Webinars to pollinate your perspective.

Strengthen network boundaries and maintain control as AI accelerates threats and infrastructure change with centralized visibility, continuous segmentation validation, and governed policy automation.

AI is accelerating attackers’ ability to identify security gaps, compromise entry points, and move through environments before manual processes can respond. When an identity, endpoint, or workload is compromised, the network becomes a critical line of defense—determining what the attacker can reach and how far the attack can spread. Clearly defined boundaries and consistently enforced segmentation will contain that movement and protect critical assets.

At the same time, AI coding agents and AI-driven operations are increasing the speed and volume of network changes. AI-driven operations are also increasing the speed and volume of network changes. Across firewalls, cloud, SASE, and microsegmentation, policies are often managed through separate tools—raising the risk of policy drift, segmentation gaps, and overly permissive access. Without a unified view, manual reviews and point-in-time audits cannot keep pace.

In this webinar, Erez Tadmor, Global Field CTO at Tufin, will show how security teams can stay in control. You’ll learn how a unified approach to network security posture management can provide centralized visibility, continuously validate segmentation and policy intent, evaluate risk before changes are implemented, and govern policy changes across complex hybrid networks.

Attendees will learn how to:

• Gain a centralized view of security policy across hybrid, multi-vendor environments
• Identify policy drift and inconsistencies across different enforcement points
• Continuously validate segmentation and security policy against intended controls
• Evaluate the risk and potential impact of network changes before implementation
• Automate policy changes while maintaining governance and compliance
• Produce audit-ready evidence that security controls are working as intended

The attacks are faster and so are the change requests. Security teams that can see, validate, and govern network policy in one place can keep up with both, without choosing between exposure and delay.

In an era where breaches are viewed as "when," not "if," true resilience isn't just about building higher walls—it’s about ensuring your business can bounce back before it becomes a business-ending event.

Join us for a demonstration where you’ll see a real-world attack lifecycle unfold. We will pit modern adversary tactics against true cyber resilience strategies, demonstrating exactly how our preemptive recovery engine is continuously evaluating your data to keep your data safe and help you bring up your operations back as quickly and cleanly as possible.

Need a capability you don't have today? You're in good company. From March 30 to Sept. 29, 54% of the security vendor-selection requests IANS handled started with a capability gap, and AI adoption drove 29%.

AJ King and Dave Shackleford, both IANS Faculty, show you how to turn that need into a defensible shortlist without burning cycles on vendors that don't fit. They cover the pre-work, RFP, proof of concept (PoC), and customer reference steps that separate opinion from evidence. You'll learn how to:

• Define the gap, budget, owner, and must-have requirements before you talk to a vendor
• Run an RFP and PoC with standardized scoring and realistic lab conditions
• Separate "AI-powered" from "AI security," and test AI claims instead of taking them at face value
• Pick, back-channel, and score customer references to hear what a vendor-curated list won't tell you

As enterprise AI adoption accelerates, organizations are realizing that the biggest challenge is proving that it's secure. The industry is rapidly shifting. Security alone is no longer enough; today’s AI initiatives are driven by strict governance, emerging compliance regulations, and the overwhelming burden of complex vendor questionnaires.

Organizations need continuous technical evidence showing what AI they use, how it is configured, whether models and agents can be exploited, what risks require remediation and whether controls continue to work after deployment.

Join us for this session where Qualys experts discuss how you can turn fragmented AI security signals into AI governance and risk management that is measurable, evidence-based.

Key takeaways

• What a governable AI programme needs beyond policy and questionnaires.
• How to operationalize continuous AI risk management across the AI lifecycle.
• How to achieve visibility into AI assets and assess AI risk, consolidated in one place.
• How to satisfy NIST, the EU AI Act, customer questionnaires, board oversight and endless vendor questionnaire.

Article 14 of the EU Cyber Resilience Act is active law, holding CISOs accountable for software supply chain risks across connected operational networks. Forward-thinking security leaders are turning this regulatory mandate into a competitive edge by transforming supply chain visibility into long-term operational resilience. Organisations deploying automated visibility eliminate blind spots while saving up to €15 million (£13 million) in potential non-compliance penalties.

Key takeaways for senior security leaders:

• Convert CRA visibility requirements into stronger cyber-physical network uptime before full 2027 enforcement.
• Automate 24-hour incident reporting workflows to streamline compliance without burdening operational teams.
• Eliminate hidden OT supply chain risks that traditional IT vulnerability tools consistently miss.

Aligning your supply chain strategy today turns regulatory pressure into an operational advantage that ensures future production.

As enterprise AI adoption accelerates, organizations are realizing that the biggest challenge is proving that it's secure. The industry is rapidly shifting. Security alone is no longer enough; today’s AI initiatives are driven by strict governance, emerging compliance regulations, and the overwhelming burden of complex vendor questionnaires.

Organizations need continuous technical evidence showing what AI they use, how it is configured, whether models and agents can be exploited, what risks require remediation and whether controls continue to work after deployment.

Join us for this session where Qualys experts discuss how you can turn fragmented AI security signals into AI governance and risk management that is measurable, evidence-based.

Key takeaways

• What a governable AI programme needs beyond policy and questionnaires.
• How to operationalize continuous AI risk management across the AI lifecycle.
• How to achieve visibility into AI assets and assess AI risk, consolidated in one place.
• How to satisfy NIST, the EU AI Act, customer questionnaires, board oversight and endless vendor questionnaire.

Advanced artificial intelligence capabilities have fundamentally compressed the time between vulnerability disclosure and active weaponization. Adversaries can now automate exploitation techniques to immediate and wide effect, turning newly discovered security flaws into active threats in hours rather than weeks. Recognizing that uniform, compliance-based approaches to patching can no longer scale to meet this reality, new mandates and executive orders emphasize the immediate prioritization of cyber defense against AI-driven risks across public sector organizations.

To meet the challenges of this shifting AI-threat landscape, public sector organizations must move toward a unified view of enterprise risk. This requires connecting previously siloed tools covering cloud infrastructure, connected on-premises servers, and external attack surfaces into a single operational plane.

This session explores how to:

• Leverage a unified Cloud-Native Application Protection Platform (CNAPP) approach to solve the complex hybrid exposure problem across legacy and modern environments.
• Integrate telemetry from existing vulnerability scanners and on-premises systems into Wiz’s Unified Vulnerability Management (UVM), translating disconnected point-solution data into centralized architectural context.
• Utilize Attack Surface Management (ASM) network path analysis to verify actual internet reachability, allowing agencies to safely implement isolating controls and extend mandatory compliance windows.
• Deploy full-stack workload visibility and identity analytics to automate remediation timeline assessments, map blast radius, track lateral movement, identify true asset ownership, and accelerate targeted forensic investigations.

Discover how leading organizations are redefining defense as AI accelerates attacks across traditional organizational boundaries.

In this session, experts explore:

• Converging identity resilience, data security, and AI agent operations into a single defense strategy.
• Tactics to reduce systemic risk and contain blast radius as automated attacks exploit identity and data systems.
• How to maintain operational integrity and restore trust across distributed infrastructures.

AI is shipping to production faster than most security programs can test it, and attackers know it. Valid AI vulnerability reports jumped 210% year over year on HackerOne's platform. Prompt injection alone surged 540%. Meanwhile, ~97% of AI-related incidents still trace back to basic access-control flaws.

The gap between how fast teams build AI and how fast they can prove it's safe is widening. This session shows how a company at the heart of the GenAI stack is closing it.

In 45 minutes, you'll walk away with:

• A real-world blueprint for securing AI-adjacent infrastructure on AWS — from a company thousands of AI apps are built on
• Which AI risks actually show up in production (prompt injection, retrieval-layer data exfiltration, agentic tool misuse) and which matter most, backed by HackerOne platform data
• How to combine continuous Bug Bounty, Pentest, code security, and agentic remediation into one exposure-management loop
• How to validate AI systems adversarially — mapped to the OWASP Top 10 for LLMs, MITRE ATLAS, and NIST AI RMF
• How to preserve developer velocity while adding AI-specific security testing (without becoming the "team that says no")
• One thing you can do Monday morning to close your AI security gap

Most organizations measure cyber risk the way they always have—periodic assessments, annual reviews, and point-in-time snapshots that were outdated before the ink dried. The problem isn't that risk management frameworks are wrong. It's that they were built for an environment where risk moved slowly enough to measure on a schedule. That environment is gone. Cloud adoption, third-party dependencies, and expanding attack surfaces are creating new exposure faster than periodic programs can track. Risk programs that can't keep pace with continuous change aren't managing risk. They're documenting it after the fact.

See how security and risk teams are closing the gap between risk measurement and risk reality.

A major concern in CTI about the proliferation of AI is how analysts can take advantage of this technology without falling prey to cognitive outsourcing and deskilling. How can you position your teams to fully utilize AI without cognitive atrophy and the loss of tradecraft?

In this webinar, we demonstrate practical approaches, useful to leaders and analysts alike, to use AI-led solutions as an augmentation to their CTI workflows, without losing critical thinking, analysis, and creativity capabilities. We’ll draw on examples from CTI but the lessons from this webinar will be transferable to other InfoSec teams.

Topics include

• The importance of AI-literacy as the foundation to delineating between healthy and risky uses of AI
• Governance and guardrails that leadership can enact to train new skills in analysts, not cost them
• Practical examples of how to toe the line between cognitive outsourcing and AI-driven efficiency using Structured Analytic Techniques

Relevant solution areas

• Cyber Threat Intelligence
• Training
• Artificial Intelligence