario: Suspicious Login and Automated Response 15:08 - Rules, Templates, and Customer Customization at Onboarding 18:46 - Accidental Risk: Sensitive Document Sharing and Exposure 21:04 - Security Misconfigurations and Internal Abuse Cases 23:43 - Full Control Points: IP, Behavior, Classification, Sharing Patterns 27:50 - Integrations, Notifications, and Real-Time Security Team Coordination 31:13 - Lateral Movement: How Attacks Spread Across the Workspace 34:25 - Use Cases Involving Google Gemini and AI Exposure Risks 36:36 - Upcoming Features: Deeper Remediation and Contextual Integration 39:30 - Closing Thoughts and Where to Learn More

A Conversation with Patrick Duffy from Material Security

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/eabe6af1-fb43-48e0-9840-b2c0001e9dba/transcript

AI is changing cybersecurity at a fundamental level—but how do we decide what to build, and when? In this episode, I outline a structured way to think about AI for security: from foundational ideas to a future-proof system that can scale with emerging threats.

• Rethinking Human Workflows as Intelligence Pipelines
By mapping tasks into visual workflows, we can pinpoint exactly where human intelligence is still required—and where AI agents are most likely to replace or enhance us.

• Using AI to Understand and Manage Organizational State
I introduce the concept of AI state management: building systems that track your current and desired security posture in real time, and using AI to bridge the gap—automating insights, decisions, and even actions across your environment.

• Building a Cyber Defense Program Inspired by Attacker Playbooks
Instead of waiting for threats, I propose a new framework based on attacker capabilities—what they wish they could do now and in the near future—and how to proactively prepare by building a continuously adapting AI-powered defense system.

Subscribe to the newsletter at:
https://danielmiessler.com/subscribe

Join the UL community at:
https://danielmiessler.com/upgrade

Follow on X:
https://x.com/danielmiessler

Follow on LinkedIn:
https://www.linkedin.com/in/danielmiessler

Chapters:
00:00 - Framing the Future: Two Key Questions on AI and Cybersecurity
01:28 - Intelligence Pipelines: Visualizing Human Work as Replaceable Workflow
06:10 - Theory of Constraints: How Attackers Are Bottlenecked by Human Labor
10:42 - Defining Agents: What Makes AI Different From Traditional Automation
12:08 - AI State Management: The Universal Use Case for Automated Intelligence
16:53 - Real-World Demo: Unified Context AI for Security Program Management
26:30 - Advanced Uses: Reassigning Projects, Updating KPIs, and Security Reports
34:58 - Automating Security Questionnaires With AI Context Awareness
38:43 - ACAD Framework: Predicting and Preparing for Future Attacker Capabilities
47:40 - Defender Response: Building AI-Driven Red Teams and Internal UCCs
52:25 - Final Answers: How Software and Security Change With AI Agents

AI is changing cybersecurity at a fundamental level&mdash;but how do we decide what to build, and when? In this episode, I outline a structured way to think about AI for security: from foundational ideas to a future-proof system that can scale with emerging threats.
&bull; Rethinking Human Workflows as Intelligence Pipelines By mapping tasks into visual workflows, we can pinpoint exactly where human intelligence is still required&mdash;and where AI agents are most likely to replace or enhance us.
&bull; Using AI to Understand and Manage Organizational State I introduce the concept of AI state management: building systems that track your current and desired security posture in real time, and using AI to bridge the gap&mdash;automating insights, decisions, and even actions across your environment.
&bull; Building a Cyber Defense Program Inspired by Attacker Playbooks Instead of waiting for threats, I propose a new framework based on attacker capabilities&mdash;what they wish they could do now and in the near future&mdash;and how to proactively prepare by building a continuously adapting AI-powered defense system.
Subscribe to the newsletter at: <a href="https://danielmiessler.com/subscribe">https://danielmiessler.com/subscribe</a>
Join the UL community at: <a href="https://danielmiessler.com/upgrade">https://danielmiessler.com/upgrade</a>
Follow on X: <a href="https://x.com/danielmiessler">https://x.com/danielmiessler</a>
Follow on LinkedIn: <a href="https://www.linkedin.com/in/danielmiessler">https://www.linkedin.com/in/danielmiessler</a>
Chapters: 00:00 - Framing the Future: Two Key Questions on AI and Cybersecurity 01:28 - Intelligence Pipelines: Visualizing Human Work as Replaceable Workflow 06:10 - Theory of Constraints: How Attackers Are Bottlenecked by Human Labor 10:42 - Defining Agents: What Makes AI Different From Traditional Automation 12:08 - AI State Management: The Universal Use Case for Automated Intelligence 16:53 - Real-World Demo: Unified Context AI for Security Program Management 26:30 - Advanced Uses: Reassigning Projects, Updating KPIs, and Security Reports 34:58 - Automating Security Questionnaires With AI Context Awareness 38:43 - ACAD Framework: Predicting and Preparing for Future Attacker Capabilities 47:40 - Defender Response: Building AI-Driven Red Teams and Internal UCCs 52:25 - Final Answers: How Software and Security Change With AI Agents

AICAD: Artificial Intelligence Capabilities For Attack & Defense

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/043beea2-0cea-4506-be81-b2bd0174c590/transcript

The conversation around AGI and ASI is louder than ever—but the definitions are often abstract, technical, and disconnected from what actually matters. In this episode, I break down a human-centered way of thinking about these terms, why they’re important, and a system that could help us get there.

I talk about: 

• A Better Definition of AGI and ASI
Instead of technical abstractions, AGI is defined as the ability to perform most cognitive tasks as well as a 2022 U.S.-based knowledge worker. ASI is intelligence that surpasses that level. Framing it this way helps us immediately understand why it matters—and what it threatens.

• Invention as the Core Output of Intelligence
The real value of AGI and ASI is their ability to generate novel solutions. Drawing inspiration from the Enlightenment, we explore how humans innovate—and how we can replicate that process using AI, automation, and structured experimentation.

• Scaling the Scientific Method with AI
By building systems that automate idea generation, recombination, and real-world testing, we can massively scale the rate of innovation. This framework—automated scientific iteration—could be the bridge from human intelligence to AGI and beyond.

Subscribe to the newsletter at:
https://danielmiessler.com/subscribe

Join the UL community at:
https://danielmiessler.com/upgrade

Follow on X:
https://x.com/danielmiessler

Follow on LinkedIn:
https://www.linkedin.com/in/danielmiessler

Chapters:

00:00 - Why AGI and ASI Definitions Should Be Human-Centric
01:55 - Defining AGI as a 2022-Era US Knowledge Worker
03:04 - Defining ASI and Why It’s Harder to Conceptualize
04:04 - The Real Reason to Care: AGI and ASI Enable Invention
05:04 - How Human Innovation Happens: Idea Collisions and Enlightenment Lessons
06:56 - Building a System That Mimics Human Idea Generation at Scale
09:00 - The Challenge of Testing: From A/B Tests to Biotech Labs
10:52 - Creating an Automated, Scalable Scientific Method With AI
12:50 - A Timeline to AGI and ASI: Predictions for 2027–2030

The conversation around AGI and ASI is louder than ever&mdash;but the definitions are often abstract, technical, and disconnected from what actually matters. In this episode, I break down a human-centered way of thinking about these terms, why they&rsquo;re important, and a system that could help us get there.
I talk about:&nbsp;
&bull; A Better Definition of AGI and ASI Instead of technical abstractions, AGI is defined as the ability to perform most cognitive tasks as well as a 2022 U.S.-based knowledge worker. ASI is intelligence that surpasses that level. Framing it this way helps us immediately understand why it matters&mdash;and what it threatens.
&bull; Invention as the Core Output of Intelligence The real value of AGI and ASI is their ability to generate novel solutions. Drawing inspiration from the Enlightenment, we explore how humans innovate&mdash;and how we can replicate that process using AI, automation, and structured experimentation.
&bull; Scaling the Scientific Method with AI By building systems that automate idea generation, recombination, and real-world testing, we can massively scale the rate of innovation. This framework&mdash;automated scientific iteration&mdash;could be the bridge from human intelligence to AGI and beyond.
Subscribe to the newsletter at: <a href="https://danielmiessler.com/subscribe">https://danielmiessler.com/subscribe</a>
Join the UL community at: <a href="https://danielmiessler.com/upgrade">https://danielmiessler.com/upgrade</a>
Follow on X: <a href="https://x.com/danielmiessler">https://x.com/danielmiessler</a>
Follow on LinkedIn: <a href="https://www.linkedin.com/in/danielmiessler">https://www.linkedin.com/in/danielmiessler</a> Chapters:
00:00 - Why AGI and ASI Definitions Should Be Human-Centric 01:55 - Defining AGI as a 2022-Era US Knowledge Worker 03:04 - Defining ASI and Why It&rsquo;s Harder to Conceptualize 04:04 - The Real Reason to Care: AGI and ASI Enable Invention 05:04 - How Human Innovation Happens: Idea Collisions and Enlightenment Lessons 06:56 - Building a System That Mimics Human Idea Generation at Scale 09:00 - The Challenge of Testing: From A/B Tests to Biotech Labs 10:52 - Creating an Automated, Scalable Scientific Method With AI 12:50 - A Timeline to AGI and ASI: Predictions for 2027&ndash;2030

A Possible Path to ASI

https://api.omny.fm/orgs/070af456-729b-4a0f-9c09-a6c100397b59/clips/d05d8ebc-1da2-44f2-b859-b2b9017492b0/transcript

The Biggest Advantage in Machine Learning Will Come From Superior Coverage, Not Superior Analysis

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