Cloud platforms emphasized preventive controls and automation. AWS introduced VPC Encryption Controls to audit and enforce encryption in transit inside and between VPCs, while Google Cloud rolled out BigQuery AI, unifying ML, vector search, and agentic functions directly where data lives. The thread running through both moves is platform hardening—making secure defaults easier to achieve and reducing integration overhead for AI-heavy operations.
Encryption and Governed Upgrades
AWS is extending encryption visibility and enforcement into customer networks with VPC Encryption Controls. The capability surfaces plaintext allowances, turns on hardware-backed AES‑256 on supported paths, and produces auditable logs for standards such as HIPAA and PCI DSS. The aim is to reduce misconfiguration risk and simplify attestations; application‑layer cryptography still remains essential end to end.
Routine maintenance also gets safer. With an organization‑wide upgrade rollout policy for Aurora and RDS, teams can sequence minor version upgrades across accounts and tags—starting in development, pausing for validation, then moving to production with health notifications along the way. And for private connectivity, API Gateway REST APIs can now attach directly to internal ALBs, cutting a hop and bringing Layer‑7 routing and health checks into API flows via a new ALB integration.
Image lifecycle management becomes more controlled as AWS adds flexible distribution for AMIs. Teams can stage multi‑region, multi‑account rollouts with approval pauses using Image Builder distribution workflows, avoiding unnecessary rebuilds while retaining step‑level visibility and governance.
Agentic Responders and Developer Copilots
Incident responders get automation at first touch. AWS integrated an agentic investigator into Security Incident Response, which gathers and correlates CloudTrail, IAM, EC2, and cost signals, asks clarifying questions, and produces concise summaries to accelerate containment. In parallel, AWS published the Scoping Matrix for agentic AI, mapping controls across identity, memory, logging, model governance, agency perimeters, and orchestration as autonomy increases from prescribed to fully autonomous systems.
Day‑to‑day operations gain embedded assistance. Console users can launch investigations of failed tasks, pod events, and rollbacks using AI troubleshooting in ECS and EKS via Amazon Q Developer. For analytics and ML work, serverless SageMaker notebooks add a built‑in agent that generates SQL and Python and runs at scale on Athena for Apache Spark—raising productivity while calling for least‑privilege access, logging, and code review of generated scripts.
Inside notebooks, the new SageMaker Data Agent converts natural‑language prompts into stepwise plans and executable code for transformation, feature engineering, and prototyping. Beyond the tooling, Google Cloud describes an R&D‑oriented, multi‑agent design for discovery and preclinical optimization in life sciences, coordinating models such as MedGemma, TxGemma, an orchestrator, and structure‑based design tools in an agentic framework intended to shift costly experiments into reproducible in‑silico loops.
Scaling Clusters and Context for AI
Google Cloud detailed how it built an experimental GKE cluster with 130,000 nodes to probe control‑plane throughput, scheduling under heavy preemption, and elasticity for large AI/ML workloads. Read‑path optimizations, a Spanner‑backed key‑value store, and job‑level queueing via Kueue underpinned the results, pointing to continued investment in multi‑cluster orchestration and high‑performance networking.
On the AWS side, Kubernetes control‑plane behavior becomes more predictable with EKS Provisioned Control Plane, letting teams pre‑provision capacity tiers for spikes and ultra‑scale clusters. To make AI assistants cluster‑aware without custom hosting, EKS and ECS now offer fully managed MCP servers in preview, standardizing how developer tools retrieve real‑time project and cluster context with IAM and CloudTrail baked in.
Data Platforms and Model Guardrails
Lakehouse users get performance and governance updates. Amazon EMR 7.12 adds Iceberg v3 with row‑level delete semantics, reduced I/O, and improved change tracking, alongside integrations for encryption and Lake Formation. In the analytics warehouse, BigQuery AI (linked above) brings vector search, generative functions, and role‑specific agents directly to data via SQL, aiming to shorten paths from feature engineering to inference.
For safer generative applications, AWS expanded formal‑methods guardrails and multimodal ingestion. Automated Reasoning checks now include natural‑language test generation to speed policy refinement and reduce hallucinations via Automated Reasoning test Q&As. And for interactive vision use cases, Bedrock Data Automation adds synchronous image processing for low‑latency, structured outputs through Data Automation. Together these features aim to tighten safety checks while simplifying real‑time multimodal workflows.