Cloud / Hybrid Cloud Security

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    • Cloud Misconfigurations Aren’t Mistakes. They’re a Systemic Problem. Here’s How to Fix the System.

    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.

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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 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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    The Assets You Don't Know About Are the Ones Getting Breached. Solving the Visibility-first Problem.

    Most organizations cannot produce a complete, accurate inventory of their external-facing assets. Shadow IT, forgotten cloud instances, unmonitored APIs, development environments left exposed, and acquired company infrastructure that was never integrated into security tooling all create blind spots. Attackers do not need to find a zero-day when a staging server with default credentials is sitting on a public IP. The assets that security teams do not know about are, by definition, the ones that are not being monitored, patched, or protected.

    Attack surface management starts with a premise that most vulnerability management programs skip: you cannot secure what you have not discovered. Addressing this requires coordination across ASM, CTEM, vulnerability management, penetration testing, and cloud security platforms to build a continuous view of the external attack surface as an attacker sees it, not as the asset inventory says it should look. The gap between those two views is where breaches happen. Organizations that have adopted this approach report finding assets they did not know existed, exposures that had persisted for months, and risk concentrations in areas their existing tools were not scanning.

    Topics include:

    • Continuously discovering and attributing external-facing assets beyond the known inventory
    • Identifying shadow IT, orphaned cloud resources, and unmonitored development environments
    • Prioritizing discovered exposures based on exploitability, business context, and attacker perspective

    Discover how organizations are closing the gap between what they think their attack surface looks like and what it actually is.

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