As the adoption of generative AI moves from sandbox experimentation to enterprise production, compliance, validation, and threat modeling are taking center stage. With the EU AI Act, ISO 42001, and the OWASP Top 10 for LLMs establishing strict operational standards, engineering teams face significant pressure to secure AI deployments.
To address this challenge, Amazon Web Services (AWS) and cybersecurity firm DarkHunt are co-hosting a free 45-minute live technical session on July 9th, 2026, titled "Securing AI Agents on Amazon Bedrock."
Understanding the Shared Responsibility Model
The session will guide attendees through the security architecture of the AWS Bedrock AgentCore ecosystem. Technical leads from AWS will present the cloud provider's shared responsibility model for generative AI, demonstrating how to construct secure agent execution boundaries, prevent data leakage, and configure robust guardrails.
Live Adversarial Attack Demonstration
Moving beyond conceptual architectures, DarkHunt will conduct a live adversarial validation demonstration. Security engineers will run real-time attack scenarios on a deployed Bedrock agent, identifying vulnerabilities like prompt injection, goal hijacking, and private database retrieval. The attack session will conclude with a step-by-step remediation guide showing developers how to harden the agent against these vulnerabilities.
Key Details & Registration
- Date: July 9th, 2026
- Format: Live Webinar (includes Q&A)
- Target Audience: Solutions Architects, Security Engineers, Compliance Officers, and AI Developers
Registration is free but spaces are capped to manage Q&A volume. Interest lists can be joined directly through the AWS event portal.
Compliance and Security Frameworks for Enterprise GenAI
As enterprises deploy generative AI agents on platforms like Amazon Bedrock, ensuring data privacy and security is paramount. The upcoming webinar will cover the critical aspects of model shielding, guardrails, and preventing prompt injection attacks. Because Bedrock allows businesses to customize foundation models using private data, securing the fine-tuning pipeline is essential to prevent data leakage.
Compliance experts will discuss how to align AI agent deployments with emerging frameworks like the EU AI Act, ensuring that model decisions are auditable, transparent, and do not violate internal data governance policies.
Frequently Asked Questions
What is Amazon Bedrock?
Amazon Bedrock is a fully managed service that offers choice of high-performing foundation models from leading AI startups and Amazon via a single API, along with a broad set of capabilities to build generative AI applications.
How can companies secure AI agents against prompt injection?
Companies can use Bedrock Guardrails to define system prompts, configure safety filters, and block toxic or unauthorized inputs before they reach the core model.
Will the session cover EU AI Act compliance?
Yes, the webinar will detail the compliance requirements of the EU AI Act, focusing on high-risk classifications and transparency obligations for enterprise AI deployments.
Architecting Secure LLM Applications in Enterprise Environments
Deploying AI agents in production requires a shift from traditional software security to LLM-specific vulnerability management. In the upcoming session, security architects will demonstrate how to mitigate risks such as prompt injection, data poisoning, and insecure output handling. The discussion will focus on configuring virtual private clouds (VPCs) to ensure private training data does not leave the corporate boundary when calling AWS services.
Additionally, speakers will showcase the implementation of AWS IAM policies and KMS encryption to restrict access to model endpoints and protect sensitive training datasets, ensuring compliance with strict healthcare and financial security standards.
Frequently Asked Questions
What are the risk vectors for AI agents running on Amazon Bedrock?
Primary risks include prompt injection (manipulating model instructions), data leakage of private training data, and unauthorized access to model fine-tuning APIs.
How does Amazon Bedrock protect customer data privacy?
Bedrock ensures customer data is encrypted in transit and at rest, and guarantees that private data is not used to train the underlying foundation models of external providers.
Is this security session suitable for compliance officers?
Yes, the webinar covers both technical security architectures and compliance frameworks, including auditing AI logs and meeting GDPR requirements for automated processing.
Architecting Secure LLM Applications in Enterprise Environments
Deploying AI agents in production requires a shift from traditional software security to LLM-specific vulnerability management. In the upcoming session, security architects will demonstrate how to mitigate risks such as prompt injection, data poisoning, and insecure output handling. The discussion will focus on configuring virtual private clouds (VPCs) to ensure private training data does not leave the corporate boundary when calling AWS services.
Additionally, speakers will showcase the implementation of AWS IAM policies and KMS encryption to restrict access to model endpoints and protect sensitive training datasets, ensuring compliance with strict healthcare and financial security standards.
Frequently Asked Questions
What are the risk vectors for AI agents running on Amazon Bedrock?
Primary risks include prompt injection (manipulating model instructions), data leakage of private training data, and unauthorized access to model fine-tuning APIs.
How does Amazon Bedrock protect customer data privacy?
Bedrock ensures customer data is encrypted in transit and at rest, and guarantees that private data is not used to train the underlying foundation models of external providers.
Is this security session suitable for compliance officers?
Yes, the webinar covers both technical security architectures and compliance frameworks, including auditing AI logs and meeting GDPR requirements for automated processing.
