Some time ago we sat down with Martin Elwin, Technology Director for Northern Europe at AWS, and talked about the massive infrastructure investments needed for AI adoption, of course also from AWS. However, simply buying GPUs doesn't deliver AI value. More is needed. We also talk about new on-prem offerings AWS introduced for organizations wanting to control their hardware while benefiting from managed services.

Other key topics include production AI deployments using Amazon Bedrock AgentCore for evaluations and drift detection, the importance of continuous testing in AI applications, and how AWS applies its own Amazon learnings to customer challenges in logistics, supply chain optimization, and regulated industries.

0:50 - AI driving infrastructure capacity needs
2:37 - Introducing AWS AI factories
3:49 - Getting business value from AI
10:13 - European sovereign cloud discussion
18:11 - Amazon Bedrock AgentCore for production AI
20:57 - Security agents and continuous testing

At NTT Upgrade 2026, Paul Bloudoff, senior director of Edge AI at NTT Data, discusses with us how edge computing, private 5G, and physical AI are transforming manufacturing operations. We talk about the symbiotic relationship between edge AI and private 5G connectivity, and focus on how manufacturers can achieve real-time insights and automation on the factory floor.

During our conversation, Bloudoff talks about real-world examples, including Cargill's deployment of private 5G across 50+ manufacturing facilities, and explains how NTT Data delivers full-stack solutions from sensors to cloud infrastructure. The conversation covers predictive maintenance, task verification through machine vision, and how foundation models can be trained with just 40 hours of video data instead of requiring months of preparation.

Key takeaways:
• Edge AI focuses on computing as close as possible to data sources in OT environments
• Private 5G enables low-latency, high-bandwidth connectivity for real-time manufacturing data
• Physical AI models can be deployed in weeks rather than years with modern foundation models
• Multiple use cases are typically needed to justify private 5G network deployments
• Safety and preventative maintenance are often the first use cases that enable adoption
• Full-stack solutions include sensors, connectivity, edge compute, algorithms, and managed services
• Partnerships with Nvidia, Qualcomm, and ISVs enable comprehensive edge AI solutions

Chapters:
1:35 - Defining the edge in enterprise environments
2:59 - Full stack edge AI solutions
4:54 - Private 5G and edge AI synergy
6:23 - Use cases for edge AI and physical AI
7:41 - Real-world private 5G deployments
13:36 - AI partnerships and ecosystem approach
13:59 - Measuring value and ROI
22:23 - Automation and the future of edge AI

In this episode of Techzine TV from RSAC 2026 Conference, Conor Sherman, CISO in Residence at Sysdig, discusses the evolving role of security leaders in the age of AI and cloud-native infrastructure. Sherman shares insights on how CISOs are adapting to rapidly changing threat landscapes where attackers can compromise cloud environments in as little as eight minutes.

Key takeaways:
• The time from vulnerability disclosure to exploitation has collapsed to under 20 hours
• Runtime security has become critical as traditional posture management alone is insufficient
• Security leaders must balance dual mandates: transforming their own organizations while advising the business on AI adoption
• Threat actors are leveraging AI to accelerate attacks, requiring active defense mechanisms at the kernel level
• CISOs should demand proof from vendors through independent testing like Atomic Red Team

Chapters:
0:00 - Introduction to Sysdig and CISO in residence role
1:36 - The CISO mindset: excitement and anxiety
5:20 - Active defense and the department of no
8:56 - Runtime security as a priority
13:00 - The layered security challenge
16:47 - Ephemeral workloads and kernel-level visibility
19:51 - What CISOs should ask vendors

Deploying AI agents is not a good idea if there is no governance. That's why Rubrik launched its Agent Cloud, which is powered by what it calls SAGE. This stands for Semantic AI Governance Engine. At RSAC 2026 Conference, we had the chance to speak to Devvret Rishi, GM for AI at Rubrik, to hear all about framework that uses small language models to provide real-time governance over agent operations.

According to Rishi, SAGE moves beyond static rules to semantic policy enforcement. This allows organizations to express intent in natural language like "AI should not give financial advice." The platform learns from human feedback, surfaces policy violations with reasoning, and runs efficiently enough to monitor every agent interaction. As agentic workforces emerge, it is important to learn why AI-powered security is the only scalable approach to governing non-deterministic systems.

Key takeaways:
• Why conventional identity infrastructure can't handle agent-to-agent interactions
• How SAGE uses semantic understanding to enforce complex policies in real-time
• The three pillars of agent management: visibility, governance, and efficiency
• Why small language models are crucial for low-latency policy enforcement
• How human-in-the-loop learning prevents false positives without causing alert fatigue
• Real-world examples of agents circumventing disabled connectors
• Deployment options from cloud-hosted to air-gapped environments

Chapters:
2:14 - Why Rubrik tackles AI security
4:05 - The agentic AI challenge
4:16 - Three pillars of agent management
7:27 - Why agents are unpredictable
9:40 - Introducing SAGE framework
11:45 - Deploying small language models
12:02 - How SAGE learns from humans

Sam Curry, CISO of Zscaler, discusses the security implications of agentic AI workflows at RSAC 2026 Conference with us. We dig into the implications of AI agents on identity management, authentication frameworks, and security operations. Curry emphasizes the critical need for proper governance, zero trust architecture, and accountability as AI agents become the dominant form of interaction on the internet. But where do you start?

Key takeaways:
• Identity and runtime are the two critical security pillars for agentic AI
• AI agents will soon outnumber human interactions online
• Authentication, authorization, and authenticity verification are essential for agent security
• API security remains a fundamental challenge in the agentic era
• Zero trust principles help contain risk and manage agent sprawl
• Automation must account for intelligent adversaries to avoid predictability
• The future security workforce will blend human expertise with agent assistance

Chapters:
0:47 - Identity and runtime as security fundamentals
1:44 - Silicon-based intelligence and agent interactions
3:11 - Building authentication and authorization frameworks
9:51 - Zero trust architecture and governance
11:53 - API security and MCP protocol challenges
18:45 - Automation and adversarial security challenges
19:57 - The future of human-agent collaboration in security

In this episode of Techzine TV, recorded at NetApp Insight Xtra in Eindhoven, Jeff Baxter, VP of Product Marketing at NetApp, discusses the company's approach to data sovereignty in Europe and beyond. With 18 years at NetApp, Baxter provides deep insights into how the company is addressing sovereignty concerns while building AI infrastructure at scale.

The conversation covers NetApp's sovereign cloud partnerships, the implications of working with major cloud providers like AWS while maintaining sovereignty principles, and the challenges of supply chain security. Baxter explains how NetApp's AFX platform and AI data engine enable organizations to build exascale AI infrastructure while maintaining full control over their data. The discussion also addresses the growing NAND flash shortage and how NetApp is helping customers optimize their storage infrastructure.

Key takeaways:
• Data sovereignty requires both data residency and control over accessibility
• NetApp partners with AWS, Azure, and Google Cloud on sovereign cloud regions
• AFX platform enables exascale AI infrastructure with the same ONTAP APIs
• DX50 compute nodes provide GPU offload for data transformation without forcing NetApp into the compute business
• AI data engine reduces storage bloat by enabling vectorized embeddings without data duplication
• NAND flash shortages require strategic optimization rather than magic bullet solutions
• NetApp's 30+ year track record provides stability compared to startup alternatives

In this episode of Techzine TV, recorded at Wiz's London Wizdom event, we explore the evolving landscape of cloud security with Amitai Cohen, responsible for threat research at Wiz. The discussion reveals why cloud security has become synonymous with all security practices and examines the most common misconfigurations organizations face.

Cohen explains how the responsibility for security is shared between vendors and customers, and why default configurations hold immense power in determining security postures. The conversation delves into supply chain security across package registries like npm and PyPI, open source maintainer responsibilities, and the emerging challenges of AI-powered attacks.

Key takeaways:
• Why IMDSv1 vs v2 in AWS represents typical cloud misconfiguration patterns
• How AI tooling built for labs is being deployed in production environments
• The rise of vibe coding and its security implications
• How attackers are using AI CLIs on developer machines
• Why runtime detection is becoming more critical than static malware analysis
• The importance of opinionated security defaults from vendors
• Supply chain security differences between major package registries

Chapters:
0:12 - Introduction to Wiz and cloud security
1:32 - Why cloud security is so fundamental
2:09 - Customer misconfigurations explained
6:18 - The power of secure defaults
6:44 - Open source security challenges
15:54 - AI creating new attack surfaces
17:39 - The importance of runtime detection

IFS unveils major AI partnerships at their first dedicated AI event, focusing on industrial and physical AI solutions. Christian Pedersen, CPO of IFS, discusses strategic collaborations with Anthropic for IFS Resolve, Boston Dynamics for robotic inspections, 1X Technologies for humanoid workforce integration, and Siemens for data center energy optimization.

Key insights include the three-level model intelligence architecture combining public LLMs, industry-specific private models, and customer data. The demonstration of Boston Dynamics' Spot robot showcases integrated workflow automation, while the One X humanoid partnership targets manufacturing and service repair applications. IFS emphasizes giving customers control over AI deployment timelines while maintaining rapid innovation cycles.

• Strategic AI partnerships focused on industrial solutions
• IFS Resolve co-developed with Anthropic for predictive maintenance
• Boston Dynamics Spot integration for autonomous inspections
• 1X Technologies humanoid robots targeting manufacturing by 2026
• Three-level intelligence architecture: public, private, and customer models
• Real-world impact: distiller William Grant saved $8.4M annually at one site
• MCP server architecture for efficient API management
• Physical AI addressing skilled labor shortages in utilities

Chapters:
0:12 - IFS AI event overview
1:27 - Anthropic partnership and IFS Resolve
2:19 - Boston Dynamics physical AI integration
5:58 - Three-level model intelligence architecture
15:02 - Real-world physical AI applications
17:25 - 1X humanoid robots for enterprise
21:29 - The future of human-robot interaction