AI / ML

Amazon GuardDuty now includes AI Protection to monitor AWS AI services like Bedrock and SageMaker for targeted threats. The feature analyzes CloudTrail events to identify risks such as prompt injection, anomalous model invocations, and cost harvesting attacks. This provides automatic visibility into AI workload security without requiring custom tooling or manual configuration.

• GuardDuty now covers Bedrock and SageMaker for AI-specific threat detection.
• Automated monitoring detects prompt injection and anomalous invocation patterns.
• Cost harvesting attacks via excessive GPU and token consumption are flagged.
• No custom tooling or manual configuration is required for setup.

AWS Security Hub now automatically discovers and catalogs AI workloads across the organization to address visibility gaps. It leverages AWS Config resources to inventory assets from managed services like Amazon Bedrock, Bedrock AgentCore, and Amazon SageMaker without requiring extra configuration. This central view helps security teams connect AI assets to active threats and misconfigurations.

• Security Hub now provides a continuously updated, organization-wide view of AI assets.
• Automatically discovers AI workloads using AWS Config resources from managed services.
• Covers Amazon Bedrock, Bedrock AgentCore, and Amazon SageMaker out of the box.
• Requires no additional configuration for inventorying these specific managed AI services.
• Enables security teams to identify and secure AI assets they previously could not see.

Google DeepMind's Gemma-4-E2B-it is available in Amazon SageMaker JumpStart, offering a multimodal model optimized for local execution. It processes text, image, and audio inputs to generate text, featuring a built-in step-by-step reasoning mode. Key capabilities include object detection, document parsing, video understanding, native function calling, and code generation across multiple languages.

• Gemma-4-E2B-it is now accessible via SageMaker JumpStart for AWS customers.
• Model supports multimodal inputs: text, image, and audio with text output.
• Includes native function calling for agentic workflows and step-by-step reasoning.
• Optimized for efficient local execution on AWS infrastructure.
• Handles OCR, chart comprehension, and code correction with multilingual support.

AWS has added OpenAI's privacy-filter to SageMaker JumpStart, introducing a bidirectional token-classification model for PII detection and masking. This tool enables high-throughput data sanitization by labeling input sequences in a single forward pass to identify entities like emails, names, and account numbers. The model is designed for context-aware processing and can be deployed with minimal effort to support secure data workflows on AWS infrastructure.

• OpenAI's privacy-filter is now available in SageMaker JumpStart for PII detection and masking.
• Model uses bidirectional token classification to label PII spans in a single forward pass.
• Supports detection of emails, names, account numbers, dates, URLs, and secrets.
• Designed for high-throughput sanitization workflows that can run on-premises or on AWS.
• Deployable with few clicks via SageMaker JumpStart for rapid integration.

AWS has added Qwen3-VL-Embedding-2B and Qwen3-Reranker-4B to SageMaker JumpStart for building search pipelines. The embedding model handles initial recall across text, images, and video, while the reranker refines results. These models work in tandem to support cross-modal retrieval tasks on AWS infrastructure.

• Qwen3-VL-Embedding-2B supports mixed modalities including text, images, screenshots, and video.
• Qwen3-Reranker-4B provides precise re-ranking after the initial embedding-based recall stage.
• Both models are now available via SageMaker JumpStart for immediate deployment.
• Designed for comprehensive search pipelines leveraging AWS foundation model infrastructure.

Small language models struggle with structural blindness when predicting molecular properties from SMILES strings. This research introduces a Context-Augmented Prompting framework that integrates graph neural networks at inference time to provide predictive hints and explanatory subgraphs. Evaluations on MUTAG and Tox21 datasets show that enriching prompts with these graph-based tools significantly improves performance across multiple prompting configurations.

• SLMs often miss graph-topological cues when relying solely on SMILES sequences.
• The new framework enables agentic tool use by invoking a GNN expert at inference.
• GNNs provide both predictive hints with confidence and instance-specific subgraphs.
• Performance gains were confirmed on MUTAG and Tox21 across five prompting setups.