Category: Artificial Intelligence

This post shows you how to configure an AI model to perform structured, evidence-based vulnerability triage with the consistency of a seasoned security analyst. You’ll learn the design decisions behind five configuration sections that enforce structural verification, evidence-based scoring, and infrastructure-aware prioritization across every analysis session. Our companion post—Building your AI vulnerability harness—covered the architecture; […]

Vulnerability scanners produce findings faster than manual triage can process them. Your developers ship more code with more dependencies, and the volume of candidate findings grows with it. Many findings a scanner produces are unlikely to be exploited. The ones that matter need to reach an engineer fast, with enough evidence that they can act […]

Security teams investigating possible AI-related security events need guardrail intervention data alongside their existing security telemetry. When a guardrail identifies or blocks a prompt injection attempt or redacts sensitive data, that intervention carries additional investigative value comparable to a failed sign-in or a network intrusion alert. AWS Bedrock publishes this telemetry to AWS CloudWatch metrics […]

European organizations can run AI workloads on Amazon Web Services (AWS) while keeping data within the European Union (EU) and meeting regulatory requirements. You can now run generative AI workloads on open weight models on Amazon Bedrock in the AWS European Sovereign Cloud. We’re excited to announce the general availability of the first open weight […]

Security teams are starting to actively use AI for security work, including vulnerability triage, penetration testing, threat modeling, incident response, and code review. The promise is speed, but a security tool that moves fast and raises too many false alarms doesn’t save time. Engineers spend time on false alarms, on-call is noisier, and teams distrust […]

After talking with enterprise security leaders over the past year, one thing has become clear: the rise of autonomous AI agents is the most significant shift in security posture since the move to cloud. Organizations across every industry are adopting AI agents that authenticate on behalf of users, execute multistep workflows, and make decisions across […]

If you’re running AI agents in production, Amazon Bedrock Guardrails protects the model boundary. But your agents also invoke tools, fetch external data, and communicate with other systems. That data flows outside the model boundary, where model-level guardrails can’t reach. You can extend guardrail coverage to those interactions using three validation checkpoints built with the […]

Many teams now deploy AI agents that pull from Amazon DynamoDB tables, document repositories, software as a service (SaaS) platforms, and internal knowledge bases to answer questions and automate workflows. A key risk in these deployments is that the agent has no awareness of who’s asking, so it might return data the user shouldn’t see. […]

When deploying AI agents with Amazon Bedrock AgentCore, organizations benefit from built-in modern support for OAuth 2.0, AWS Identity and Access Management (IAM), and API key authentication through Amazon Bedrock AgentCore Gateway. However, some enterprise environments still use legacy authentication mechanisms such as HTTP Basic Authentication (Basic Auth) (RFC 7617). The extensible architecture of AgentCore […]

Customers have access to models that are continuously getting better with each new generation bringing larger context windows, stronger reasoning, and lower token costs. Getting the strongest AI-powered security will come from tools that combine the most relevant models with deep knowledge of a customer’s specific environment. AWS Continuum for code vulnerabilities (Preview) is built […]