Cybersecurity teams are overwhelmed—stuck in a reactive loop of chasing alerts, reacting to incidents, and never getting ahead. Expected to do more with less, many SOCs struggle under the weight of tool sprawl, slow workflows, and fragmented processes. It’s time to break the firefighting cycle and modernize the SOC. By transforming operations through smarter architecture, automation, and consolidation, organizations can drive efficiency, reduce burnout, and stay ahead of today’s threats.

Topics include:
• Breaking the cycle of reactive incident response
• Building a proactive threat management framework
• Maximizing analyst productivity through automation and workflow design
• Adopting scalable architectures that grow with business needs

Join us and get a clear roadmap to transform your SOC into a proactive, strategic force multiplier.

Despite growing investments in cybersecurity, many organizations still struggle to reduce real risk. Why? Because dashboards don’t reduce risk—action does. To reduce real risk, organizations must operationalize cyber risk strategies—prioritizing action over reporting, and aligning security efforts with measurable business outcomes.

Topics include:
• Making cyber risk visible, measurable, and relevant to business leaders
• Linking asset visibility to business-critical risk decisions
• Moving from reporting risk to actively reducing it—at scale

Learn how to operationalize your risk strategy to prioritize action, align security with business outcomes, and turn visibility into measurable impact.

Defenders have long played catch-up in the cybersecurity arms race. But agentic AI offers a chance to flip the script—enabling systems that anticipate, adapt, and respond to threats before they escalate. To stay ahead of evolving threats, organizations must shift from reactive security postures to resilient, autonomous systems powered by agentic AI.

Topics include:
• What makes agentic AI different from traditional automation
• How agentic AI fits into the evolving cyber kill chain
• Building trust, oversight, and resilience into autonomous systems

Join us to gain practical insights into deploying agentic AI to stay ahead of evolving threats.

Seventy percent of SOC analysts with five years or less of experience leave within three years. The typical explanation is burnout from an overwhelming threat landscape. The less comfortable explanation is that the tools meant to help analysts are making their jobs worse. Fragmented workflows, constant context-switching across disconnected platforms, and thousands of daily alerts with no actionable context are turning what should be a high-impact career into a repetitive grind. When analysts spend more time wrangling dashboards than investigating threats, the best ones leave.

The retention problem is not just a staffing issue. It is an operational risk. Every departure takes institutional knowledge with it, increases the load on remaining team members, and widens the window for missed detections. Organizations that want to keep experienced analysts need to redesign how SOC work gets done, starting with how detection, investigation, automation, and analyst experience are delivered across the stack.

Addressing this challenge requires coordination across SIEM, XDR, SOAR, MDR, and security analytics platforms to reduce friction, improve context, and make investigations more actionable.

Topics include:

• How fragmented tooling and manual workflows contribute to analyst turnover
• Reducing cognitive load through unified investigation and automated triage
• Building a SOC environment that retains talent by making the work sustainable

Join us to explore how rethinking SOC tooling and workflows can address the retention crisis at its source.

More than half of development teams report that application security testing slows their release pipeline. On the other side, security teams point to the 81% of organizations that knowingly shipped vulnerable code in the past year. Both sides have legitimate concerns, and the friction between them is getting worse as release cadences accelerate and AI-generated code enters production. The result is a standoff where developers route around security controls and AppSec teams lose influence over the code that actually ships.

The path forward is not about one side winning. It is about removing the friction that makes security feel like an obstacle. That means fewer low-value alerts landing on developer desks, clearer ownership of findings, risk-based prioritization that respects engineering time, and tooling that works inside the developer workflow rather than beside it.

Resolving this tension requires alignment across testing, prioritization, and runtime protection approaches – from SAST, DAST, and SCA to API security, container security, and developer-native security tooling embedded directly into CI/CD pipelines.

Topics include:

• Why AppSec noise (not AppSec itself) is driving the friction with engineering
• Embedding security into CI/CD pipelines without creating unplanned developer work
• Shifting from “fix everything” to prioritizing the 2–5% of findings that carry real risk

Learn how security and engineering teams are resolving friction and building AppSec programs that move at the speed of development.

When a storage bucket is left publicly accessible or an IAM role is overprovisioned, the instinct is to call it a mistake and fix the specific instance. The problem is that these “mistakes” keep happening because the systems that produce them are designed for speed, not security. Nearly anyone in the organization can spin up cloud resources with a few clicks, often with no security review. More than half of organizations cite lenient IAM practices as a top data security challenge, and 72% of cloud environments have publicly exposed PaaS databases lacking sufficient access controls. This is not a human error problem. It is a governance and architecture problem.

Fixing individual misconfigurations is necessary, but it is not a strategy. Organizations need guardrails that prevent misconfigurations at the point of creation, continuous validation that catches drift before attackers do, and unified visibility across multi-cloud environments where assets appear and disappear in real-time.

Solving this requires coordination across cloud security posture management, entitlement management, workload protection, and cloud-native application protection platforms to enforce guardrails, reduce overpermissioning, and maintain visibility across dynamic environments.

Topics include:

• Moving from reactive misconfiguration remediation to preventive guardrails
• Addressing IAM sprawl, overpermissioning, and role creep across cloud environments
• Continuous security validation in dynamic multi-cloud architectures

Explore how to treat cloud misconfigurations as a systemic challenge and build the governance, automation, and visibility to fix the system that produces them.

The average organization has more than 800,000 data files at risk from oversharing, erroneous access permissions, and inappropriate classification. That number is climbing as AI pipelines generate and ingest data faster than any manual classification effort can keep up. Half of all enterprise workloads are now cloud-based, and the rise of AI is accelerating data creation without guardrails or oversight. The result is shadow data: sensitive information scattered across environments that security teams cannot see, classify, or protect.

Traditional data security strategies assume that most data lives in known locations with defined access controls. That assumption broke years ago. Today, 90% of business-critical documents are shared outside the C-suite, AI models are training on datasets that may contain PII or intellectual property, and unstructured content is multiplying across SaaS, cloud storage, and collaboration platforms.

Regaining control requires visibility and coordination across data discovery, classification, access governance, and data protection controls – from DSPM and DLP to SaaS security and AI data governance.

Topics include:

• Discovering and classifying sensitive data across cloud, SaaS, and AI environments
• Addressing the shadow data problem created by AI-driven data proliferation
• Reducing oversharing risk through automated access governance and posture management

Join us to learn how organizations are regaining visibility and control over data they did not know they were exposing.

Non-human identities now outnumber human identities by a ratio of 144 to 1. That number grew 44% in a single year and shows no sign of slowing as organizations deploy AI agents, automate workflows, and expand cloud infrastructure. Most of these machine identities operate with excessive privileges, unclear ownership, and no lifecycle management. When a development team spins up a service account for a proof-of-concept project, that credential often persists long after the project ends, maintaining broad access to production systems.

This accumulation of unmanaged identities, dormant accounts, and overprivileged access is what some in the industry are calling “identity debt.” Like technical debt, it compounds silently until something breaks. Organizations that built their IAM programs around human identities are discovering that machines carry more privilege, access more systems, and create more risk than people do.

Addressing identity debt requires extending governance across IAM, PAM, ITDR, and cloud entitlement management to cover human and non-human identities with consistent lifecycle controls and visibility.

Topics include:

• Extending identity governance to non-human identities, service accounts, and AI agents
• Identifying and remediating orphaned accounts and privilege sprawl before attackers exploit them
• Building lifecycle management processes that scale with the rate of machine identity creation

Discover how to audit, govern, and secure the identities that your current IAM program was never designed to manage.

Nearly three-quarters of organizations have two or fewer full-time employees managing vendor risk, even though more than half oversee 300 or more third-party relationships. Close to half experienced a third-party cyber event in the past year. The math does not work, and most TPRM teams know it. They are running on spreadsheets, manually chasing questionnaire responses, and conducting annual assessments that produce a point-in-time snapshot of a continuously changing risk surface.

Regulatory pressure is intensifying at the same time. Two-thirds of institutions face demands to enhance their TPRM programs, and frameworks like DORA and updated SEC disclosure requirements are raising the stakes for third-party oversight. The gap between what regulators expect and what lean TPRM teams can deliver is widening.

Closing that gap requires coordination across assessment automation, continuous monitoring, risk intelligence, and third-party visibility platforms to scale coverage without scaling headcount.

Topics include:

• Automating vendor risk assessments to scale coverage without scaling headcount
• Moving from annual questionnaires to continuous third-party monitoring
• Prioritizing vendor oversight based on actual risk rather than treating all vendors equally

Learn how resource-constrained TPRM teams are closing the gap between regulatory expectations and operational reality.

With more than 40,000 new CVEs published in the past year alone and projections exceeding 50,000 for 2025, patching everything is impossible. Most organizations prioritize remediation by CVSS severity scores, but severity does not equal exploitability. Research shows that 32% of reported security issues have a low probability of exploitation, while some moderate-severity vulnerabilities sit on active exploit chains right now.

The shift from vulnerability management to exposure management reflects a growing recognition that context matters more than volume. Organizations need to know not just what is vulnerable but whether a vulnerability is reachable from the internet, whether an exploit exists in the wild, what business-critical assets sit in the blast radius, and how quickly an attacker could leverage it.

Operationalizing this shift requires coordination across vulnerability management, attack surface visibility, penetration testing, and exposure intelligence platforms to prioritize what attackers can actually use.

Topics include:

• Using exploit intelligence and business context to prioritize remediation over CVSS scores alone
• Mapping the gap between what is vulnerable and what is actually exploitable
• Operationalizing continuous threat exposure management across hybrid environments

Explore how leading organizations are replacing volume-based patching with risk-informed remediation that focuses on what attackers can actually use.