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Securing the AI supply chain on GKE: Introducing k8s-aibom for automated AI BOMs

How should your security team manage shadow AI? Workloads deployed by developers without formal registration can often evade traditional security scanners, because organizations are reluctant to slow down development and compromise stability by demanding privileged Daemonsets, kernel-level access, and manual pod-spec edits. To break this deadlock, today we are open-sourcing k8s-aibom. This lightweight, unprivileged Kubernetes controller continuously monitors the cluster API and container environments to automatically detect running AI runtimes (like vLLM and Triton) and generate standard CycloneDX Machine Learning Bill of Materials (ML-BOMs). By providing automated, audit-grade visibility directly from runtime execution — regardless of whether the workload was formally registered — k8s-aibom can help teams safely move AI projects from pilot to production without developer integration friction. The architecture of zero friction k8s-aibom is designed from the ground up to respect both the CISO mandate for total visibility and the SRE mandate for cluster stability. It deploys as a single, unprivileged Deployment in the k8s-aibom-system namespace. It involves zero developer friction — no sidecars, no eBPF kernel modules, no privileged DaemonSets, and no modifications to existing developer pod specifications. k8s-aibom watches for AI workloads and produces BOMs. The discovery pipeline executes through four clear stages: Scrape cluster workloads: The controller continuously monitors KServe resources, Deployments, StatefulSets, DaemonSets, and Jobs across the cluster. Identify AI stacks: Advanced pattern matching inspects container images, environment variables, and command-line arguments to detect serving runtimes (vLLM, Triton Inference Server, TGI, Ollama), autonomous agent frameworks (LangChain, AutoGen, CrewAI), vector databases and RAG stores (Milvus, Qdrant, pgvector), as well as distributed training jobs and evaluation harnesses. Generate standard manifests: The controller compiles the discovered artifacts into formal OWASP CycloneDX 1.6 Machine Learning Bill of Materials (ML-BOM) documents. Export to sinks: The controller attaches the resulting ML-BOM directly to the custom resource status (status.bomDocument) of an in-cluster AIBOM Custom Resource (CR) and routes it to optional external sinks, including Google Cloud Storage buckets and external webhook endpoints. Application teams do not need to modify their pod specifications, inject sidecar containers, or alter their continuous integration and continuous delivery (CI/CD) pipelines. Furthermore, k8s-aibom treats the Kubernetes cluster state as a pure functional input: Identical cluster inputs produce byte-identical ML-BOM documents. This deterministic property makes k8s-aibom an ideal fit for GitOps workflows, enabling site-reliability engineers (SREs) to perform exact diffs and trigger precise change-detection alerts when AI dependencies drift. Where existing AIBOM tooling falls short Many AI BOM solutions offer build-time scanners producing BOMs from artifacts at rest. These tools help you track the code that was intended to be deployed. Commercial AI security platforms extend the picture with cloud-native posture management, but typically through external scanning shaped around vendor-specific data models. Few, if any, of these tools help compliance reviewers, security operations (SecOps) teams, and platform engineers understand what is running right now, what is it connected to, and how can we verify those assertions. We purpose-built k8s-aibom to bridge that gap. It produces BOMs from live cluster observation rather than artifact scanning, emits standards-conformant CycloneDX 1.6 ML-BOMs that integrate with the broader OWASP and Open Source Security Foundation (OpenSSF) supply-chain ecosystem rather than vendor-proprietary formats, and runs as an unprivileged controller on any conformant Kubernetes cluster — making it complementary to existing build-time and posture-management tooling rather than a replacement for either. The Confidence Model: Separating intent from inference For compliance auditors and SecOps engineers, raw telemetry is often noise. Standard monitoring tools indicate that a container is running, but can’t prove whether an AI model was explicitly configured by a platform engineer or dynamically pulled by an autonomous script at runtime. k8s-aibom solves this ambiguity through its deterministic Confidence Model, categorizing discovered assets into distinct tiers: Declared: Explicitly defined by the customer or developer in the workload configuration (For example, explicitly passed container arguments such as –model meta-llama/Llama-2-7b.) A “declared” confidence detection represents clear human intent. Inferred: Derived autonomously by the controller’s pattern-matching engine through deep inspection of container images, environment variables, and execution profiles. (For example, identifying ^vllm/.* container signatures.) Unresolved: Applied to workloads where an active AI presence is detected, but exact model parameters, weights, and versions can’t be deterministically established. An “unresolved” confidence detection immediately flags the workload for targeted security review. This structured taxonomy allows compliance reviewers to instantly separate explicit engineering intent from machine inference, establishing an unassailable chain of trust during audits. Immutability and least privilege: Building an audit-grade security model Auditors remain deeply skeptical of standard observability telemetry because logs and metrics can be modified, dropped, and tampered with by compromised nodes or elevated administrators. k8s-aibom establishes an audit-grade evidence trail built on strict least-privilege isolation and data immutability. The controller operates under a dedicated Kubernetes service account bound to a minimal Identity and Access Management (IAM) Workload Identity. It acts as the sole identity authorized to write BOM records to external storage sinks, requiring only roles/storage.objectCreator permissions. To satisfy the most stringent audit and evidentiary standards, the Google Cloud Storage external sink implementation enforces DoesNotExist preconditions on object creation. Once an ML-BOM is written to the Cloud Storage bucket, the object becomes cryptographically immutable. It can’t be silently overwritten, modified, or retroactively tampered with by compromised cluster actors or rogue workloads. SecOps teams gain absolute assurance that the historical audit log presented to regulators represents an unalterable record of cluster execution. Accelerating governance readiness: Mapping to global regulatory frameworks By automating the generation of standardized CycloneDX 1.6 ML-BOMs, k8s-aibom directly bridges the gap between low-level Kubernetes runtime state and high-level governance frameworks. It unblocks stalled GKE AI deployments by providing the foundational empirical data essential to major global standards: EU AI Act: Designed to help organizations align with Article 12 (automated logging and record-keeping for continuous traceability) and Article 50 (transparency obligations for AI systems). By automatically cataloging serving runtimes and agent stacks, the tool helps simplify the gathering of technical evidence that may be needed during compliance audits. NIST AI Risk Management Framework (AI RMF): Provides continuous, empirical asset visibility that can help support the Govern, Map, Measure, and Manage functions, helping shift compliance workflows from purely manual checks toward more automated asset inventory tracking. ISO/IEC 42001:Supports compliance efforts for AI management system asset discovery and tracking, reducing the reliance on manual spreadsheets or periodic snapshot audits for inventory validation. Getting started It’s rare that a technical solution like k8s-aibom can help mitigate the multi-faceted problem of shadow AI, impacting CISOs, governance, risk, and compliance teams, SecOps teams, platform engineers, and developers. To learn more by inspecting the controller, review the CRD definitions, and contribute to the open-source k8s-aibom project, please visit the k8s-aibom GitHub Repository.

BY Glen Messenger

MIN READ 6 MIN READ

Editor’s note: Some of the most interesting questions in AI are being asked by information theoreticians, around how to provide context to an emerging class of AI agents. A few weeks ago, we waded into those waters with a blog about the Open Knowledge Format, a specification that formalizes the LLM-wiki pattern into a portable, interoperable format to represent the metadata, context, and curated knowledge that modern AI systems need to operate. That blog generated a ton of interest, so we’ve decided to bring you more of the same, as part of our new “Frontier and Center” series. Today, we hear from two members of Google Data Cloud’s frontier AI team on the recurring challenge of how to systematically evaluate whether or not an agent is able to answer questions effectively based on its context. Read on for more, and watch this space for more blogs from this team. A passing grade is the least interesting thing an exam can tell you. It says the student cleared the bar; leaving you entirely in the dark about how narrow their failures were, how effortless their passes were, or what to teach next. Yet this is exactly how we evaluate AI agents. We run a fixed benchmark, calculate a score, and declare progress. In doing so, we are handing our agents a pass/fail exam when what we actually need is a map of the agent’s capabilities: a picture of the terrain that shows exactly where capability falls off, and by how much. For data agents, this map matters a lot for data discovery in search and retrieval — the unglamorous first step where an agent, handed a vague human question and a warehouse or data lake of thousands of tables and files, has to find the right datasets before it can reason over anything. Discovery is a “needle in a haystack” problem. Real users phrase their questions imperfectly, and inferring what datasets to retrieve presents a real challenge to agents. So the interesting question in evaluations is never “can the agent pass?” It is “how vague can the question get before the agent breaks?” An exam cannot easily answer that, but a map can. Today, we share an approach rooted in information theory that we’ve been leveraging to add detail and nuance, i.e., fidelity, to benchmarks, so we can better understand agents’ performance as a part of their evaluations. Along the way, the added fidelity exposed some deeper issues with the quality of emergent evaluation cases themselves. Difficulty, measured When it comes to retrieval, evaluation cases are often stratified into tiers of difficulty. This can happen organically, e.g., pervasive and enduring failure scenarios are deemed difficult. Or it can be from labels applied by humans or machines categorizing some questions as “easy” or “hard” for an agent to answer correctly, e.g., based on the context provided in the query. While this kind of sentiment-based labeling is not the only way to label test cases, it’s frequently used despite its imperfections, such as being challenging to reproduce. Despite being an industry staple, the approach of assessing every evaluation case by hand is unrealistic at scale. What we need is a rigorous approach that can modulate the difficulty of evaluation cases. We’re iterating on a meta-benchmark we call Discovery Bench: a framework that modulates an evaluation case by generating “easy” and “hard” variations of every case. This allows us to audit how close or how far an agent is from succeeding in those cases. The lever for modulating the difficulty of an input query comes via a tried-and-trusted concept that’s present across information theory and machine learning: surprisal, or the likelihood of an output given a set of inputs. In our case, a query’s surprisal represents the uncertainty that remains about the correct dataset given the query. The thinking behind our approach is simple: A term or a phrase in an evaluation query has high informative power when it sharply distinguishes the target from everything else in the corpus. Therefore, we can adjust the difficulty of evaluation cases by adding or removing terms with varying levels of informative power. Let’s work through a real example from KramaBench, a publicly available benchmark. One of KramaBench’s datasets has information about orbiting satellites, and the example query from the suite includes the following text: “…the total count of satellite major altitude changes for satellite 48445 during 2024 using TLE history.” The token “TLE” is sharply distinguishing; it points almost uniquely at the TLE_____48445 table from the dataset. Strip it, and the query degrades to “the count of satellite altitudes for satellite 48445,” whose vague phrasing now matches density tables, precise-orbit files, and decay logs alike. Surprisal makes this quantitative: rare, pointed terms carry more bits than common ones. The remaining surprisal of a query is how much uncertainty is left about its answer. As surprisal approaches zero, the query has become specific enough to pinpoint exactly one dataset. The heart of the idea behind Discovery Bench is this refinement loop, which we call iterative surprisal-based query refinement, or iSQR, which generates cases with higher or lower informative power to test where an agent can start successfully answering the query: Figure 2: The iSQR refinement loop. The crux is being able to control the challenge embedded into the evaluation case by making adjustments: Instead of one fixed phrasing per question, we generate the same question at three levels of calibrated ambiguity [high, medium, low], with each grounded in bits (not subjective opinion). We can even justify, term by term, why a word was added or removed. Difficulty stops being a property that is attributed by sentiment or classification, and becomes one we engineer. The cliff you couldn’t see Here is what Discovery Bench’s difficulty dial reveals — and what a single-phrasing benchmark structurally cannot. We have an F1 agent that’s built for recall (on Gemini 3.1 Pro). Running it against KramaBench and across the full sweep of ambiguity levels traces a curve: 0.34 at high ambiguity, 0.76 at neutral, 0.81 at medium, 0.78 at low. Figure 3: F1 swept across ambiguity — the dot versus the curve. Two findings fall out immediately (and neither were visible to a conventional eval). First, the cliffs. This query scores a perfect F1 = 1.00 at neutral phrasing — and 0.00 at high ambiguity. It is the satellite-48445 case from above: drop the distinguishing token “TLE” and the agent loses the table entirely. Same query, same agent, same ground truth; one notch vaguer and it falls off a cliff. A static benchmark tests the neutral phrasing, stamps “solved,” and reports flat ground where there is a precipice. Pass/fail was particularly misleading in that it did not just miss the cliff, but it told us the terrain was level. Second, the sweet spot. For Discovery Agent, medium ambiguity beat neutral, and low ambiguity sometimes underperformed it. More specificity is not monotonically better for the system being evaluated; there is an optimal amount of steering. That is a graded, actionable signal. This is the “how close, how hard” texture we were missing from a scalar. It tells you where to hill-climb, or improve, the agent: in our case, straight at concrete failure modes like time-sharded tables (precision collapsing to ~8% as the agent over-retrieves 21 near-identical shards for a two-table answer) and context blow-up (F1 dropping from 0.75 to 0.32 once a query triggers long search chains). The map did not just say that the agent failed, but it said where, and why. Note that our hypothesis that less ambiguity and more context (via steering terms) should improve retrieval generally holds true, but for the specific Discovery Agent being exercised, the idiosyncratic “sweet spot” meaningfully highlighted trade-offs in its implementation. We’re not alone The field is converging on meta-benchmarking and exerting greater control of how we challenge and evaluate our agents. A growing body of work uses item response theory, the latent-ability model behind standardized testing, to treat difficulty as a measured quantity rather than a label: tinyBenchmarks and metabench show that a handful of informative items reproduce a model’s full score, and PSN-IRT turns the same lens on benchmark quality itself. Others audit the ground truth directly: MMLU-Redux found that 6.49% of Massive Multitask Language Understanding (MMLU) questions are mislabeled, and Platinum Benchmarks re-cleaned ten datasets to minimize both label errors and ambiguity — the same two axes we sweep for. And ambiguity is increasingly treated as intrinsic rather than noise: AmbigQA showed that a large fraction of real questions admit multiple readings, and later work finds that apparent hallucinations often stem from query ambiguity rather than model failure. What we have not seen elsewhere is the combination: information-theoretic ambiguity sweeping applied as a meta-benchmark over live enterprise data. A benchmark we trusted turned out to be broken We built our first evaluation on kramabench-astronomy, a benchmark established in the field, and one which other teams had already leaned on for their own evals. Teams derived benchmarks from this dataset, and we hypothesized subtle issues may have been introduced over time. When we actually read the benchmarks used by teams, with Gemini’s help, we found it was wrong in meaningful ways: ground-truth tables that did not answer their query, a question whose 124 sharded tables exceeded what some teams’ retrieval APIs could even return, months specified where exact dates were required. Quietly broken ground truth means quietly wrong conclusions not just for us, but for every prior analysis built on it. This is the generalized crux of the matter: an evaluation is itself an artifact that can be defective, and almost nobody evaluates it. We instrument the agent and trust the ruler, but where do we validate that the measuring stick makes sense? When two maps disagree Now the recursive turn: If difficulty is something we generate, then we need to evaluate the generator itself; we should not trust it blindly either. So we built the same ambiguity sweep two ways: steering terms from a pure-LLM guess, versus terms grounded in TF-IDF surprisal. The two disagreed violently. At high ambiguity, the LLM-built sweep scored the agent at F1 ≈ 0.34; the grounded sweep, ≈ 0.85. One of these maps is badly distorted. The grounded one, predictably, is the more robust: surprisal gives it a footing the free-running LLM lacks. This is “evaluate your evals,” made concrete. The information-theoretic lens does not only grade the agent along a continuous axis; it grades the benchmark’s own construction, and adjudicates between the two. Evaluate your evals We have spent years optimizing agents against rulers we never measured. The bitter irony is that better models make this worse: as agents clear coarse benchmarks, the score saturates near the top and the exam loses its ability to highlight where the agent can be improved. So the call to action is uncomfortable and overdue: evaluate your evals. Read your ground truth. Treat difficulty as a measured quantity, not a label: sweep it, plot it, find the bit-width where your system breaks. Ask not just “did it pass?” but “how close was the miss, how hard was the pass, and would a slightly vaguer question have sent it off a cliff?” Build evaluations that produce signals; not just verdicts. There is a genuine tension to sit with here. Difficulty-as-entropy is only as reliable as the model that estimates the entropy. There’s a risk that if we push too hard on a measurable proxy, we optimize the ruler instead of the agent. That is not a reason to retreat to pass/fail; it is a reason to keep the evaluator under the same scrutiny as what it is evaluating. The moment we stop asking who evaluates the evaluators is the moment our maps stop being useful again. 1. Maia Polo, F. et al. tinyBenchmarks: Evaluating LLMs with Fewer Examples. ICML 2024. arxiv.org/abs/2402.14992 2. Kipnis, A. et al. metabench: A Sparse Benchmark of Reasoning and Knowledge in Large Language Models. ICLR 2025. arxiv.org/abs/2407.12844 3. Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory (PSN-IRT). AAAI 2026. arxiv.org/abs/2505.15055 4. Gema, A. P. et al. Are We Done with MMLU? (MMLU-Redux). 2024. arxiv.org/abs/2406.04127 5. Vendrow, J. et al. Do Large Language Model Benchmarks Test Reliability? (Platinum Benchmarks). 2025. arxiv.org/abs/2502.03461 6. White, C., Dooley, S. et al. LiveBench: A Challenging, Contamination-Limited LLM Benchmark. 2024. arxiv.org/abs/2406.19314 7. Min, S. et al. AmbigQA: Answering Ambiguous Open-domain Questions. EMNLP 2020. aclanthology.org/2020.emnlp-main.466 8. Lai, E., Vitagliano, G. et al. KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes. 2025. arxiv.org/abs/2506.06541

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This session is designed for global coverage across EMEA and AMER regions. Register for the May 21 Community TechTalk Apr 27 - May 1 Master Your Launch: The Apigee Production Go-Live Checklist Ensure a secure launch with the Apigee production guide. Join Nicola Cardace on May 28 to explore security guardrails, including IAM roles, mTLS configurations, and encrypted KVM migrations. Scheduled at 11 AM EDT / 5 PM CEST to support EMEA and AMER teams, this TechTalk provides the technical roadmap you need to flip the switch with absolute confidence. Register for the May 28 Community TechTalk Transforming APIs into Governed Agentic Tools on the Google Cloud Agentic Platform Turn your APIs into secure, governed agentic tools on the Google Cloud Agentic Platform. Join Specialist Christophe Lalevée on May 7 for a technical deep dive into AI productization. Scheduled at 5 PM CEST / 11 AM EDT to maximize coverage for developers across EMEA and AMER, this session explores the integration and governance frameworks required to scale enterprise-ready AI with confidence. Register for the May 7 Community TechTalk Fractional G4 VMs are Generaly Available, providing a highly efficient and cost-effective entry point for AI and graphics workloads. These new configurations, using NVIDIA virtual GPU (vGPU) technology, allow you to leverage the power of the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs in flexible, smaller increments, so you can right-size your infrastructure to match the specific demands of your applications. By providing more granular access to advanced hardware, fractional G4 VMs let you optimize resource allocation and reduce overhead without sacrificing performance. You can now select from additional GPU slice sizes for your specific needs: 1/2 GPU: Ideal for more intensive tasks such as LLM inference, robotics sensor simulation, and high-fidelity 3D rendering. 1/4 GPU: Optimized for mainstream workloads, including mid-range creative design, video transcoding, and real-time data visualization. 1/8 GPU: Great for lightweight applications such as remote desktops, productivity tools, and entry-level streaming services. Transitioning AI from a sandbox prototype to an enterprise-grade system is a major hurdle. A monolithic script won’t suffice for widespread deployment. To achieve true scale and reliability with Gemini, organizations must adopt service-oriented micro-agent architectures, establish Zero-Trust security, and implement rigorous EvalOps. Master the “Agentic Maturity Ladder” to ensure your AI & Agentic solutions are robust, secure, and ready for the real world. Watch the deep dive and read the developer blog to learn more. ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available Data scientists and developers can now combine the local productivity of VS Code with the scalable infrastructure of Google Cloud. The new Google Cloud Workbench Notebooks extension allows you to connect to and run notebooks on managed cloud environments directly within your local IDE. This integration streamlines the ML lifecycle by eliminating context switching and providing high-performance compute for complex workloads in a familiar interface. As part of our commitment to the developer ecosystem, the extension is fully open-sourced to support community-driven innovation. Install from Marketplace: GoogleCloudTools.workbench-notebooks Contribute on GitHub: colab-enterprise-vscode Apr 20 - Apr 24 Announcing the 2026 Google Cloud Partners of the Year Google Cloud is honored to celebrate the winners of the 2026 Partner of the Year awards! These awards recognize an exceptional group of partners across AI, Security, Infrastructure, and more, who have demonstrated a commitment to customer success. From global system integrators to specialized startups, these winners are leveraging the power of Google Cloud to solve complex challenges and drive digital transformation worldwide. Join us in congratulating these organizations for their innovation, collaboration, and impactful results over the past year. See the 2026 Partner Award winners Apr 13 - Apr 17 We’re excited to announce the Public Preview of Datastream’s metadata integration with Knowledge Catalog. This is the first step in our vision to provide a centralized, “single pane of glass” for all Datastream assets. The enhancement automatically synchronizes Streams, Connection Profiles, and Private Connections, eliminating data silos. It enhances discoverability, allowing you to search for Datastream assets using the same interface as BigQuery tables. Centralized governance is also provided, making your real-time data estate more transparent and easier to manage. Upgrading Apigee OPDK to 4.53 with OS Modernization Modernize your infrastructure using Google’s official, sequential upgrade path. Our Technical expert, Rakesh Talanki outlines how to upgrade Apigee OPDK to v4.53 while migrating to a supported OS (RHEL 8.x/9.x). This guide covers the “build-out” methodology, including multi-data center syncing, to ensure a stable, zero-downtime transition Read the guide Cloud Run Worker Pools and CREMA: Powering Serverless AI at Scale Google Cloud has announced the General Availability of Cloud Run worker pools, a new resource type designed specifically for pull-based, non-HTTP workloads. Unlike traditional Cloud Run services that scale based on request traffic, worker pools provide an “always-on” environment for background tasks like processing message queues or running large-scale AI inference. To support this, Google Cloud also open-sourced the Cloud Run External Metrics Autoscaler (CREMA). Built on KEDA, CREMA enables queue-aware autoscaling for worker pools, allowing them to dynamically scale based on external signals like Pub/Sub backlog or Kafka lag. Apigee Model Context Protocol (MCP) now Generally Available Expose enterprise APIs as MCP tools for agentic AI applications with the General Availability of MCP in Apigee. This update allows developers to transform APIs into AI-ready tools using OpenAPI Specifications, removing the need for local MCP servers or additional infrastructure. With managed endpoints and semantic search in API hub, you can now provide AI agents with secure, governed access to enterprise data at scale. Explore the MCP overview Apr 6 - Apr 10 Community TechTalk: Powering Retail Agents with ADK, UCP & Apigee X Move beyond basic chatbots to secure, transactional AI experiences. Join our Community TechTalk on April 16 to learn how Apigee X and Gemini build a “Trust Layer” for AI shopping assistants using UCP standards. We’ll demonstrate how to block prompt injections with Model Armor and implement cost governance via token limits to secure the path from discovery to purchase. Register for the TechTalk Implement multimodal capabilities in your AI agents Explore three new reference architectures for building sophisticated multi-agent AI systems that can process and analyze multimodal data. To analyze disparate multimodal data and produce a high-confidence classification, see Classify multimodal data. To create a fluid conversational AI that processes audio and video streams in real time, see Enable live bidirectional multimodal streaming. To consolidate fragmented multimodal data into a searchable knowledge graph, see Multimodal GraphRAG resource orchestration. Automate SecOps workflows with an agentic AI system To accelerate incident response and reduce manual toil for your security team, you need a system that can automate remediation playbooks. Our new reference architecture helps you build an AI agent that orchestrates complex triage and investigation workflows across disparate security tools, such as SIEM, CSPM, and EDR, from a single interface. See the full guide to orchestrate security operations workflows. Mar 30 - Apr 3 ASEAN Webinar | April 30: Mastering Agentic Governance at Scale with GCP As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud experts Shilpi Puri & Wely Lau for a webinar on April 30th at 11:00 AM SGT to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud. RSVP here. Mar 23 - Mar 27 Turn your API sprawl into an agent-ready catalog As organizations scale, APIs often become scattered across multiple gateways, creating “blind spots” that hinder AI adoption. To solve this, we’ve introduced two new capabilities for Apigee API hub: a new integration with API Gateway to automatically centralize API metadata into a single control plane, and a specification boost add-on (now in public preview). This add-on uses AI to enhance your API documentation with the precise examples and error codes that AI agents need to function reliably. Read the full blog post to get started. Webinar | April 16: AI Command & Control As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud expert Satyam Maloo for a webinar on April 16th at 11:00 AM IST to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud. RSVP here. Modernizing and Decoupling Event Ingestion with Apigee In modern cloud-native architectures, decoupling producers from consumers is critical for building resilient systems. While Google Cloud Pub/Sub provides a scalable backbone, exposing it directly to external clients can introduce security and management overhead. This new guide explores how to leverage Apigee as an intelligent HTTP ingestion point. Learn how to handle security, mediation, and traffic control before messages reach your internal bus using the PublishMessage policy or Pub/Sub API. Read the full guide. Mar 16 - Mar 20 Gemini-powered Assistant in BigQuery Studio Gets Context-Aware Upgrades The Gemini-powered assistant in BigQuery Studio has been transformed into a fully context-aware analytics partner, supporting your entire data lifecycle. The new capabilities include intelligent resource discovery, which uses Dataplex Universal Catalog search to find resources across projects and deep dive into metadata using natural language. You can now automate tasks, such as scheduling production-grade queries directly through the chat interface, and instantly troubleshoot long-running or failed jobs with root cause analysis and cost control auditing. Explore the full range of what the assistant can do. Mar 9 - Mar 13 Want to use Gemini to develop code and don’t know where to start? This article includes a couple of examples of developing code with Gemini prompts; it identified changes that were needed to be made to get the code working. The article also refers to other examples that are available on github. Mar 2 - Mar 6 Introducing Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model. Built for high-volume developer workloads at scale, 3.1 Flash-Lite delivers high quality for its price and model tier. Gemini 3.1 Flash-Lite can tackle tasks at scale, like high-volume translation and content moderation, where cost is a priority. And it can also handle more complex workloads where more in-depth reasoning is needed, like generating user interfaces and dashboards, creating simulations or following instructions. Starting today, 3.1 Flash-Lite is rolling out in preview to enterprises via Vertex AI and developers via the Gemini API in Google AI Studio. TechTalk: Implementing Device Authorization Grant (RFC 8628) for Apigee Learn how to authorize “headless” devices like Smart TVs or AI agents that lack keyboards and browsers. Join our Community TechTalk on March 19 (5PM CET / 12PM EDT) to go under the hood of Apigee X/Hybrid. We’ll cover the real-world mechanics of state management, polling, and human-in-the-loop security patterns for devices and autonomous agents. Register for the TechTalk Feb 23 - Feb 27 Pro-level image generation gets faster and more accessible with Nano Banana 2 Nano Banana 2 is our state-of-the-art image generation and editing model. It delivers Pro-level image generation and editing at the speed you expect from Flash — making the quality, reasoning, and world knowledge you loved about Nano Banana Pro more accessible. Learn more about the model here. The Intelligent Path to Compliance: Transforming Regulatory QC with Google Cloud Reducing “Refuse to File” (RTF) risks and submission cycle times is critical for life sciences leaders. Google Cloud’s Regulatory Submission Semantic QC Auditor leverages Gemini and RAG architecture to transform Quality Control from a manual burden into an active, intelligent workflow. By automating semantic cross-referencing, narrative coherence checks, and dynamic guidance-based auditing, this solution ensures rigorous accuracy and auditability. Operating within a secure GxP-ready environment, it empowers teams to detect subtle inconsistencies and generate remediation plans without sacrificing data privacy. Learn more. Stop typing, start interacting! The Gemini Live Agent Challenge is here. Build immersive agents that can help you see, hear, and speak using Gemini and Google Cloud. Compete for your share of $80,000+ in prizes and a trip to Google Cloud Next ‘26! Submissions are open from February 16, 2026 to March 16, 2026. Learn more and register at geminiliveagentchallenge.devpost.com Feb 9 - Feb 13 Introducing Gemini 3.1 Pro on Google Cloud. 3.1 Pro is a noticeably smarter, more capable baseline for complex problem-solving. We’re shipping 3.1 Pro at scale, building upon our goal to help you transform your business for the agentic future. Learn more about the model’s capabilities here. Gemini 3.1 Pro is available starting today in preview in Vertex AI and Gemini Enterprise. Developers can access the model in preview via the Gemini API in Google AI Studio, Android Studio, Google Antigravity, and Gemini CLI. Automate Storage Compatibility with GKE Dynamic Default Storage Classes Managing storage across mixed-generation VM clusters in GKE just got easier. With the new Dynamic Default Storage Class, Google Kubernetes Engine automatically selects between Persistent Disk (PD) and Hyperdisk based on a node’s specific hardware compatibility. This abstraction eliminates the need for complex scheduling rules and manual pairing, ensuring your volumes “just work” regardless of the underlying infrastructure. By defining both variants in a single class, you reduce operational overhead while maintaining peak performance and cost-efficiency across your entire cluster. Explore automated disk type selection Community TechTalk: AI-Powered Apigee Development with strofa.io Join the Apigee community on February 26 for a deep dive into strofa.io. Guest speaker Denis Kalitviansky will demonstrate how this new AI-powered tool automates and orchestrates Apigee development, from local emulators to large-scale hybrid environments. Discover how to scale your API management and streamline team collaboration using the latest in AI-driven automation. Register now to reserve your spot. Jan 26 - Jan 30 Simplify API Governance with Native OpenAPI v3 Support Eliminate integration debt and accelerate deployment velocity with the General Availability of OpenAPI v3 (OASv3) support for API Gateway and Cloud Endpoints. You no longer need to downgrade modern specifications to OASv2. Instead, you can now define API contracts and enforce critical policies—including telemetry, quotas, and security—using native Google-specific extensions directly within your OASv3 files. This update ensures your APIs are secure by design while remaining fully compatible with the modern developer ecosystem and Google Cloud’s AI services. Get started with OpenAPI v3 on API Gateway and Cloud Endpoints. Accelerate API Testing with the New Open Source API Tester Start validating your APIs with API Tester, a simple, YAML-based Test Driven Development (TDD) framework. Designed for the Apigee community, this tool allows you to write human-readable tests, run them instantly via a web client or CLI, and perform deep unit testing on Apigee proxies. With native support for JSONPath assertions and Apigee shared flows, you can verify everything from payload data to internal variables like proxy.basepath without leaving your terminal. Explore the API Tester guide and start testing your proxies today. Secure Sensitive Data with Kubernetes Secrets in Apigee hybrid Enhance security in Apigee hybrid by accessing Kubernetes Secrets directly within your API proxies. This hybrid-exclusive feature keeps sensitive credentials within your cluster boundary and prevents replication to the management plane. It supports strict separation of duties: operators manage secrets via kubectl, while developers reference them as secure flow variables—ideal for high-compliance and GitOps workflows. Implement Kubernetes Secrets in your hybrid proxies. See the Console in a Whole New Light: Dark Mode is Now Generally Available in Google Cloud Elevate your cloud management workflow with Dark Mode, now generally available in the Google Cloud console. We have delivered a modern, cohesive, and accessible experience reimagined for maximum comfort and productivity—especially during extended working hours and low-light environments. Dark Mode can be enabled automatically based on your operating system’s preference, or manually through the Settings -> Appearance menu. Switch to Dark Mode today to enjoy a modern, comfortable, and productive environment! Apigee X Networking: PSC or VPC Peering? Deciding how to connect Apigee X? Watch this video to compare Private Service Connect and VPC Peering. We break down northbound and southbound routing, IP consumption, and how to reach targets on-prem or in the cloud. Learn to simplify your architecture and avoid common networking “gotchas” for a smoother deployment. Watch the video. Jan 19 - Jan 23 Bridge the Gap: Excel-to-API Conversion in Apigee Portals Give your customers more ways to connect! This new article by Tyler Ayers explores how to extend the Apigee Integrated Portal to support direct Excel file uploads. By leveraging SheetJS and custom portal scripts, you can enable users to upload spreadsheets, preview data, and submit it directly to your APIs, all without writing a single line of integration code themselves. It’s a powerful way to simplify onboarding for those who aren’t yet API-ready. Learn how to build it. Elevate your applications with Firestore’s new advanced query engine We have fundamentally reimagined Firestore with pipeline operations for Enterprise edition. Experience a powerful new engine featuring over a hundred new query features, index-less queries, new index types, and observability tooling to improve query performance. Seamlessly migrate using built-in tools and leverage Firestore’s existing differentiated serverless foundation, virtually unlimited scale, and industry-leading SLA. Join a community of 600K developers to craft expressive applications that maximize the benefits of rich queryability, real-time listen queries, robust offline caching, and cutting-edge AI-assistive coding integrations. Learn more about Firestore pipeline operations.

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