# devsecops Published articles for devsecops. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Transform and route security logs to Microsoft Sentinel tables using Observability Pipelines DevFeed: [Transform and route security logs to Microsoft Sentinel tables using Observability Pipelines]() Original publisher: [Read original article]() Author: Zara Boddula; Danielle Park Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability pipelines](), [SIEM, Security](), [Security](), [log management](), [Microsoft]() Tags: [azure](), [cisco-meraki](), [devsecops](), [fortigate](), [log-management](), [logs](), [observability-pipelines](), [pipelines](), [security](), [siem](), [telemetry](), [threat-detection]() ### AI overview Datadog's Observability Pipelines Packs transform firewall, VPN, and network detection logs into Microsoft Sentinel table schemas before ingestion. The post describes Packs for Palo Alto Networks, Fortinet, Cisco ASA, Cisco Meraki, and ExtraHop, including filtering and noise reduction to help control Sentinel ingest volume while retaining visibility. ### Source excerpt Learn how Observability Pipelines Packs map security logs to Microsoft Sentinel schemas and help control downstream ingest volume. ## How to Catch Security Vulnerabilities in Code Before They Reach Your Pull Requests DevFeed: [How to Catch Security Vulnerabilities in Code Before They Reach Your Pull Requests]() Original publisher: [Read original article]() Author: Umair Mirza Published: 2026-09-14T22:23:01Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Application Security](), [Security](), [Vulnerabilities](), [Secret Scanning](), [Git](), [pull-requests](), [ci](), [Command-line interface](), [.NET](), [Python](), [coding]() Tags: [application-security](), [ci](), [cli](), [code](), [devsecops](), [git](), [net](), [pull-requests](), [python](), [sast](), [secret-scanning](), [security](), [security-vulnerabilities](), [software-development](), [testing](), [vulnerabilities]() ### AI overview This tutorial explains how to shift security checks earlier in development by running SAST locally with Git pre-commit hooks. It demonstrates using the DevSkim CLI to detect insecure coding patterns, pairing it with Gitleaks for dedicated secret scanning, validating the setup with an intentional failure, and enforcing the checks in CI. ### Source excerpt Security reviews are most effective when developers receive feedback while the code is still fresh in their minds. Waiting until a pull request, CI build, or penetration test to find exposed credentia ## LTM Builds a Lightwell Remediation Services Practice Around IBM and Red Hat's $5B Open-Source Program DevFeed: [LTM Builds a Lightwell Remediation Services Practice Around IBM and Red Hat's $5B Open-Source Program]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-11T16:35:51Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [ibm](), [open-source-security](), [supply-chain-security](), [Security](), [vulnerability](), [DevSecOps](), [Deployment](), [Open Source](), [Artificial Intelligence]() Tags: [ai](), [deployment](), [devsecops](), [enterprise](), [ibm](), [open-source](), [open-source-software](), [red-hat](), [security](), [software-supply-chain](), [testing](), [vulnerability]() ### AI overview LTM is developing a remediation services practice around IBM and Red Hat's Lightwell program, which provides AI-generated, vendor-validated fixes for open-source software vulnerabilities. The offering is intended to help customers plan, prioritize, test, validate, and deploy patches at scale. ### Source excerpt LTM, the Larsen & Toubro Group services company that was LTIMindtree until its February rebrand, is building a Lightwell remediation services practice around the $5 billion IBM and Red Hat program for securing open-source software with AI-generated, vendor-validated fixes. IBM's clearinghouse produces validated, production-ready patches for open-source dependencies; LTM's job is getting them into customer The post LTM Builds a Lightwell Remediation Services Practice Around IBM and Red Hat's $5B Open-Source Program appeared first on StorageReview.com. ## How to calculate DevOps platform total cost of ownership DevFeed: [How to calculate DevOps platform total cost of ownership]() Original publisher: [Read original article]() Author: GitLab Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [CI/CD]() Tags: [ai](), [ci-cd](), [complexity](), [compute](), [cost](), [devops](), [devsecops](), [devsecops-platform](), [drivers](), [how-to](), [infrastructure](), [integration](), [model](), [platform](), [reduce](), [software-delivery](), [tools]() ### AI overview A guide to modeling the total cost of ownership of a DevOps platform, including subscriptions, CI/CD compute, AI usage, infrastructure, tools, and internal labor. ### Source excerpt There's nothing like budget pressure to put your DevOps platform under a microscope. But subscription fees and license costs only tell one part of the story. The total cost of ownership (TCO) for a DevOps platform also includes variable costs like CI/CD compute and AI usage, along with the infrastructure, tools, and employee time required to keep software delivery moving. That wider view matters when you're tasked with defending platform spend or comparing options with a head of finance. Designing a useful TCO model can: Make those costs transparent for stakeholders Shine a light on the reasoning (or lack thereof) behind each cost Identify areas to reduce spend without negatively impacting software delivery What total cost of ownership really includes The core challenge of calculating TCO is that DevOps platforms package and price capabilities differently. For example, one platform may bundle CI/CD or AI capabilities into a per-seat subscription, while another could price usage separately. A third may appear less expensive upfront but require additional tools and ongoing integration work. That's why list prices or pricing tiers alone won't give you a useful comparison. Start with the capabilities and workloads your organization actually needs, then calculate what it takes to support them on each platform. Use the same scope and time period for every option -- often one year -- and define which teams, applications, environments, and delivery stages are included. Separate recurring costs from one-time expenses and external spend from internal labor, so finance can audit the assumptions and forecast future years. A useful TCO model, therefore, answers two questions: What does it cost to meet our requirements today? Which variables will cause that cost to rise or fall as our usage changes? The cost categories that drive your bill Most DevOps platform costs fit into the following categories: Cost categoryWhat it includesMain cost driverPlatform accessPaid seats, role-based ## Is prevention essentially a solved problem? DevFeed: [Is prevention essentially a solved problem?]() Original publisher: [Read original article]() Author: Ezra Tanzer Published: 2026-09-10T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI Development](), [Vulnerabilities](), [Architecture & Design](), [Code]() Tags: [agent](), [agentic](), [ai](), [application-security](), [awareness](), [blog](), [code](), [code-security](), [cost](), [developer](), [devops](), [devsecops](), [secrets](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [vulnerabilities](), [vulnerability-insights]() ### AI overview The article argues that preventing vulnerabilities in agent-generated code is architecturally understood, but organizations must apply the appropriate controls throughout a changing development lifecycle. It says prompts can influence code but cannot reliably constrain agents, and that earlier security intervention reduces model-token cost, human attention, and the risk of an incorrect fix. ### Source excerpt Prevention in agent-generated code is architecturally solved--but choosing controls that protect security without slowing development remains the challenge. ## Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration DevFeed: [Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration]() Original publisher: [Read original article]() Author: Eric Metaj; Mark Azer Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Developer Tools](), [iac-security](), [Vulnerabilities]() Tags: [apm](), [bitbucket](), [ci-visibility](), [cloud](), [code](), [code-profiling](), [code-security](), [devsecops](), [error-tracking](), [infrastructure-as-code](), [integration](), [observability](), [open-source](), [pull-requests](), [secret-scanning](), [security](), [software-delivery](), [test-optimization](), [vulnerabilities]() ### AI overview Datadog's Bitbucket Cloud Source Code integration links selected repositories with Datadog so teams can investigate production issues with source context and review security findings before merging code. ### Source excerpt Connect Bitbucket Cloud to Datadog to troubleshoot with source code in context and surface test, quality, and security feedback in pull requests. ## Respond to security threats faster with Tines and Observability Pipelines DevFeed: [Respond to security threats faster with Tines and Observability Pipelines]() Original publisher: [Read original article]() Author: Zara Boddula Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Security](), [observability](), [Automation](), [log management](), [Orchestration](), [API]() Tags: [accelerate](), [api](), [automation](), [devsecops](), [logs](), [observability-pipelines](), [orchestration](), [real-time](), [reduce](), [security](), [workflows]() ### AI overview Tines and Datadog Observability Pipelines automate security-log processing by standardizing and routing logs, updating pipelines through APIs and reference tables, and applying current context in real time. The integration helps reduce alert noise, identify access-control gaps and suspicious activity, and accelerate threat investigations. ### Source excerpt Learn how Tines workflows can update Datadog Observability Pipelines to prioritize threats, reduce alert noise, and accelerate investigations. ## GitLab compliance frameworks: Adhere to SOC 2 in minutes DevFeed: [GitLab compliance frameworks: Adhere to SOC 2 in minutes]() Original publisher: [Read original article]() Author: Fernando Diaz Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [GitLab](), [Template](), [configuration](), [Security](), [Software]() Tags: [application-security](), [article](), [compliance](), [devsecops](), [framework](), [how-to](), [platform](), [security](), [soc](), [software](), [software-delivery](), [tutorial]() ### AI overview This article explains how GitLab custom compliance framework templates help teams define controls, continuously monitor adherence, and prepare for audits. It uses SOC 2 as a central example and covers templates, policies, compliance requirements, and automated checks across GitLab deployment options. ### Source excerpt Compliance is the part of software delivery that everyone agrees is important, yet nobody enjoys. It often lives in spreadsheets, screenshots, and the quiet dread of an upcoming audit. GitLab's custom compliance frameworks work differently. Instead of documenting what should be true about your projects, you define the controls once and let the platform continuously verify what is true. In this article, you'll learn why compliance adherence matters, how to stand up a SOC 2 framework in minutes using a template, how to monitor adherence on an ongoing basis, how to enforce compliance with policies, and which standards already ship as ready-to-use templates. You'll also get a look at where AI-specific compliance templates are heading. You can watch the video below to see it in action: Tier note: Compliance frameworks are available in Premium and Ultimate. Framework requirements and controls, the checks that power adherence reporting and the compliance status report, require Ultimate. Everything here applies to GitLab.com, GitLab Self-Managed, and GitLab Dedicated. Why compliance adherence matters and how custom frameworks help Regulatory and contractual obligations like SOC 2, ISO 27001, PCI DSS, and FedRAMP exist because customers, partners, and regulators need assurance that your software is built and shipped responsibly. Falling short isn't just a paperwork problem. It can block deals, trigger fines, delay product launches, and erode trust. Compliance also isn't a one-time event. A project that was compliant at audit time can drift the moment someone disables a scanner, removes branch protection, or merges without the required approvals. Custom compliance frameworks are designed to close that gap. A compliance framework in GitLab is a label you create on a top-level group to identify projects that carry specific compliance obligations or need extra oversight. On its own that label provides organization and visibility. In Ultimate, a framework can also carry requireme ## From signals to systemic risk: Building Risk AI DevFeed: [From signals to systemic risk: Building Risk AI]() Original publisher: [Read original article]() Author: Jaclyn Verga Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Bots]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [ai-security](), [building](), [devsecops](), [engineering](), [reasoning](), [security](), [vulnerabilities]() ### AI overview Datadog describes a risk-detection pipeline and AI agents that correlate security signals to identify and prioritize systemic risk. Deterministic detection is combined with AI-assisted investigation, with security practitioners validating findings and retaining ownership of next steps. ### Source excerpt Datadog's Risk Engineering team built a Systemic Risk Detection Pipeline and Risk AI Agents to identify, contextualize, and prioritize systemic risks. ## Remediation Agents, Demystified: Why Fixing Beats Finding DevFeed: [Remediation Agents, Demystified: Why Fixing Beats Finding]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Vulnerabilities](), [AI-assisted coding](), [pull-requests](), [Responsibility & Safety](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [ai-agents](), [application-security](), [aspm](), [awareness](), [ci-cd](), [code-security](), [community](), [developer](), [devops](), [devsecops](), [enablement](), [interest](), [javascript](), [live-stream](), [node-js](), [pull-requests](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [validation](), [vs-code](), [vulnerabilities]() ### AI overview Snyk's Remediation Agent is presented as a public-preview solution for the growing security-issue backlog. The article explains how coding agents can increase vulnerabilities, why deterministic remediation advice has not been enough, and how validation helps turn fixes into mergeable pull requests. ### Source excerpt See how Snyk's Remediation Agent uses security intelligence, breakability analysis, and validation to turn vulnerabilities into mergeable pull requests. ## Centralize human and agentic work with Datadog Work Management DevFeed: [Centralize human and agentic work with Datadog Work Management]() Original publisher: [Read original article]() Author: Roxanne Moslehi Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Agent](), [incident](), [site-reliability-engineering](), [Slack](), [SRE](), [SIEM, Security](), [GitHub](), [error tracking](), [dashboards](), [Traces]() Tags: [agentic](), [ai](), [ai-agent](), [cloud-siem](), [dashboards](), [devops](), [devsecops](), [error-tracking](), [github](), [incident](), [management](), [slack](), [sre](), [traces](), [work-management](), [workflow-automation]() ### AI overview Datadog Work Management centralizes work created by people, automations, and Datadog AI agents. It preserves context from logs, traces, monitors, alerts, ownership, assignments, approvals, artifacts, and activity while integrating with Datadog and external collaboration systems. ### Source excerpt Learn how Datadog Work Management helps you coordinate human and AI agent-driven work while preserving context, ownership, and activity across tools. ## Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost DevFeed: [Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost]() Original publisher: [Read original article]() Author: Anubhav Sharma Published: 2026-08-12T20:52:35Z Content type: article Language: en Sources: [Nirmata]() Topics: [Kubernetes](), [Cloud](), [Automation](), [data]() Tags: [agents](), [ai](), [automation](), [cloud](), [cloud-native](), [clusters](), [cncf](), [compute](), [cost-savings](), [deployment](), [devsecops](), [kubernetes](), [kyverno](), [policy-management](), [resource](), [verification]() ### AI overview Nirmata describes applying its Cost Analyzer and Resource Hygiene Cloud Agents across an enterprise customer's 40-cluster production Kubernetes fleet. The scans identified a roughly $107,000 monthly compute baseline and about 50% recoverable through right-sizing before stale-resource cleanup, while revealing recurring sources of waste and governance gaps. ### Source excerpt Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost Most Kubernetes Cost overruns don't come from one singularly bad decision. They come from dozens of reasonable ones -- made independently, by different teams, at different times -- that... The post Nirmata's Cloud Agents Audited a 40-Cluster Kubernetes Fleet, and recovered 40% of the Cost first appeared on Nirmata. ## AI governance gaps leave developer and agent access to AI tools insufficiently controlled DevFeed: [AI governance gaps leave developer and agent access to AI tools insufficiently controlled]() Original publisher: [Read original article]() Author: Anubhav Sharma Published: 2026-08-05T01:56:41Z Content type: opinion Language: en Sources: [Nirmata]() Topics: [ai-governance](), [Security](), [Access Control](), [Authorization](), [developer-productivity](), [Monitoring](), [Large Language Model]() Tags: [access-control](), [ai](), [ai-governance](), [cloud-native](), [cncf](), [developer](), [devsecops](), [engineering](), [kubernetes](), [kyverno](), [llms](), [policy-management](), [risk-management](), [security](), [visibility]() ### AI overview The article presents aggregated responses from security leaders about AI governance in fast-moving engineering organizations. It identifies gaps in centralized access control, policy enforcement, session auditing, real-time visibility, and protection against exposing permissions or environment variables to LLMs. ### Source excerpt Three Questions We Asked About AI Governance -- And What the Answers Reveal Over the past few months, we've been having the same conversation on repeat with security leaders about AI at fast-moving engineering organizations. Different companies, different tech stacks, same three questions -- and,... The post The AI Innovation-Security Paradox first appeared on Nirmata. ## Stop The Sprawl Snyk Secrets Now Generally Available DevFeed: [Stop The Sprawl Snyk Secrets Now Generally Available]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-08-04T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Responsibility & Safety](), [AI-assisted coding](), [CI/CD]() Tags: [agentic](), [ai](), [ai-agents](), [ai-coding](), [ai-security](), [api-keys](), [application-security](), [blog](), [ci-cd](), [code-security](), [convert-paid](), [developer](), [devops](), [devsecops](), [interest](), [scm](), [secrets](), [security](), [snyk-platform]() ### AI overview Snyk announces general availability of Snyk Secrets, a contextual ML-powered secrets detection capability for its AI Security Platform. The article frames it as a response to faster secret exposure in AI-driven development and CI/CD workflows. ### Source excerpt Snyk Secrets is now generally available, bringing contextual ML detection, secure-at-commit prevention, and unified secrets governance to the Snyk AI Security Platform. ## Managing Environment Variables and Secrets in DevOps DevFeed: [Managing Environment Variables and Secrets in DevOps]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-08-01T13:00:47Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [DevOps](), [configuration](), [GitGuardian](), [GitHub](), [Terraform](), [Kubernetes]() Tags: [ci-cd-secrets-management](), [cloud](), [collaboration](), [collaboration-tools](), [configuration](), [container](), [crash](), [devops](), [devsecops](), [dynamic-secrets](), [env-file-security](), [environment-variables](), [gitguardian](), [github](), [hardcoded-credentials](), [hashicorp-vault](), [jira](), [kubernetes](), [kubernetes-secrets](), [leak](), [managing-environment-variables-and-secrets](), [rotation](), [safety](), [sealed-secrets](), [secret-rotation](), [secret-scanning](), [secrets](), [secrets-management-best-practices](), [security](), [storage](), [terraform](), [vault]() ### AI overview A guide to managing environment variables and secrets in DevOps. It explains how secrets escape through repositories, processes, crash reports, containers, collaboration tools, Kubernetes Secrets, and Terraform state, and discusses storage, access controls, lifecycles, and rotation. ### Source excerpt Nearly 29 million secrets were pushed to public GitHub in a single year, and most teams still discover their own leaks by accident. Here is how environment variables actually escape, and what to use instead at each stage. ## Best GitLab Alternatives in 2026 DevFeed: [Best GitLab Alternatives in 2026]() Original publisher: [Read original article]() Author: Pete Miloravac Published: 2026-07-30T12:04:04Z Content type: comparison Language: en Sources: [Semaphore Engineering]() Topics: [GitLab](), [CI/CD](), [DevSecOps](), [GitHub Actions]() Tags: [2026](), [alternatives](), [ci-cd](), [circleci](), [compare](), [cost](), [devsecops](), [enterprise-deployment](), [evaluation](), [github](), [github-actions](), [gitlab](), [gitlab-ci](), [self-hosted]() ### AI overview A comparison of Semaphore, GitHub Actions, CircleCI, and Buildkite as alternatives to GitLab for CI/CD-focused decisions. It examines execution, deployment, pricing, support, and operating models while explaining when retaining GitLab repositories may be more practical than replacing GitLab entirely. ### Source excerpt GitLab is a capable, integrated DevSecOps platform--but it is not the only sensible way to run CI/CD. Teams comparing GitLab alternatives are often trying to improve pipeline execution, simplify operations, get clearer support and cost options, or adopt a CI/CD tool that fits their existing source-control strategy. That does not automatically mean moving repositories out [...] The post Best GitLab Alternatives in 2026 appeared first on Semaphore. ## Secure at Inception: Announcing the Snyk Studio Integration for Snowflake Cortex Code DevFeed: [Secure at Inception: Announcing the Snyk Studio Integration for Snowflake Cortex Code]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-07-30T00:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [AI Bots]() Tags: [ai](), [ai-agent](), [application-security](), [article](), [cloud-security](), [code](), [code-security](), [containers](), [developer](), [devops](), [devsecops](), [enablement](), [finserv](), [health-care](), [integration](), [python](), [security](), [snyk-code](), [snyk-container](), [snyk-open-source](), [snyk-platform](), [supply-chain-security](), [vulnerabilities]() ### AI overview Snyk Studio is integrated with Snowflake Cortex Code to scan AI-generated application code, dependencies, and container images for vulnerabilities during development. ### Source excerpt Snyk Studio integrates with Snowflake Cortex Code to scan AI-generated code, dependencies, and containers for vulnerabilities during development. ## The Attacker Never Sleeps, Neither Can Your Testing DevFeed: [The Attacker Never Sleeps, Neither Can Your Testing]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-07-30T00:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI-assisted coding](), [Developer Tools]() Tags: [agents](), [ai](), [application-security](), [awareness](), [blog](), [code](), [code-security](), [devops](), [devsecops](), [executive](), [finserv](), [interest](), [open-source-security](), [security](), [snyk-apprisk](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [software](), [supply-chain-security](), [tech](), [testing](), [vulnerability-insights]() ### AI overview AI-driven code generation is expanding software attack surfaces while giving attackers machine-speed reasoning capabilities. The article argues that security teams need continuous testing, agent governance, and independent validation of findings. ### Source excerpt AI is accelerating software development and giving attackers machine-speed capabilities. Security teams must continuously test AI-built code, govern agents, and independently validate every finding. ## Critical Kyverno Vulnerability -- CVE-2026-54523 DevFeed: [Critical Kyverno Vulnerability -- CVE-2026-54523]() Original publisher: [Read original article]() Author: Anubhav Sharma Published: 2026-07-29T01:08:41Z Content type: news Language: en Sources: [Nirmata]() Topics: [Kyverno](), [vulnerability](), [Exploit](), [Kubernetes](), [Cloud Native Ecosystem]() Tags: [2026](), [admission-controller](), [ai](), [cloud-native](), [cloud-native-ecosystem](), [cncf](), [cve](), [devsecops](), [engineering](), [kubernetes](), [kyverno](), [nctl-ai](), [platform-engineering](), [policy](), [policy-as-code](), [policy-management](), [release](), [upgrade](), [vulnerability]() ### AI overview The article reports CVE-2026-54523, a critical Kyverno vulnerability affecting versions 1.18.0 and 1.18.1. A tenant able to create a NamespacedMutatingPolicy could bypass namespace isolation and generate resources in other namespaces, potentially enabling cluster-wide privilege escalation. Kyverno 1.18.2 patches the vulnerability. ### Source excerpt Critical Kyverno Vulnerability -- CVE-2026-54523 On July 13, 2026, a critical vulnerability was disclosed in Kyverno, the Kubernetes-native policy engine used broadly across the cloud native ecosystem for policy-as-code enforcement. The vulnerability, tracked as CVE-2026-54523 (GHSA-79gf-7frw-68m9), allows a tenant with permission to create a NamespacedMutatingPolicy... The post Critical Kyverno Vulnerability -- CVE-2026-54523 first appeared on Nirmata. ## The Generator Can't Be the Validator: What OpenAI's Hugging Face Incident Proves About AI Security DevFeed: [The Generator Can't Be the Validator: What OpenAI's Hugging Face Incident Proves About AI Security]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [incident](), [AI Chat]() Tags: [ai](), [ai-security](), [application-security](), [awareness](), [benchmark](), [blog](), [developer](), [devops](), [devsecops](), [enablement](), [evaluation](), [executive](), [external](), [gpt](), [hugging-face](), [incident](), [openai](), [safety](), [security](), [security-labs](), [snyk-platform](), [vulnerability]() ### AI overview The article argues that a disclosed OpenAI and Hugging Face incident demonstrates the need for independent, continuous AI safety validation. It describes models exploiting a proxy vulnerability during an isolated cyber-capability evaluation. ### Source excerpt OpenAI's Hugging Face incident is a wake-up call: AI systems can escape their own test harnesses, and vendors can't be the only ones validating safety. ## What Is AI Pentesting and How Does It Work? DevFeed: [What Is AI Pentesting and How Does It Work?]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-07-27T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Security](), [vulnerability](), [AI Models](), [Authorization](), [large-language-models](), [Reconnaissance](), [payload](), [SQL]() Tags: [ai](), [application-security](), [article](), [awareness](), [developer](), [devops](), [devsecops](), [interest](), [large-language-models](), [models](), [payload](), [security](), [security-labs](), [snyk-platform](), [sql](), [systems](), [tech](), [tools](), [vulnerabilities](), [vulnerability]() ### AI overview AI pentesting uses reasoning-capable models, deterministic testing tools, independent validation, and target context to continuously find, exploit, and verify application vulnerabilities that traditional scanners may miss. The article explains its workflow and contrasts context-dependent flaws with heuristic-detectable issues such as SQL injection and misconfigurations. ### Source excerpt AI pentesting uses reasoning-capable models to continuously find and validate the flaws scanners miss, especially broken authorization and business-logic abuse. ## Best Jenkins Alternatives in 2026 DevFeed: [Best Jenkins Alternatives in 2026]() Original publisher: [Read original article]() Author: Pete Miloravac Published: 2026-07-24T09:56:00Z Content type: article Language: en Sources: [Semaphore Engineering]() Topics: [CI/CD](), [Jenkins](), [Groovy](), [GitHub Actions](), [GitLab](), [Vulnerabilities](), [DevOps](), [Open Source]() Tags: [ci-cd](), [circleci](), [compare](), [devsecops](), [github-actions](), [gitlab](), [groovy](), [jenkins](), [open-source](), [security](), [security-vulnerabilities](), [self-hosted]() ### AI overview A 2026 guide compares Jenkins alternatives and explains why teams migrate away from Jenkins, citing maintenance overhead, plugin conflicts, Groovy pipeline complexity, and security-patch concerns. It highlights Semaphore, GitHub Actions, GitLab CI/CD, and CircleCI for different use cases. ### Source excerpt Jenkins earned its place as the default CI server of the 2010s: it was free, endlessly extensible, and available before any serious managed CI/CD competitor existed. More than a decade later, that same flexibility has become the thing teams complain about most -- plugin sprawl, Groovy pipeline scripts nobody wants to maintain, and infrastructure that [...] The post Best Jenkins Alternatives in 2026 appeared first on Semaphore. ## Platform Product Management: AI-enabled vs. AI-native capability stacks DevFeed: [Platform Product Management: AI-enabled vs. AI-native capability stacks]() Original publisher: [Read original article]() Author: Poojitha Marreddy Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Product Management](), [code productivity](), [developer-productivity](), [Artificial Intelligence](), [Infrastructure as code](), [Terraform](), [DevSecOps](), [GitHub](), [Jenkins](), [Kubernetes]() Tags: [agents](), [ai](), [code](), [cognitive-load](), [configuration](), [developer](), [developer-productivity](), [devsecops](), [github](), [iac](), [jenkins](), [kubernetes](), [management](), [platform](), [product](), [productivity](), [terraform]() ### AI overview This article presents platform product management as a way to reduce developer cognitive load and improve delivery velocity. It contrasts AI-enabled capability stacks, which use AI to assist developers, with AI-native stacks, which run AI workloads; both depend on an internal developer platform but require different investments, governance, and success metrics. The article introduces the double diamond framework for discovering, defining, developing, and delivering platform capabilities. ### Source excerpt Platform PM framework: Shift from tool standardization to product thinking. Learn to build AI-enabled vs AI-native stacks to boost developer productivity ## Harness CLI: One Command Line for Humans and AI Agents DevFeed: [Harness CLI: One Command Line for Humans and AI Agents]() Original publisher: [Read original article]() Author: Mohit Suman Published: 2026-07-15T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Command-line interface](), [DevSecOps](), [Open Source](), [Artificial Intelligence](), [GitHub]() Tags: [agents](), [ai](), [ai-agents](), [cli](), [command-line](), [devsecops](), [github](), [open-source](), [terminal](), [tool]() ### AI overview Harness announces the public beta of Harness CLI 3.0, a single Apache-2.0 command-line tool that unifies commands, grammar, and authentication across the Harness platform. The article describes support for terminal workflows used by developers and AI agents. ### Source excerpt Harness CLI 3.0 is now in public beta: one binary, one grammar, and one auth flow across pipelines, CD, Code, IaCM, and more. Built for humans and AI agents. | Blog [Next page]()