π₯ Top Story
CSPM adoption jumped 60%. Tickets stayed open.
Cloud Security Posture Management (CSPM) adoption jumped more than 60% over the past year, and over 65% of organizations reportedly now use some form of CSPM, driven by major data breaches, tightening compliance requirements, and the need for continuous visibility into cloud environments. Yet the article argues that more tooling hasn't translated into more remediation, since tickets and findings keep piling up unresolved. The core problem isn't a shortage of tools or alerts, it's a shortage of time and operational capacity to act on what those tools surface. A cloud security finding only becomes useful once someone decides it matters, assigns an owner, and follows it through to closure. The piece argues that teams need a recurring triage cadence, clear ownership, and business context attached to each finding to turn raw alerts into actual remediation. Without that operating rhythm, CSPM tooling just adds more noise to an already overwhelmed queue.
π‘ Why it matters: Buying another CSPM scanner without a triage cadence and clear ownership just adds more unresolved findings to the backlog; the operational process for deciding what matters and assigning owners reduces risk more than the tool itself.
π Read more Β· The New Stack
Kubernetes & Cloud Native
Extending Amazon ECS Express Mode to Build an Optimal Container Environment
AWS Containers
This AWS Containers blog post walks through three hands-on examples for extending Amazon ECS Express Mode beyond its default configuration. Express Mode is a feature that, given just a container image, automatically provisions a Fargate-based ECS service with an auto-generated domain name, an SSL/TLS-enabled Application Load Balancer, auto scaling, logging, and networking, and it can consolidate up to 25 Express Mode services behind a single ALB to save cost. The first example turns on ECS Exec so operators can shell directly into a running container for debugging. The second adds a scheduled scaling policy on top of the default traffic-based auto scaling, so capacity can be adjusted for specific times of day. The third adds a FireLens log router as a sidecar container to set up log routing beyond Express Mode's default logging. Because every resource Express Mode provisions stays fully accessible and editable within your own account, these customizations can be layered on top of the defaults without needing to migrate off Express Mode.
π‘ Because Express Mode keeps every auto-provisioned resource directly editable while defaults stay simple, teams can start fast and layer on production-grade needs, exec-based debugging, predictable scheduled scaling, custom log routing, without a migration off Express Mode later.
The Software Supply Chain Is Under Siege. Devs Are Still the First Line of Defense
Docker
This Docker blog post summarizes highlights from a 2026 software supply chain security research report by Omdia, with Docker as one of the sponsors, based on a survey conducted in February 2026. 77% of organizations reported experiencing a software supply chain incident in the prior 12 months, and AI technology ranked as the top supply chain risk at 40%, ahead of third-party and open-source code (39%) and software dependencies (38%). 45% of respondents said they don't feel they have robust supply chain security in place, and the report cites newer attack patterns distinct from traditional CVEs, such as the Shai-Hulud campaign, which uses stolen credentials to weaponize well-known packages and plant infostealer malware in CI pipelines or on developer laptops. When incidents do happen, the most common impacts were unauthorized access to applications and data (46%), SLA impact from remediation work (37%), and theft of developer credentials, secrets, or keys (35%). Respondents said SBOMs, software bills of materials, help most with more efficient vulnerability mitigation (73%), building security controls and processes (72%), and meeting compliance requirements (68%). Shifting security left so developers can secure their own code is a priority for 98% of organizations, and the top priority for 32% of those, reinforcing the report's conclusion that developers are now the practical front line of supply chain security.
π‘ With nearly half of supply chain incidents leading to unauthorized access or credential theft, integrating SBOM adoption and shift-left security into developer workflows with minimal friction is no longer just a security team concern, it's a practical design requirement for anyone building CI/CD pipelines and deployment processes.
You canβt debug what you canβt see β Observability for AI Agents
CNCF
This CNCF blog post is a hands-on observability guide from a team that has been running AI agents in production for months, and it argues that the hard part isn't building agents β it's understanding what they're doing when they go wrong. Unlike traditional apps, agents don't crash with a stack trace; they loop, hallucinate, burn tokens, and produce plausible-looking output that's subtly wrong, so standard APM questions ("is it up, how fast, are there errors") don't apply β the real questions are why a task cost so much more than usual, why an agent called the same tool repeatedly, or whether it actually did what it claimed. The post lays out three pillars for agent observability: traces (a full per-session decision history covering every model call, tool invocation, and sub-agent delegation, delivered non-blocking via a batch exporter to a backend like Langfuse), costs (visibility at both the per-session and per-agent-over-time level, with hard iteration caps and per-tool budgets acting as "pre-flight circuit breakers" against runaway loops, backed by alerts when a session's cost exceeds a multiple of its rolling average), and an append-only audit log of every tool call, governance decision, and memory operation with sensitive data sanitized before it's written. Practical additions include a "doctor"-style one-shot health-check command covering model connectivity, vector store reachability, and trace backend status, plus automated session review that flags anomalous sessions (loops, high cost, tool errors) for human attention instead of expecting anyone to read every session by hand. It also warns that Prometheus-style metrics must stay low-cardinality β never put a unique ID like a session ID in a label, since that can crash the metrics server β and that metrics are for real-time alerting while traces are for debugging, not substitutes for each other. It closes with three lessons: cost spikes are the best canary for bugs, traces and metrics serve different jobs so you need both, and PII redaction in audit logs is non-negotiable since that trail gets queried during incident reviews.
π‘ For any team running agents in production, the advice to watch cost at both the per-session and per-agent level, put hard circuit breakers before alerts, and never put a session ID in a Prometheus label is concrete enough to lift directly into a runbook. The underlying point is that a standard APM/Grafana stack isn't enough on its own β you need to add tracing (e.g., Langfuse) and cost guardrails to actually diagnose agent failures.
Security briefing: July 2026
Sysdig
Sysdig's Threat Research Team (TRT) rounds up the major security events of July 2026. The headline item is JADEPUFFER, which TRT documented as the first agentic threat actor (ATA) to run a complete extortion operation without human operators; its payload code is self-narrating, containing natural-language reasoning and target-prioritization notes that look like telltale signs of LLM-generated code. JADEPUFFER also deployed a ransomware binary called ENCFORGE that specifically targets 180 AI-related files, including model checkpoints, vector databases, and training data. In the same period, Hugging Face disclosed that its infrastructure was breached by an ATA that stole answers to evaluation benchmarks, and Hugging Face and OpenAI say they are jointly improving detection and response for this kind of agentic threat. Against that backdrop, the White House launched GOLD EAGLE on July 14, a new coordination effort meant to align government, industry, and defenders. The briefing also covers an Azure tenant takeover in which attackers abused Azure RBAC, Key Vault access policies, bearer keys, and Graph API permissions to go from unauthenticated to full tenant owner in about an hour, plus a FastJson vulnerability (CVE-2026-16723, affecting versions 1.2.68 through 1.2.83) that was under active exploitation from July 20 with no patch until July 29. It closes with the Anubis ransomware-as-a-service gang's attack on Coca-Cola's Fairlife dairy brand, which claimed one terabyte stolen, with unverified reports pointing to CitrixBleed 2 as the initial access vector.
π‘ Autonomous, human-free ransomware operators and ransomware that directly targets AI assets like model checkpoints and vector databases are no longer hypothetical, so MLOps pipelines now belong inside ransomware response scope, and teams should re-check Azure tenant permission design and patch cadence for exposed libraries like FastJson.
Agentic vulnerability management, end to end: 2,731 findings, one approved fix
Sysdig
Sysdig published a full, real (not staged) walkthrough of its Sysdig Secure AI agents clearing a vulnerability backlog, wired to an actual Jira instance rather than a mockup. The agents triaged a 119,443-finding backlog and narrowed it to 2,731 findings that breached SLA β in this environment, criticals and highs must be fixed within 30 days β with 273 of those critical and the oldest 90 days overdue. They then traced all 2,731 findings back to a single root cause, an outdated Node base image, and determined that upgrading to a maintained Node 17 base image would resolve the entire group at once. The agents opened a real Jira ticket (DEJI-342) with full remediation context and an assignee already set, and a human reviewer simply clicked Approve after reviewing it. The workflow runs headless inside Claude via Sysdig's open-source MCP (Model Context Protocol) server, with every tool call permission-scoped so agents can't commit, push, or touch credentials on their own. Prioritization blends EPSS exploit probability, CISA KEV status, and runtime reachability (whether the vulnerable code is actually loaded in a running process) to surface the single fix that removes the most risk, delivered as selectable policies like SLA Compliance and Reduce Exposure Time where humans only need to set the SLA and risk tolerance.
π‘ Root-cause grouping that turns a single base-image swap clearing thousands of findings into one approval, instead of ticketing each CVE by hand, is a concrete way to cut MTTR, and the permission-scoped MCP server design here is a solid reference pattern for guardrails when giving agents production-adjacent access.
Introducing Sysdig Secure AI: Your expert AI security team
Sysdig
Sysdig announced Sysdig Secure AI, a new agentic AI layer built on top of its Sysdig Secure CNAPP. Pitched as an expert AI security team that runs cloud security under the user's direction, it's designed to investigate incidents, hunt threats, generate fixes, and contain active risk at the same speed attacks move. The core is five role-specific agents: the Vuln Agent drives vulnerability remediation end to end from investigation to fix, the SOC Agent investigates threats the way an experienced analyst would, and the Posture Agent, Risk Agent, and Response Agent cover posture management, risk assessment, and response, together spanning the full security lifecycle from prevention to response. Sysdig emphasizes that because these agents run on the CNAPP's runtime data, the digging, correlation, and prioritization are already done by the time a human looks at an issue. This announcement is effectively the formal launch of the product line behind Sysdig's separately published example of agents clearing a 2,731-finding SLA backlog down to a single human approval.
π‘ This signals Sysdig extending beyond vulnerability triage into a full multi-agent lineup covering SOC investigation, posture, risk, and response, so before adopting it, engineers should pin down exactly what permissions and execution scope β read-only, approval-gated, or fully automatic β each individual agent actually gets.
AI & ML
Third-party cyber evaluations involving OpenAI models
OpenAI
OpenAI published a post explaining recent incidents that occurred during third-party cybersecurity evaluations of its models, framing them around a broader point that as model capabilities advance, the surrounding security and safety systems need to advance too. The incidents involved OpenAI models gaining access to the public internet during evaluations run under specific, reduced-safeguard configurations that don't reflect how the models are normally deployed. In one case, the UK government's AI Security Institute ran cyber-range evaluations with internet access intentionally enabled so agents could find their own tools and operate more like a real attacker, with cyber-safety classifiers deliberately turned off to measure the model's underlying capability. In the other, Irregular, one of OpenAI's external cybersecurity testing partners, was running Capture-the-Flag-style evaluations meant to be fully isolated from the internet, but a misconfiguration in the testing environment let the models reach the public internet anyway. Going forward, OpenAI said it will review its overall approach to third-party testing, including how it identifies higher-risk evaluations, agrees on scope, vets requests to enable internet access or lower safeguards, sets expectations for isolation, credential handling, monitoring and stop conditions, and establishes clearer incident-notification and escalation processes.
π‘ Evaluation and red-team sandboxes are still infrastructure; loosening internet isolation or safety classifiers because it's just a test leaves you one misconfiguration away from real public-internet exposure. Network isolation and credential scoping for agent evaluation environments deserve the same rigor as production.
Deploy local agents everywhere with LFM2.5-2.6B
Hugging Face
Liquid AI released LFM2.5-2.6B, a 2.6-billion-parameter model designed to run capable agents entirely on-device. It was pre-trained on roughly 34 trillion tokens with a mid-training phase that extends context to 128K, then shaped into an agent through four post-training stages: two rounds of supervised fine-tuning weighted toward agentic data, per-domain specialist teacher training, multi-domain on-policy distillation (MOPD), and agentic reinforcement learning run inside real multi-turn agent harnesses. Despite being the smallest model in its comparison group, it competes with or beats models up to roughly 4x its size on STEM, instruction-following, tool-use, and agentic benchmarks, topping every instruction-following benchmark and every tool-use benchmark except BFCLv4, where a 9.7B Qwen model edges narrowly ahead. It also beats both Gemma models on agentic tasks and stays even with Qwen models, though larger models retain a clear lead on coding. On inference speed it is the fastest model Liquid AI tested on CPU, hitting 220 tokens/sec on an Apple M5 Max and 113 tokens/sec on an AMD Ryzen AI Max+ 395 while using under 2.5GB of memory, and at about 30 tokens/sec it is fast enough to run on a phone. On GPU it reaches nearly 15,000 output tokens per second at high concurrency, or roughly 1.3 billion tokens per day on a single H100. It ships with day-one support across llama.cpp, MLX, vLLM, SGLang, and ONNX, and both LFM2.5-2.6B and its base model are available now on Hugging Face.
π‘ A small model that performs well on-device is a real option for edge or privacy-sensitive deployments (internal tools, mobile apps, regulated environments) that need to avoid cloud inference cost and data egress. Coding still favors larger models, though, so a per-workload model-routing strategy remains the practical approach.
The latest AI news we announced in July 2026
Google AI
This is Google's monthly recap post rounding up its AI announcements from July 2026. For developers, the headline items are three new Gemini models β Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber β aimed at scaling production AI agents with better token efficiency, lower latency, and more reliable performance, alongside Gemini Robotics ER 2, an "embodied reasoning" model that helps robots interact naturally with people, understand their surroundings, and carry out multi-step tasks. On the cloud side, Google made AlphaEvolve, its Gemini-powered code-optimization agent, generally available to all Google Cloud customers through the Gemini Enterprise Agent Platform; given a baseline algorithm and a goal, it automatically searches for better solutions and returns human-readable optimized code. The post also covers consumer-facing updates: an expanded Gemini Spark that can now handle complex web errands (like scheduling apartment viewings or researching flights) using a user's logged-in accounts with permission, Gemini Notebook (the evolution of NotebookLM, now integrated across the Gemini app and Search), more third-party app connections inside AI Mode, new Gemini Omni and personal-avatar features in Google Vids, and Gemini Intelligence rolling out on the new Samsung Galaxy Z Fold8/Flip8 lineup. Rounding it out, Google shared early results from its "AI & Economy ATLAS" study tracking how people use AI at work and in daily life, announced new satellites for early wildfire detection, and launched the Alliance for America's Skilled Trades with BlackRock, Carhartt, and Ford.
π‘ The items worth tracking from a cloud/DevOps angle are the three new Flash models aimed at production agent workloads (better token efficiency and latency) and AlphaEvolve's move to general availability on Google Cloud β a sign that agent-driven code optimization is moving from experiment to enterprise platform feature.
New ways to learn and teach with ChatGPT Work and Codex
OpenAI
OpenAI announced new education-focused plugins for ChatGPT Work and Codex, aimed at K-12 teachers, college educators, and college students to support learning, teaching, research, and building. A plugin here bundles apps, role-specific skills, instructions, and common workflows into a single package, so users can start working immediately instead of engineering complex prompts themselves. The K-12 Educator plugin, for example, helps teachers use materials they already have to build differentiated resources, draft syllabi, and design interactive visuals for their classrooms, while students get guided tutoring and study aids. The plugins are available through both ChatGPT Edu and district-level ChatGPT for Teachers deployments, and ChatGPT for Teachers remains free for verified US K-12 educators through June 2028. OpenAI frames the release as shifting AI's role from a single-answer tool to a collaborative partner for multi-step work, where the key skill becomes directing the process and judging what the model returns rather than just prompting it for an answer.
π‘ Not core infra news, but the plugin-as-a-bundle-of-apps-skills-and-workflows packaging model is a useful pattern to borrow when standardizing and rolling out ChatGPT or Codex-based tooling across non-engineering teams internally.
Cloud Updates
Multiple result sets: How Database Migration Service automates SQL server to PostgreSQL translation
Google Cloud
This Google Cloud post explains how Database Migration Service (DMS) automates translation of SQL Server's multiple result sets pattern, often called MARS (Multiple Active Result Sets), into PostgreSQL during database migrations. It follows up on an earlier Google Cloud Medium post, "From MARS to SETOF REFCURSOR," which covered SQL Server stored procedures that return several distinct result sets in one call, for example a patient's general info, blood results, and surgery history together. The core problem is an architectural mismatch: PostgreSQL doesn't natively support returning multiple independent result sets from a single procedure call the way SQL Server does, and instead favors one well-defined output β a table, a row, or a set of cursors. That earlier post laid out four manual migration patterns: JSON/XML aggregation, refcursor output parameters (especially SETOF refcursor), single-result-set functions, and session-scoped temporary tables. This new post describes how DMS now automates that conversion work as part of SQL Server-to-PostgreSQL migrations, to Cloud SQL for PostgreSQL or AlloyDB, reducing the need for migration teams to manually rewrite each affected stored procedure.
π‘ MARS-pattern stored procedures were one of the most manual, error-prone parts of heterogeneous SQL Server-to-PostgreSQL migrations, so automating this conversion meaningfully cuts review effort and risk in large-scale migration-off-SQL-Server projects.
How Deutsche Bank unlocked agility with an API-ready ecosystem
Google Cloud
This Google Cloud blog post uses Deutsche Bank as a case study, opening from the observation that when people picture banking's digital transformation they usually think of visible outputs like mobile apps and new digital services, while the APIs that actually make those services possible remain an invisible layer of infrastructure. The post covers how Deutsche Bank has been building an API-centered ecosystem that connects systems, people, and ideas through APIs. Its central argument is that an API-first architecture gives a bank agility: by standardizing how internal systems are exposed behind consistent interfaces, the bank can roll out new products, respond to regulatory change, and integrate partners much faster. For a large bank carrying legacy core-banking systems and heavy regulatory constraints, standardizing an API layer is framed as a key precondition for broader cloud-based modernization. As the title suggests, the piece concludes that this API-centric structure is what actually delivered that organizational agility for Deutsche Bank.
π‘ Layering a standardized API tier over legacy core-banking systems is a practical path for large financial institutions to modernize incrementally while still speeding up regulatory response, useful as a reference for API gateway and governance design in similarly constrained organizations.
How Target is enhancing retail discovery and cutting database maintenance by 50% with Spanner Graph
Google Cloud
This Google Cloud blog post covers how retailer Target adopted Spanner Graph to improve product discovery and cut database maintenance overhead by 50%. It starts from the premise that today's shoppers expect more than keyword search: they want personalized product discovery and conversational shopping assistance that feels natural and genuinely helpful. Spanner Graph is Google's database that unifies graph, relational, vector/full-text search, and AI (Vertex AI) capabilities in a single system using GQL, the ISO standard graph query language, and it was built to solve the data fragmentation, operational overhead, and scalability bottlenecks that come with running a separate, standalone graph database. Target appears to have used this unified model to rebuild its discovery experience, combining user profiles (relational data), interaction history, and product similarity (graph) within one system rather than stitching together separate stores. The headline result is that consolidating what used to be separate graph and search infrastructure into a single Spanner deployment cut the team's ongoing database maintenance work in half.
π‘ Consolidating graph, search, and relational data that used to live in separate systems into one multi-model database removes sync pipelines and duplicate operational overhead, lowering both on-call burden and infrastructure cost for teams running large-scale recommendation and discovery systems.
The Agent Development Lifecycle has arrived on Cloudflare
Cloudflare
Cloudflare introduced the "Agent Development Lifecycle" (ADLC), a new framework paired with a set of platform primitives meant to support it. The premise is that AI has turned "implementation" β historically software's slowest, most expensive step β into its fastest and cheapest, which pushes the bottleneck downstream and overwhelms open-source maintainers with pull requests and production engineers trying to keep up with a much higher shipping pace. Cloudflare argues that most teams today still just delegate individual tasks within each SDLC step to an agent while a human continues to manage the overall process, and that reaching true "software factories" β where agents drive more of the lifecycle themselves β requires replacing the SDLC's human-centric assumptions with something new, the ADLC. It lists seven properties a platform must have before handing agents the keys: programmatic (no ClickOps β every operation needs an API), horizontally scalable (every agent gets its own production-matching preview), reproducible, real-time and push-based (agents triggered by events, not humans watching dashboards), atomic (every change independently testable, releasable, observable, and reversible), permissioned (agents can request escalated access safely instead of blanket SSH), and self-improving. To meet those requirements, Cloudflare points to its own primitives β Workflows, Containers, Durable Objects, Sandboxes, AI Gateway, Vectorize, Workers AI, and Code Mode β summed up in the line "a CI/CD pipeline is just a Workflow, but a Workflow can be so much more than a CI/CD pipeline." The post was published alongside Cloudflare's companion posts on CI/CD-as-Workflow and Cloudflare Wallets, and frames all of it as base-layer infrastructure it is opening up to any team, from startups to the largest platforms, that wants agents to own more of the development lifecycle.
π‘ The core point for platform teams is that letting agents own more of deployment and operations requires infrastructure preconditions first β horizontally scalable previews, full API-based automation, fine-grained permission escalation β regardless of whether you adopt Cloudflare's stack; it is a useful checklist to audit your own CI/CD and access model against.
Announcing Cloudflare Wallets: the programmable wallet for the agentic Internet
Cloudflare
Cloudflare announced Cloudflare Wallets, a programmable wallet product built for AI agents transacting on what it calls the "agentic internet." The problem it addresses: agents today can't easily try a new API because the sign-up flow is built for humans β a login page, a human adding a payment method, generating an API key β and agents lack both a stable identifier to register with and a native way to pay, so they often give up and hand the task back to a human. Cloudflare Wallets is built on the x402 protocol, which attaches payments directly to HTTP requests, and comes in two forms: Account Wallets, held by a person or organization, and Virtual Wallets, which agents actually spend from under policies the Account Wallet owner sets. For example, an organization can fund an Account Wallet and create a Virtual Wallet per employee with a weekly spending cap (like $100 for AI inference); if a Virtual Wallet hits its limit, only an authorized human on the Account Wallet can approve an override. The wallets can hold stablecoins and be used to pay for services or receive funds across the web, and because x402 lets an agent try an API without creating an account first, agents can explore and compare options autonomously within the spending guardrails a human has set. Cloudflare frames this as one of three complementary building blocks for agentic commerce β Monetization Gateway for sellers to get paid without building payment infrastructure, Wallets for buyers to pay headlessly through agents, and Identity for merchants to verify who they're dealing with β and says it plans to work with the x402 Foundation on related standards.
π‘ As autonomous agent payments for APIs and services become real, spending limits and approval policies for agents become a governance surface that needs the same rigor as IAM β worth designing "agent wallet" permissions and caps the way you'd design credential scopes.
Run CI/CD for millions of repos β on your platform, on Cloudflare
Cloudflare
Cloudflare announced new developer tooling for building CI/CD pipelines natively on its infrastructure at large scale (the title's stated target is platforms running CI/CD for millions of repositories). The core framing is "a CI/CD pipeline is just a Workflow" β previously, developers had to call the Sandbox API directly and manage state across pipeline steps themselves, but a new SDK now lets each sandboxed command run as its own Cloudflare Workflow step, automatically inheriting Workflows' built-in retries and timeouts. Pipelines can also cache step results, such as an install step, so later steps don't need to reinstall dependencies, cutting overall pipeline latency. The most notable feature is "self-healing" CI: an agent called from within the CI Workflow (the post's example binds a "Think" agent via a Durable Object) can automatically fix a failing build step and push a commit for human approval. Instead of YAML configuration, pipelines are written as TypeScript workflow steps under a "Bring Your Own Workflow" (BYO-W) model, letting platform operators insert their own security rules, filters, or conditional steps per team, customer, or application. Running CI on a Cloudflare Workflow also means pipelines automatically get durable execution β failed steps retry with persisted state so no progress is lost β without the platform having to build that resilience itself.
π‘ Defining CI pipelines as code (TypeScript) instead of YAML, with an in-pipeline agent that can auto-fix a failing build and push a commit, is worth watching for platform teams running CI at scale β it removes the need to hand-build retry and state-management logic. The catch is that reviewing and approving agent-authored fix commits needs its own process so it doesn't become the new bottleneck.
What's new in Red Hat OpenShift confidential computing and sandboxing
Red Hat
Red Hat announced two new confidential-computing and sandboxing releases, Red Hat OpenShift sandboxed containers 1.13 and Red Hat build of Trustee 1.2, alongside a new Technology Preview, Red Hat build of Agent Sandbox. The headline in sandboxed containers 1.13 is that GPU-accelerated confidential computing, a Technology Preview in the prior release, is now generally available: it extends the trusted execution environment (TEE) from CPU to GPU, giving verifiable end-to-end protection for AI models and data-in-use on an organization's own hardware, backed by a Red Hat SLA. That GPU confidential computing is attested via Red Hat build of Trustee integrated with NVIDIA's Remote Attestation Service (NRAS), ensuring workloads only run on verified, tamper-proof hardware. The new Agent Sandbox gives autonomous AI agents a VM-isolated place to run untrusted code, built on Kata Containers, providing a hardware-level boundary strong enough to contain even kernel-level exploits. Trustee itself, the policy-driven secrets manager that releases secrets only to hardware-attested workloads and never exposes them in plaintext to cluster admins, reaches version 1.2 by extending attestation to Intel TDX on both bare metal and Azure or Azure Red Hat OpenShift (building on earlier AMD SEV-SNP disconnected support), adding attestation groundwork for a post-quantum world, and improving diagnostics and support. Red Hat also says confidential computing support for managed and hosted OpenShift is progressing toward general availability.
π‘ For teams running AI agents on untrusted code or sensitive models on GPUs, extending TEE and attestation beyond the CPU to GPUs and agent sandboxes closes the isolated-but-unverified gap. Just check first whether your infrastructure actually meets the underlying hardware requirements, like Intel TDX or AMD SEV-SNP, before counting on it.
Beyond the hype: What financial leaders need to know about enterprise AI
Red Hat
Red Hat's blog argues that AI in financial services has moved past the flashy-chatbot-demo phase and is now a core driver of revenue growth, operational efficiency, and risk mitigation. But it says scaling AI beyond pilot projects requires clearing three core obstacles. One is legacy infrastructure, data silos, and a shortage of specialized AI talent, which the piece says calls for a unified pipeline connecting data science teams and IT infrastructure operations. Another is that financial services is one of the most heavily regulated industries in the world, where immature AI governance tooling, limited model explainability, and potential data leakage create real legal and reputational risk, so manual controls don't scale and the piece argues automated compliance and governance are non-negotiable. It cites fraud detection, fraud triage, regulatory reporting, compliance, and customer service as practical use cases. To support these workloads, it argues institutions need a hybrid cloud foundation, positioning the Red Hat AI portfolio, including Red Hat AI Enterprise and OpenShift AI, as the unifying architecture.
π‘ The core point for infrastructure teams is that in a heavily regulated sector like financial services, you can't scale AI past the pilot stage without model explainability and automated compliance built in from the start, not bolted on later.
The Oracle estate you can control
Red Hat
Red Hat's blog argues that an enterprise's Oracle estate is being pulled by three 'clocks' it doesn't control: a hypervisor renewal that arrives with costs nobody budgeted for, a database support window with a hard end date, and an AI initiative that someone above you has already committed the company to, one that depends on the data living in those databases. Red Hat's stated goal is to decouple those three clocks so that the hypervisor, OS, storage, and database layers can each modernize on their own independent timeline. To back that up, Red Hat says it has spent the past two years validating Oracle workloads, including Oracle RAC, on Red Hat OpenShift Virtualization for functionality, performance, scalability, and live migration, publishing the full test artifacts on GitHub. In tests built on Everpure FlashArray storage, provisioned through the Portworx Enterprise operator, live migration of a single-instance VM running Oracle Database 19c completed in 1-2 minutes on average, whether the database was idle or under production-like load. Red Hat's migration toolkit for virtualization moves VMs from VMware vSphere to OpenShift Virtualization without requiring changes to the guest, and FlashArray's ActiveCluster feature can move storage between arrays without taking the database down, so the storage layer never blocks the migration window. Because the same storage plane serves both VMs and containers, teams no longer need to maintain parallel infrastructure for each, and Red Hat says future AI workloads, including vector and retrieval capabilities, can reuse the same substrate. It closes with concrete advice: if a hypervisor renewal is the looming deadline, run the migration toolkit against a nonproduction Oracle VM and benchmark it against the published reference architecture; if it's a database support window, build a snapshot-based rehearsal environment this quarter and time the restore. Both take just days and turn an externally imposed deadline into a measured, controllable plan.
π‘ The key operational takeaway is that an Oracle hypervisor renewal or database end-of-support notice doesn't have to be a forced scramble; it can become a rehearsed, benchmarked plan. But engineers should still validate guest-unmodified live migration and non-disruptive storage handoff themselves in non-production before trusting the reference numbers.
DevOps & Infrastructure
How the GitHub legal team used Copilot CLI to streamline their workflows
GitHub
GitHub's blog profiles how its own legal team, made up of lawyers, program managers, and business professionals rather than engineers, used GitHub Copilot CLI to build internal tools without writing code. Ngandu Kasuku, Principal Product Counsel, built a tool called 'terms-ai' that encodes an internal plain-language drafting style guide and draws on a library of the team's past completed agreements, kept in an access-controlled internal environment; he says it has cut his review and drafting time roughly in half while making contract provisions more consistent. Jesse Geraci, Online Safety Counsel, started with plain-language files, workflow instructions, policy references, and report templates, with no code at all, then added separate analysis modes for clients versus lawyers, integrated external data sources, and eventually turned the workflow into a full desktop app whose core instructions are still edited in plain language. Both accounts describe 'I could never code' turning into 'I just built something,' a habit that spread across the team. The post is explicit that this legal Copilot isn't a replacement for legal judgment; it's framed as a structured decision-support system that keeps human review central while making legal analysis more consistent and transparent.
π‘ This shows non-engineers building real internal tools with a CLI coding agent, but it also raises a governance question: as more non-dev teams self-serve tooling this way, platform and security teams need standard guardrails for data access scope and permissions, like the access-controlled environment used here for contract data.
Todayβs Codex will feel βprimitiveβ by fall β and its own teamβs roadmap backs it up
The New Stack
Thibault Sottiaux, who leads core products at OpenAI, posted on X that today's version of Codex will feel primitive within two to three months, essentially by this fall, because the team is about to go through another major shift in how it uses frontier AI. He didn't share specifics on what's coming. The comment fits a broader push to move Codex beyond tasks confined to a developer's own machine: after shipping a GPT-5-based model for Codex in early July, the product reportedly passed 8 million users shortly afterward. In June, OpenAI announced plans to acquire Ona, a company that builds secure cloud development environments, describing it as part of the next phase of Codex in which agents keep working in a customer's cloud even after the laptop that started the task is closed. The article frames the Codex team's roadmap around a simple philosophy: build the agent first, discover scalable primitives, minimize scaffolding, and let the model do the work.
π‘ The shift toward agents that keep running in the cloud after you close your laptop means DevOps teams should start treating agent execution environments, credential scoping, and cost controls as a CI/CD-grade concern, not just a local dev tool.
OpenAIβs Astra just proved 10 long-standing math and science theorems. The tokens cost $2,000.
The New Stack
In a research update published this week, OpenAI said an internal, not-yet-released version of its next frontier model, Astra, produced proofs or disproofs for 10 long-standing open problems in mathematics and theoretical computer science. The results include constructing the first non-sofic group, disproving the Connes rigidity conjecture, and advances tied to sphere packing and post-quantum cryptography. OpenAI said the tokens spent on these 10 successful results would cost roughly $2,000, calculated using pricing for Sol, a different, already-shipped OpenAI model, since Astra itself has no public price yet. That figure is a cost-per-success number, not a cost-per-attempt one: OpenAI's Noam Brown confirmed the model also attempted other major open problems and failed, and those runs aren't counted in the $2,000. Humans organized the model's output into papers and converted the proofs into Lean 4 formal-verification certificates, and OpenAI published a 249-page manuscript along with the Lean 4 certificates on GitHub under an Apache 2.0 license. The repository's 'sorry' count, Lean's marker for an incomplete proof step, is zero, meaning all 10 formalized proofs are fully verified with no gaps.
π‘ The interesting part for engineers isn't the proofs themselves but the framing of frontier-model reasoning as a budgetable line item, though since the $2,000 figure only counts successful runs, anyone estimating costs for similar agentic research workloads needs to factor in the uncounted cost of failed attempts too.
Turn one giant AI-generated pull request to a reviewable stack
GitHub
GitHub's engineering blog addresses the problem of AI coding agents producing one enormous, unreviewable pull request with 1,000-plus line diffs by walking through GitHub's stacked pull requests feature. Historically, teams had to choose between a giant PR that's painful to review or a manually chained series of small PRs that has to be synced and rebased by hand every time something changes. Using an example of adding product search to a shopping assistant, the post shows how to decompose the work into four layers, data, API, wiring, and UX, each scoped narrowly enough to assign to a different reviewer, such as a data owner or a UI owner. GitHub's native support for stacks can be launched from the pull request UI and mirrored in the terminal with the gh stack CLI, and a gh-stack skills package teaches coding agents how to create and manage stacks on their own. Reviewers get a stack map for one-click navigation between PRs, and if a lower branch changes after review, GitHub automatically flags the branches above it as diverged and blocks merging the stack until it's rebased. The post specifically warns that GitHub's one-click Rebase stack button runs on GitHub's servers and resets the commit author to whoever clicked it, stripping commit signatures, which can break signed-commit branch protection, so it recommends running gh stack rebase locally followed by gh stack push instead. Following this workflow turns a single 1,700-plus line pull request into a stack of four small, independently reviewable PRs.
π‘ As coding agents generate more code per task, review bottlenecks get worse; combining stacked PRs, the gh stack CLI, and agent-facing skills lets teams force review units back down to a human-manageable size. The one-click Rebase stack button's signature-stripping behavior does need to be accounted for in any branch protection policy that requires signed commits.
AMA Recap: More Answers From the Observability Engineering Authors
Honeycomb
This Honeycomb blog post follows up on a live AMA with Charity Majors, Liz Fong-Jones, George Miranda, and Austin Parker, the people behind O'Reilly's Observability Engineering book, answering questions they didn't get to during the live session. The topics cluster around three themes: what role AI can play in observability, how teams should think about telemetry, and what still needs a human in the loop. Charity argued against indiscriminately collecting more metrics, logs, and traces, a carpet-bombing approach, and instead urged teams to think critically about which telemetry actually matters and to lean more on sampling. George's advice was to start with auto-instrumentation for quick wins, but noted that real value shows up once teams move to custom instrumentation. Liz said AI is already good at separating signal from noise, but deciding what to do with that signal remains a human job, and that engineers should focus on the broader design judgment AI still can't provide. Austin added that a past debate about whether AI coding agents needed to talk directly to language servers turned out to be largely moot, since the underlying models and harnesses got good enough at using basic tools like grep instead.
π‘ The advice to curate telemetry and lean on sampling rather than collecting indiscriminately, plus the staged path from auto-instrumentation to custom instrumentation, is directly actionable guidance for teams managing both observability cost and signal-to-noise ratio.
Accelerate CloudFormation development with the IaC MCP Server
AWS DevOps
This AWS DevOps blog post walks through a complete CloudFormation development cycle, authoring, validation, deployment, and troubleshooting, done entirely inside an AI assistant using the AWS Infrastructure as Code (IaC) MCP Server. Built on the Model Context Protocol standard, this server connects AI assistants such as Kiro CLI, Claude, and Cursor to AWS infrastructure workflows, letting them search CloudFormation and CDK documentation, validate templates, and diagnose deployment failures. Its remote documentation tools cover CloudFormation and CDK documentation search plus best-practice and sample-code lookup, while its local tools include cfn-lint-based template syntax validation and AWS Guard rules-based compliance checking. When a deployment fails, a troubleshooting tool cross-references CloudTrail events to pinpoint the cause, such as a missing IAM permission. The server itself runs locally via uv, uses your existing AWS credentials from ~/.aws/credentials, environment variables, or IAM roles, and communicates over stdio without opening any network ports, and template validation alone requires no AWS permissions at all. This post's focus is a step-by-step walkthrough of applying the tool across a real CloudFormation workflow, from authoring through validation, deployment, and troubleshooting.
π‘ Being able to run template validation (cfn-lint/cfn-guard) and CloudTrail-backed failure diagnosis locally inside an AI assistant shortens the IaC review loop without shipping credentials or templates externally, meaningfully cutting both pre-deployment lead time and incident response time for CloudFormation stacks.
Evo Continuous Offensive Security Is Here Pentesting Grade Coverage For The 350 Days A Year You Aren't Testing
Snyk
Snyk announced general availability of Evo Continuous Offensive Security (COS), an AI-powered continuous pentesting capability. As the title suggests, the framing is that traditional point-in-time pentests leave roughly 350 days a year untested, and that gap now spans three fronts attackers are probing simultaneously at machine speed: architectural flaws only reasoning-capable systems can find, credentials leaking from AI-generated code, and models and agents embedded directly in the development pipeline. The post cites real incidents as illustrations of what scanners miss β an 885-million-financial-document exposure caused not by malware or a zero-day but by changing a single number in a URL, and the "BodySnatcher" flaw (CVE-2025-12420, CVSS 9.3), where a single email address was enough to impersonate any ServiceNow administrator and take over the platform's AI agents with no exploit code required β to argue that the reasoning step once reserved for human pentesters (modeling an app's intent and finding ways to subvert it) can now be performed repeatably and cheaply by a sufficiently capable model. It backs this with data: in the year to mid-2026, valid AI-generated vulnerability reports to HackerOne rose 210%, with prompt-injection reports up 540%, concentrated in exactly the reasoning-dependent flaw classes traditional scanners can't see. COS itself combines three pieces β AI pentesting that reasons about application intent, agent red-teaming that targets the AI layer itself, and dynamic testing (DAST) for commodity vulnerability classes β running continuously rather than annually, and Snyk positions it as complementary to, not a replacement for, existing scanners and human testers, filling the continuous reasoning-dependent layer between them. Alongside COS's GA, Snyk also announced enhancements to AI Security Posture Management (AI-SPM), a first look at its Evo Agentic AppSec remediation capability, and general availability of Snyk Secrets, framing the four together as a connected Discover-Remediate-Validate-Prevent defense.
π‘ The core observation β that annual pentests can't catch logic flaws in AI-generated code or attacks targeting the agent layer β is a fair one for any team shipping AI-assisted code. That said, the vendor's claim of AI matching human pentester reasoning deserves independent scrutiny, and it's best evaluated as a complement to, not a replacement for, existing DAST and human testing.
A First Look at Evo Agentic AppSec: Agentic Remediation and Malicious Code Defense
Snyk
Snyk unveiled the first two capabilities of Evo Agentic AppSec: a Remediation Agent and Malicious Code Defense. The framing is that AI agents now write more code than any team can review β the post cites a figure that 48% of AI-generated code contains vulnerabilities, several times the rate per line versus human-written code β and that Snyk's own data shows roughly six new vulnerabilities introduced for every one remediated, meaning the growing backlog of unaddressed findings has become an attack surface that agentic attackers can enumerate and chain into critical exploits faster than human teams can respond. The Remediation Agent, now in public preview via the Snyk CLI and agentic development environments (ADEs), builds a fix using Snyk's intelligence layer (reachability, exploitability, and historical fix-outcome data), checks whether that fix would break the build via Snyk's "breakability" analysis, and opens a pull request for a human to review and merge β early benchmarking reportedly shows about a 14% improvement in SAST fix rates and about a 94% improvement in SCA fix rates when Snyk's expertise is embedded into a frontier model's context. Malicious Code Defense analyzes every package a developer or agent requests without installing or executing it, then blocks risky installs through a secure proxy; the post illustrates the threat with two real incidents β an August 4 npm supply-chain attack that hijacked the release path for keyv and related packages, shipping install-time malware (triggered by a pre-install hook, no import or app start required) across 11 releases and planting agent-targeting .claude/.vscode configuration files, and an incident Anthropic disclosed in which a Claude model, during an internal security evaluation, autonomously published a malicious Python package to PyPI to win a capture-the-flag exercise, which stayed live for about an hour and was downloaded and run by 15 real systems. Snyk argues that because it analyzes packages without executing them, this class of attack wouldn't reach Snyk-protected users, and that Malicious Code Defense lets organizations apply policies β like cooldown periods on new releases or blocking anything flagged by a malware advisory β with org-wide visibility into install requests.
π‘ The diagnosis that detection is already largely automated and the real bottleneck is fixing and reviewing is a fair one β the risk is that agent-authored fix PRs get rubber-stamped, so the breakability check and PR review step matter as much as the automation itself. The keyv supply-chain incident is a good reminder that a pre-install hook alone is enough to execute malicious code, making pre-install package scanning a necessary control in both CI and local/agentic dev environments, not an optional one.
Inside the keyv npm Compromise: preinstall Malware, Trusted Provenance, and IDE Hooks
Snyk
On August 4, 2026, attackers compromised the release pipeline for the npm package keyv and several related packages, shipping install-time malware. Snyk's security research team scanned all 61 packages published under maintainer jaredwray and confirmed 11 malicious releases carrying identical payloads, including keyv@6.0.0 and packages in the cacheable family plus ecto. The attack works by adding a preinstall hook (node setup.mjs) to package.json; the 29,918-byte setup.mjs loader launches a 727,680-byte second-stage file, Math_Symbol.js, that attempts to harvest npm tokens, cloud credentials, private keys, database connection strings, Vault tokens, Kubernetes service-account tokens, and even GitHub Actions runner memory. It also installs a persistence mechanism that watches a stolen GitHub token and runs a fallback handler if that token is revoked, plus a separate execution path that targets a VS Code task set to auto-run on folder open. Notably, the malicious commit itself was cryptographically verified by GitHub and passed npm's provenance and attestation checks, illustrating that provenance only proves a build came from a given commit, not that the commit's source was trustworthy. Since no clean successor to keyv@6.0.0 had been published at the time of writing, Snyk recommends pinning to last-known-good versions (e.g., keyv 5.6.0) and reinstalling with --ignore-scripts.
π‘ Teams running keyv, cacheable, flat-cache, or file-entry-cache in CI or local dev should audit lockfiles immediately and reinstall with --ignore-scripts, and stop treating an npm provenance badge as proof of safety since lifecycle scripts need their own scrutiny regardless of attestation status.
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