Webinar Description
Key Takeaways
- Two-part webinar series addressing visibility challenges for AI workloads running on AWS infrastructure
- Live demonstrations of infrastructure discovery and runtime observability capabilities
- Relevant for security engineers, cloud architects, DevOps teams, and compliance officers
- Covers asset inventory, security drift detection, sensitive data exposure, and AI governance
- Focuses on enterprises with significant AWS and AI investments seeking operational control
Introduction
Demo Days: From Discovery to Observability — Your Enterprise AI on AWS (AMER) is a two-part live webinar series hosted by Wallarm that examines how organisations can establish comprehensive visibility over artificial intelligence workloads operating within AWS environments. The programme targets security professionals, cloud architects, and compliance officers working in enterprises that have deployed AI capabilities at scale. As organisations increasingly integrate AI services into their cloud infrastructure, maintaining accurate inventories and understanding runtime behaviour has become a pressing operational and security concern. The series arrives at a moment when many enterprises struggle to reconcile the rapid deployment of AI services with established governance frameworks and security protocols.
About This Event
The webinar series is structured around two complementary themes. The first session concentrates on discovery—the process of identifying all AI-related assets and services distributed across AWS accounts. The second session shifts focus to observability, examining how organisations can monitor the real-time behaviour of these workloads once they have been catalogued. Both sessions feature live product demonstrations using Wallarm’s Infrastructure Discovery and AI Hypervisor solutions, with opportunities for attendees to pose questions directly to technical experts.
This format allows participants to observe practical implementations rather than theoretical discussions. The demonstrations aim to illustrate how automated discovery tools can surface assets that may have been provisioned outside standard change management processes, and how runtime monitoring can provide context that static security assessments cannot capture.
The Visibility Challenge in Dynamic Cloud Environments
Modern AWS environments rarely remain static. Development teams provision new services, experiment with AI capabilities, and integrate third-party tools at a pace that often outstrips the ability of security and operations teams to maintain accurate asset inventories. This creates what practitioners commonly describe as operational blind spots—services and workloads that exist within the environment but fall outside established monitoring and governance frameworks.
The problem intensifies when AI workloads enter the picture. Unlike traditional applications with predictable behaviour patterns, AI services may interact with data sources, external APIs, and other infrastructure components in ways that are difficult to anticipate during initial deployment. A machine learning model that processes customer data, for instance, may establish connections to storage services or external endpoints that were not part of the original architecture review. Without continuous discovery mechanisms, these relationships can remain invisible until a security incident or compliance audit reveals them.
Security drift compounds the challenge. Configurations change, permissions expand, and new integrations appear as teams iterate on their AI implementations. What began as a well-documented deployment can evolve into something substantially different over weeks or months of active development. The gap between documented architecture and actual infrastructure state represents a significant risk factor for organisations subject to regulatory requirements or internal governance standards.
Runtime Observability and AI Governance
Discovery addresses the question of what exists within an environment. Observability addresses the equally important question of what those assets are actually doing. For AI workloads, this distinction carries particular weight. A deployed model may behave differently under production conditions than it did during testing, processing data types or volumes that trigger unexpected behaviours.
Runtime monitoring provides the contextual information necessary to understand AI activity as it occurs. This includes tracking data flows, identifying when sensitive information passes through AI processing pipelines, and detecting anomalous patterns that might indicate misconfiguration or security concerns. The ability to observe AI workloads in real time supports both immediate incident response and longer-term governance objectives.
Sensitive data exposure represents a particular concern in AI deployments. Models trained on or processing personally identifiable information, financial records, or other regulated data categories require careful handling. Without visibility into actual data flows, organisations may inadvertently violate data handling policies or regulatory requirements. Runtime observability tools can flag when sensitive data categories appear in unexpected contexts, enabling teams to investigate and remediate before exposure becomes a compliance issue.
Integration with Existing Security Infrastructure
The webinar series also addresses how discovery and observability capabilities integrate with broader security operations. Security teams typically work with existing alerting systems, incident management platforms, and compliance reporting tools. New visibility capabilities deliver the most value when they enrich existing workflows rather than creating parallel information streams that require separate monitoring.
Alert enrichment—adding contextual information to security notifications—helps analysts understand the significance of detected events. An alert indicating unusual API activity carries different implications depending on whether the affected service processes public data or sensitive customer records. By correlating discovery data with runtime observations, security teams can prioritise their response efforts more effectively.
The relationship between Kubernetes orchestration and AI workload management also features in the discussion. Many organisations deploy AI services within containerised environments managed by Kubernetes, adding another layer of complexity to asset tracking and security monitoring. Understanding how AI workloads interact with Kubernetes infrastructure, APIs, and underlying AWS services requires visibility tools designed for these interconnected environments.
Who Should Attend
The programme is designed for professionals responsible for securing and governing AI deployments in enterprise AWS environments. Security engineers and cloud security architects will find relevant material on asset discovery and threat detection. DevOps leads and platform engineers may benefit from understanding how observability tools can support operational stability alongside security objectives. Compliance managers and governance professionals can evaluate how runtime monitoring supports audit requirements and policy enforcement.
The content assumes familiarity with AWS infrastructure concepts and basic understanding of AI deployment patterns. Organisations in early stages of AI adoption may find value in understanding the visibility challenges they will encounter as deployments scale, while those with mature AI programmes can assess whether their current tooling adequately addresses the discovery and observability requirements discussed.
Conclusion
As enterprises expand their use of AI services within AWS infrastructure, the gap between deployment velocity and security visibility continues to widen. This webinar series addresses that gap directly, offering practical demonstrations of how automated discovery and runtime observability can restore operational control. For organisations grappling with asset inventory challenges, security drift, or insufficient context for AI-related security events, the sessions provide an opportunity to evaluate current approaches against emerging capabilities in the cloud security landscape.