Webinar Description
Key Takeaways
- Two-part webinar series addressing visibility gaps in enterprise AI workloads running on AWS infrastructure
- Live demonstrations of infrastructure discovery and runtime observability tools for AI governance
- Focus areas include Kubernetes security, API protection, sensitive data exposure detection, and security alert enrichment
- Designed for security engineers, cloud architects, DevOps leads, and compliance officers at medium to large enterprises
- Addresses operational challenges around infrastructure drift, hidden assets, and AI behaviour monitoring
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 and governance over AI workloads operating within AWS environments. The programme targets security professionals, cloud architects, and DevOps engineers responsible for protecting enterprise infrastructure where artificial intelligence applications are increasingly deployed. As organisations accelerate AI adoption across cloud platforms, the challenge of maintaining security oversight has grown substantially more complex, making the topics covered in this series particularly timely for teams grappling with dynamic, distributed computing environments.
About This Event
The webinar series is structured around two complementary sessions that progress from foundational asset discovery through to runtime observability. The first component focuses on identifying all AI-related assets and services distributed across AWS accounts, while the second examines real-time monitoring of AI workload behaviour, with particular attention to Kubernetes environments where many enterprise AI applications operate.
Each session features live product demonstrations of Wallarm’s Infrastructure Discovery and AI Hypervisor solutions. These demonstrations are designed to illustrate practical workflows for uncovering hidden assets, identifying security risks, and establishing ongoing monitoring capabilities. The interactive format includes live question-and-answer segments, allowing attendees to explore specific implementation scenarios relevant to their environments.
Infrastructure Discovery and Asset Visibility
A central theme throughout the series is the challenge of maintaining accurate inventories in dynamic cloud environments. AWS deployments frequently evolve as development teams provision new services, scale existing workloads, and experiment with emerging AI capabilities. This constant change creates conditions where security teams may lack complete awareness of what assets exist, how they are configured, and whether they introduce risk.
Infrastructure drift represents a particularly persistent problem in enterprise AWS environments. As configurations diverge from documented baselines, exposed services and misconfigured resources can accumulate without detection. The discovery capabilities demonstrated in this series address these gaps by systematically scanning AWS accounts to surface AI-related services that may have been deployed outside standard governance processes.
The relationship between infrastructure discovery and API security receives significant attention. Modern AI workloads typically expose functionality through APIs, and these interfaces represent both operational necessities and potential attack surfaces. Understanding which APIs exist, how they are secured, and what data they handle forms a critical foundation for any comprehensive AI governance programme.
Runtime Observability for AI Workloads
Beyond static discovery, the series examines how organisations can monitor AI workloads during active operation. Runtime observability provides insight into what AI systems are actually doing—the data they process, the decisions they make, and the external services they communicate with. This operational visibility proves essential for detecting anomalous behaviour, identifying policy violations, and responding to security incidents.
Kubernetes environments present distinct observability challenges due to their ephemeral nature. Containers spin up and terminate rapidly, workloads scale dynamically, and network traffic patterns shift continuously. Effective monitoring in these environments requires tooling specifically designed to handle this fluidity while still providing coherent security insights.
Sensitive data exposure detection represents another key capability explored during the demonstrations. AI workloads frequently process confidential information, and understanding where such data flows—both within internal systems and to external services—helps organisations maintain compliance with data protection requirements and internal governance policies.
The Convergence of AI Governance and Cloud Security
The topics addressed in this webinar series reflect broader industry developments around AI governance in enterprise environments. As organisations move beyond experimental AI projects toward production deployments, the need for systematic oversight has become increasingly apparent. Security teams that previously focused primarily on traditional application and infrastructure protection now find themselves responsible for AI systems with distinct risk profiles.
AI workloads introduce governance considerations that differ from conventional software. These systems may behave unpredictably, process sensitive data in novel ways, and interact with external services that introduce supply chain risks. The observability approaches demonstrated in this series aim to provide security teams with the visibility necessary to understand and manage these emerging risk categories.
The emphasis on AWS specifically reflects the platform’s prominence in enterprise AI deployments. Many organisations have standardised on AWS for machine learning and AI workloads, leveraging services designed for model training, inference, and data processing. Security tooling that integrates natively with AWS environments can provide more comprehensive coverage than platform-agnostic alternatives that lack deep visibility into cloud-specific services.
Who Should Attend
The programme is designed for professionals at medium to large enterprises who bear responsibility for securing cloud infrastructure and AI deployments. Security engineers and cloud security architects will find the technical demonstrations directly applicable to their operational responsibilities. DevOps leads responsible for Kubernetes environments can gain insight into how security observability integrates with container orchestration workflows.
IT security managers seeking to establish or improve AI governance programmes may benefit from understanding the capabilities available for visibility and monitoring. Compliance officers concerned with data protection and regulatory requirements can explore how observability tooling supports audit and reporting needs. The content assumes familiarity with AWS services and cloud security fundamentals, making it most suitable for practitioners with existing experience in enterprise cloud environments.
Operational Challenges Addressed
The webinar series targets several interconnected operational challenges that security teams commonly encounter. Lack of visibility into AI workloads creates blind spots where risks can accumulate undetected. Difficulty tracking new assets and services in rapidly changing AWS environments leads to incomplete security coverage. Infrastructure drift and exposed services introduce vulnerabilities that may persist until discovered through incident response rather than proactive monitoring.
Understanding AI behaviour at runtime addresses the gap between knowing what assets exist and knowing what those assets actually do. Security alert enrichment—providing additional context around detected events—helps teams prioritise responses and investigate incidents more effectively. Together, these capabilities support a more comprehensive approach to AI security that spans the full lifecycle from deployment through ongoing operation.