Category: Generative AI

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 […]

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 […]

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 […]

AI coding agents are part of the developer toolchain. Tools like Kiro and Claude Code generate features, tests, and code refactors from natural-language prompts. A single agent can open dozens of pull requests (PRs) across your repositories in an afternoon. That productivity comes with a trade-off: agents optimize for task completion at machine speed with […]

Amazon is sharing new findings about how a threat actor linked to the Democratic People’s Republic of Korea (DPRK) is targeting open source software libraries, the shared building blocks that companies around the world use to develop applications. Amazon Threat Intelligence has linked several recent compromises of popular Node Package Manager (NPM) libraries to the […]

System prompts form the foundation of generative AI applications. A system prompt is a collection of instructions and operational context provided to a large language model (LLM) that shapes how the model behaves and interacts with users and tools. System prompts often contain proprietary information, including role definitions, behavioral guidelines, tool descriptions and usage instructions, […]

Agents have agency: they adapt and find multiple ways to solve problems. This autonomy creates a fundamental security challenge: the large language model (LLM) at the heart of the agent is non-deterministic, and its decisions can’t be predicted or guaranteed in advance. It can hallucinate harmful actions with complete confidence. It’s vulnerable to prompt injection […]

The financial services industry (FSI) is using AI to transform how financial institutions serve their customers. AI solutions can help proactively manage portfolios, automatically refinance mortgages when rates decrease, and negotiate insurance premiums for customers. However, this adoption brings new governance, risk, and compliance (GRC) considerations that organizations need to address. To help FSI customers […]

We have released our latest compliance guide, ISO/IEC 42001:2023 on AWS, which provides practical guidance for organizations designing and operating an Artificial Intelligence Management System (AIMS) using AWS services. As organizations deploy AI and generative AI workloads in the cloud, aligning with globally recognized standards such as ISO/IEC 42001:2023 becomes an important step toward strengthening […]

A Monday morning security alert flags unauthorized access attempts, security group misconfigurations, and AWS Identity and Access Management (IAM) policy violations. Your team needs answers fast. Security teams are using Kiro and Amazon Q Developer to handle repetitive tasks—scanning resources, drafting policies, and researching Common Vulnerabilities and Exposures (CVEs)—so engineers can focus on risk decisions […]