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

Together with DJ Sampath, SVP of AI Software and Platforms at Cisco, we take a deep dive in the evolution from AIOps to Agentic Ops. The main focus of the discussion is on AI Canvas, Cisco's latest innovation for network troubleshooting. Unlike traditional chatbot-based AI tools, AI Canvas leverages a purpose-built deep network model trained on 30 years of human network engineering interactions, the company claims.

The conversation explores three core principles of agentic operations: bringing data across silos together, enabling multiplayer collaboration, and using purpose-built models for specific domains. Sampath details how AI Canvas aims to transform IT troubleshooting from a reactive, dashboard-heavy process into an intelligent, agent-driven experience that autonomously debugs and resolves network issues.

Key takeaways:
• Agentic Ops represents autonomous AI agents executing tasks end-to-end, not just chatbot interactions
• AI Canvas uses a deep network model trained on real human network engineering data, not just synthetic data
• Three pillars: unified data silos, multiplayer workflows, and purpose-built models
• AI Canvas creates dynamic, generative UI widgets that it populates based on troubleshooting context
• Starting with Meraki and Thousand Eyes integration, expanding to Cisco Cloud Control
• Future expansion to include third-party vendors through MCP servers

Chapters:
0:13 - Introduction
0:25 - Understanding AI Canvas
1:43 - From AIOps to Agentic Ops
6:43 - Three core principles of Agentic Ops
8:11 - Deep Network Model explained
9:24 - AI Canvas in action
13:07 - Automation and workflows
16:23 - Prerequisites and getting started

Alex Kreilein, VP of Product Security at Qualys, discusses the shift from vulnerability management to risk operations at the company's Risk Operations Conference (ROCon). He explains why focusing solely on vulnerability counts misses the point and how organizations can achieve better security outcomes through risk-based approaches.

Kreilein breaks down the practical challenges of the concept of the SBOM (Software Bill of Materials), introducing VEX (Vulnerability Exploitability Exchange) as the missing piece for effective vulnerability communication. He shares insights on developer friction points, the real reasons for vulnerability debt, and why test efficacy matters more than compliance checkboxes.

Key takeaways:
• Why risk operations differs fundamentally from vulnerability management
• How SBOMs become actionable with VEX status messages (affected, not affected, under investigation, fixed)
• The hidden cost of technical debt and fragile applications
• Real-world approaches to secure by design and developer productivity
• How agentic AI can help security teams focus on strategic outcomes
• Why compliance is a floor, not a ceiling for security

0:07 - Introduction to Qualys RiskOps Conference
0:33 - Understanding risk versus vulnerabilities
2:21 - The role of VP Product Security
3:03 - Software bills of materials explained
9:08 - VEX for vulnerability communication
10:51 - Agentic AI in security
13:38 - Building secure protocols
15:58 - Developer challenges with security

We discuss Workday's acquisition of Pipedream with Gabe Monroy, SVP and GM Platform at Workday. Pipedream is an iPaaS platform competing with Zapier and Make. The acquisition supports Workday's ambitious AI strategy, enabling enterprise insights to trigger actions across 3,000+ third-party connectors. Monroy explains how Workday plans to maintain Pipedream as an open ecosystem while integrating it into their agentic AI vision. He also covers the recent Flowwise acquisition, Workday Go for SMB expansion, and the new EU sovereign cloud offering to meet European regulatory requirements.

The interview reveals Workday's accelerated innovation pace through strategic acquisitions and organic development, positioning the platform to compete with major enterprise SaaS players while addressing multi-platform agent governance challenges.

Key Takeaways:
• Pipedream will remain available to existing users and startups
• Workday is building agent capabilities using Pipedream connectors for use cases like automated performance review feedback
• The company is targeting SMB markets with Workday Go
• EU sovereign cloud addresses data residency and AI regulation requirements
• Workday is increasing development velocity through both acquisitions and internal improvements

Chapters:
0:59 - Pipedream acquisition announcement
3:29 - Integration and ecosystem strategy
6:36 - Managing agents across platforms
8:29 - Workday Go for SMB market
10:45 - Competition and innovation velocity
14:43 - EU sovereign cloud initiative
16:59 - Future acquisition plans

Keywords: Workday, Pipedream, iPaaS, agentic AI, enterprise integration, API automation, Flowwise, EU sovereign cloud, Workday Go, platform strategy, Peter Bayless, enterprise SaaS, agent orchestration, SMB expansion

Richard Smith, EVP and GM EMEA Cloud Infrastructure at Oracle, sits down with us during Oracle AI World to discuss the company's strategic move to go all-in on AI. Not only the name of its flagship event has changed from Cloud World to AI World, it is much more fundamental.

Our conversation covers, among other things, Oracle's AI data platform. One of the goals of this platform is to allow customers to use their own data for AI workloads without moving it. With data data sovereignty and security front and center nowadays, this is an important feature of Oracle's approach.

The conversation meanders from high-level strategic to rather fine-grained and specific. On the one hand we talk about the importance of database vectorization in the latest version of the Oracle database and about how Oracle handles scale-across workloads. On the other, we ask Smith questions about Oracle's massive infrastructure investments, including the Abilene data center project and Stargate initiatives, but also European sovereignty concerns, DORA compliance. Oracle being a key player in the 'AI bubble' discussion that is going on at the moment, we don't shy away from that discussion either.