Webinar Description
Key Takeaways
- Live demonstration of runtime security controls for AI agents operating across enterprise systems
- Focus on credential-less access approaches and intent-aware monitoring for AI-driven automation
- Coverage of multi-cloud environments including AWS, Claude, and OpenAI frameworks
- Designed for CISOs, security architects, DevOps engineers, and cloud security professionals
- Addresses compliance requirements, audit trail maintenance, and real-time risk mitigation
Introduction
As organisations accelerate their adoption of AI agents to automate complex workflows, a critical security gap has emerged: these autonomous systems increasingly interact with production infrastructure, internal databases, and SaaS applications with minimal oversight. This virtual demonstration from Akeyless examines the operational and security challenges that arise when AI agents act on behalf of enterprises, exploring how runtime controls can provide visibility and governance without impeding automation benefits.
The session targets cybersecurity professionals and technical leaders grappling with a fundamental question—how to maintain control over AI systems that are designed to operate independently.
About This Event
This technical webinar presents live demonstrations of AI agent security in action across multi-cloud platforms. Rather than theoretical discussion, the session walks through practical scenarios including coding agents accessing production environments, real-time intervention when agents attempt risky actions, and the maintenance of comprehensive audit trails that trace activity from initial prompt through to outcome.
The demonstration centres on two Akeyless technologies: Agentic Runtime Authority and SecretlessAI™. Together, these systems aim to eliminate traditional credential exposure by providing credential-less access while implementing intent-aware controls that can monitor, filter, mask, or block agent actions based on contextual risk assessment.
The Runtime Security Challenge for AI Agents
Traditional security models were designed around human users and predictable application behaviours. AI agents introduce a fundamentally different paradigm—autonomous systems that make decisions, chain together multiple actions, and interact with sensitive resources at machine speed. This creates several interconnected challenges that the session addresses directly.
Credential management becomes particularly complex when AI agents require access to multiple systems. Static credentials stored for agent use represent a significant attack surface, while overly permissive access grants agents capabilities far beyond their intended scope. The credential-less approach demonstrated in this session attempts to resolve this tension by providing just-in-time access without persistent secrets.
Equally important is the question of runtime visibility. When an AI agent queries a database or modifies infrastructure, security teams need to understand not just what action occurred, but why—tracing back to the original prompt and intent. This traceability becomes essential for both security incident response and regulatory compliance.
Multi-Cloud and Framework Considerations
The demonstration spans multiple AI frameworks and cloud platforms, reflecting the reality that most enterprises operate heterogeneous environments. Coverage includes AWS infrastructure alongside AI capabilities from OpenAI and Anthropic’s Claude, acknowledging that organisations rarely standardise on a single provider for their AI initiatives.
This multi-cloud context adds complexity to governance efforts. Security policies must translate consistently across different platforms, and audit trails need to provide unified visibility regardless of where agent activity occurs. The session explores how centralised runtime controls can address this fragmentation.
Who Should Attend
The technical depth and enterprise focus make this session most relevant for security and infrastructure professionals in medium to large organisations. CISOs and security architects will find value in understanding the governance frameworks available for AI agent deployments. DevOps leads and cloud security engineers can assess how these controls integrate with existing automation pipelines.
Compliance officers facing questions about AI governance and audit requirements will benefit from seeing how traceability mechanisms work in practice. IT managers evaluating AI agent deployments can better understand the security considerations that should inform their implementation strategies.
Conclusion
As AI agents transition from experimental tools to production systems handling sensitive operations, the security frameworks governing their behaviour must evolve accordingly. This demonstration provides a practical window into how runtime controls, credential-less architectures, and intent-aware monitoring can help organisations maintain oversight without sacrificing the efficiency gains that AI automation promises.