Episode # 183

Adriel is a leader in cybersecurity with over 20 years of experience. Adriel founded Secure Network Operations and the SNOsoft Research Team, whose vulnerability research helped shape modern responsible disclosure practices. He later launched Netragard, pioneering Realistic Threat Penetration Testing, which he now call Red Teaming, and expanding into a broad range of security services.

• Website: Netregard
• X/Twitter: Netregard

What Listeners Will Learn:

• Why "AI penetration testing" is often closer to automated scanning than real offensive testing
• How AI changes security risk mainly through volume and speed, not necessarily sophistication
• Where organizations get misled into a false sense of security
• Why "preventing breach" is unrealistic and why limiting damage paths matters more
• What cybersecurity professionals should focus on to stay relevant in the LLM era
• How AI may influence vulnerability research, but still struggles with novel exploitation thinking

• Netregard

Welcome to Open Tech Talks.

Quick note before we start, thank you.

The messages, the feedback, the "keep this practical" reminders… they've been incredibly helpful. Open Tech Talks has always been a weekly sandbox for technology insights, experimentation, and inspiration—with one objective: learn, test, and share what's real.

Now, a personal moment from this week.

A few days ago, I sat with a business owner who said something that stuck with me:

"AI is everywhere… but I don't know where to start without breaking my business."

And that's the truth for most companies, especially small businesses.

Because "start with AI" sounds simple… until it touches real operations:

• leads that go cold,

• follow-ups that don't happen,

• teams that feel overwhelmed,

• tools that multiply,

• processes that nobody can explain clearly.

Most AI projects don't fail because the model is weak.

They fail because the process is unclear, the team is overloaded, and the strategy is missing.

Let's begin.

Episode # 182

He works with CEOs, COOs, and operating partners in the $20M–$250M range who are ready to go beyond pilots and turn AI into real EBITDA growth. His proven 90-day sprint framework, AImpact OS, delivers measurable lifts across productivity, customer service, and sales.

• Website: Strategic Advisors

What Listeners Will Learn:

• Identify the best "starting point" for AI using business pain, not hype
• Understand why AI pilots fail mostly due to adoption (not technology)
• Learn a practical approach to simplify workflows before adding automation
• See how SMBs can move faster than enterprises in the AI era
• Understand the difference between augmentation and transformation with AI
• Learn how to avoid tool overload and focus on measurable outcomes

• Strategic Advisors

This week, I've been thinking about something slightly uncomfortable.

Last weekend, I was reviewing one of my older architecture diagrams from five years ago. A cloud-native migration plan I was deeply proud of at the time. It was clean. Structured. Scalable.

And then I asked myself:

If I were to rebuild this today in the era of generative AI…

Would I build it the same way?

The honest answer?

No.

Not because it was wrong.

But because our assumptions have changed.

Two years ago, AI was a feature.

Today, AI is shaping architecture decisions.

We're not just designing systems anymore.

We're designing systems that design, generate, predict, and automate.

And here's the tension I keep seeing in enterprise conversations:

Everyone wants AI.

But very few are asking:

"What technical debt are we creating while chasing it?"

That's why today's conversation matters.

Today, I'm joined by Maxim Salav, based in Australia, someone who works deeply in enterprise architecture and technical debt remediation.

And this episode is not about hype.

It's about responsibility.

Because AI doesn't remove architectural complexity.

In many cases, it amplifies it.

Let's get into it.

Chapters

00:00 Introduction to Technical Debt and Architecture
01:34 The Impact of AI on Technical Debt
04:12 Generative AI and Architectural Challenges
08:40 Adopting AI in Organizations
12:26 Building AI Strategies and Governance
17:33 Data Quality and AI Integration
22:43 Guardrails for AI Adoption

Episode # 181

He is a technology advisor and enterprise architect with more than two decades of experience working with high-growth companies, complex systems, and business-critical platforms.

• Website: Arch-Experts

What Listeners Will Learn:

• What technical debt really means in the AI era
• How generative AI can unintentionally increase hidden system risk
• Why architecture remains critical despite AI coding tools
• The importance of governance and verification layers in AI systems
• How large enterprises are cautiously integrating AI
• Why strategy must precede AI deployment
• The evolving role of enterprise architects in AI-native environments

• Arch-Experts