Artificial Intelligence (AI)

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    • From Reactive to Resilient: Agentic AI and the Future of Cyber Defense

    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.
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    Your SOC Has a Retention Problem. Your Tooling Might Be the Cause.

    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.

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    Shadow Data, AI Pipelines, and the 802,000 Files You’re Oversharing Right Now

    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.

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    AI in the SOC: Separating the Tools That Actually Work From the Ones That Add More Noise

    Every security vendor now claims AI capabilities. For SOC teams already processing thousands of alerts per day, the promise is appealing: automated triage, intelligent prioritization, faster investigations. The reality is more complicated. Poorly implemented AI can generate its own layer of noise, create false confidence in automated decisions, and introduce opaque reasoning that analysts cannot validate or trust.

    The SOC teams seeing real results from AI are the ones asking the right questions before deploying it. They are auditing data quality first, defining what “automated” should and should not mean for their environment, and measuring whether AI is reducing time-to-resolution or just shifting where analysts spend their time.

    Getting this right requires alignment across detection, triage, investigation, and automation layers of the SOC – from SIEM and XDR to SOAR, MDR, and AI-driven analytics platforms.

    Topics include:

    • Evaluating AI-driven SOC tools based on measurable outcomes, not vendor claims
    • Addressing data quality and pipeline readiness before deploying AI-powered detection
    • Defining the right division of labor between automated triage and human investigation

    Join us for an honest look at where AI is delivering real value in security operations and where it is falling short.

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    • AI Regulations Are Moving Faster Than Your Compliance Framework. A Practical Catch-up Plan.

    The EU AI Act is in effect. NIST and ISO frameworks are expanding to cover machine identity hygiene and AI decision-making transparency. The SEC now requires public companies to disclose material cybersecurity incidents within four business days. And most GRC teams are still operating frameworks that were designed for a slower, more predictable regulatory cycle. The gap between what regulators expect and what compliance programs can deliver is growing with every new mandate, and AI adoption across the enterprise is accelerating the timeline.

    This is not just a documentation problem. AI introduces compliance challenges that existing GRC workflows were never designed to handle: models trained on data with unclear provenance, automated decisions that need audit trails, and AI deployments that span business units with no centralized oversight. Addressing this requires coordination across GRC platforms, data security tooling, AI governance solutions, and risk quantification approaches to build programs that keep pace with regulatory change. Organizations that treat AI governance as a future initiative rather than a current requirement are accumulating risk that becomes harder and more expensive to remediate with each quarter that passes.

    Topics include:

    • Mapping current GRC frameworks against emerging AI-specific regulatory requirements
    • Building audit trails and governance structures for AI-driven decisions and data usage
    • Moving from periodic compliance reviews to continuous assurance models

    Join us for a practical look at how GRC teams are updating their programs to keep pace with the regulatory demands of enterprise AI adoption.

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    AI Models Are Trained on Your Sensitive Data. Who's Watching What Goes In and What Comes Out?

    Three-quarters of organizations now run AI in production environments. Ninety-nine percent reported at least one attack on their AI systems within the past year. The data flowing into and out of these models, training datasets, fine-tuning inputs, prompt histories, and generated outputs, represents a category of data exposure that most security programs were never built to monitor. When a large language model is fine-tuned on customer records, internal strategy documents, or proprietary code, the question of who can access what the model learned becomes urgent.

    AI pipelines create data exfiltration paths that blend into legitimate business operations. A model query that returns sensitive information is not a traditional data breach, but the impact can be identical. Addressing this requires coordination across DSPM, DLP, SaaS security, cloud security, and AI data governance platforms to build visibility into what data feeds AI systems, what those systems can reveal through inference, and whether access controls exist at each stage of the pipeline. The data security perimeter has expanded, and the tools designed to protect structured databases and file shares are not sufficient for the unstructured, dynamic data flows that AI depends on.

    Topics include:

    • Gaining visibility into what sensitive data enters AI training pipelines and inference endpoints
    • Extending data loss prevention and SaaS security strategies to cover AI-specific exfiltration vectors
    • Building governance frameworks for AI data flows across development, staging, and production

    Discover how organizations are closing the data security gap created by enterprise AI adoption before it becomes their next breach headline.

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    Context Lives in Five Different Tools. That's Why Your Incident Response Takes Hours Instead of Minutes.

    The average enterprise deploys 28 security monitoring tools. Each one generates its own alert stream, uses its own console, and stores context in its own format. When an incident occurs, analysts do not start by investigating. They start by assembling. They pull logs from the SIEM, check the EDR console, cross-reference the firewall, open the ticketing system, and manually piece together a timeline. This context-switching burns time, introduces errors, and extends incident response from minutes to hours. The tools designed to improve security are, in practice, fragmenting the information analysts need most.

    The organizations with the fastest response times are not necessarily using better tools. They are using fewer consoles, shared context, and automated enrichment that presents a unified investigation surface. Addressing this requires coordination across SIEM, XDR, SOAR, MDR, security analytics, and data enrichment platforms to collapse the distance between alert and decision. When an alert arrives pre-correlated with asset data, user context, threat intelligence, and historical activity, analysts skip the assembly phase and go straight to decision-making. That is the difference between a 15-minute investigation and a three-hour one.

    Topics include:

    • Reducing context-switching by consolidating investigation workflows across security tools
    • Automating alert enrichment with asset, identity, and threat intelligence context at the point of triage
    • Building incident response workflows that prioritize speed-to-decision over tool-by-tool investigation

    Learn how SOC teams are cutting investigation time by unifying the context that is currently scattered across their security stack.

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    AI-generated Phishing Looks Nothing Like the Phishing You Trained Your Users to Spot

    Security awareness training taught users to look for misspelled words, awkward grammar, and suspicious sender addresses. AI has eliminated all three. AI-generated phishing emails are grammatically polished, contextually relevant, and increasingly personalized using data scraped from social media, corporate websites, and previous breaches. The World Economic Forum's 2025 Global Cybersecurity Outlook found that 42% of organizations reported a sharp increase in social engineering and phishing attacks, and AI is the primary driver. The phishing playbook that employees were trained to recognize no longer matches what is arriving in their inboxes.

    This shift has implications beyond awareness training. Secure email gateways that rely on known signatures and reputation scoring struggle with AI-generated content that is unique to each target. Business email compromise attacks use socially engineered text rather than malicious attachments, bypassing controls designed for payload-based threats. Addressing this requires coordination across email security, behavioral analysis, identity signals, and AI-driven detection platforms to build layered defenses that catch threats traditional tools miss, combined with updated training programs that reflect what modern phishing actually looks like.

    Topics include:

    • How AI-generated phishing bypasses traditional email security and awareness defenses
    • Layering behavioral analysis and identity-based signals with AI-powered detection
    • Updating security awareness programs to reflect current social engineering techniques

    Explore how organizations are adapting their email security strategies for phishing attacks that no longer look like phishing.

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