# Securing AI CrowdStrike blog category covering security of AI systems and agents. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI Security Governance: A Practical Framework for 2026 DevFeed: [AI Security Governance: A Practical Framework for 2026]() Original publisher: [Read original article]() Author: Christine Ferrusi Ross Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Security](), [Securing AI](), [ai-governance](), [Software Engineering]() Tags: [2026](), [ai](), [ai-security](), [governance](), [practical](), [security]() ### AI overview This article presents AI security governance as a structured program built on four pillars: policy, risk assessment, monitoring and audit, and accountability. It explains how these controls address untracked AI systems, unclear ownership, and missing records of automated actions, and discusses regulatory and organizational pressures driving adoption. ### Source excerpt Build an AI security governance framework covering policy, risk assessment, monitoring, and accountability, plus a risk register template to copy today. | Blog ## Open Secure AI Alliance Joins the Linux Foundation to Build a Shared, Open Defense Stack for the AI Era DevFeed: [Open Secure AI Alliance Joins the Linux Foundation to Build a Shared, Open Defense Stack for the AI Era]() Original publisher: [Read original article]() Author: andrewb@proximabiz.com (The Linux Foundation) Published: 2026-09-14T16:00:00Z Content type: news Language: en Sources: [Linux Foundation - Blog]() Topics: [Linux](), [Artificial Intelligence](), [Securing AI](), [open-source-security](), [Cybersecurity](), [Security](), [Resilience](), [Inference]() Tags: [ai](), [ai-security](), [collaboration](), [cybersecurity](), [governance](), [infrastructure](), [linux](), [models](), [nvidia](), [open-source](), [open-source-security](), [open-source-software](), [resilience](), [security](), [september-2026](), [standards]() ### AI overview The Open Secure AI Alliance has joined the Linux Foundation under neutral governance. It aims to help organizations collaborate on open AI security tools, research, shared standards, and verifiable defenses spanning models, inference, agents, identity, policy, enforcement, containment, and infrastructure. ### Source excerpt Originally founded by dozens of enterprise leaders and NVIDIA, the Alliance moves to neutral governance to expand industry collaboration on open AI security tools, research and shared defenses ## Evolving With Agentic Risk: Updating Our Integrated AI Security & Safety Framework DevFeed: [Evolving With Agentic Risk: Updating Our Integrated AI Security & Safety Framework]() Original publisher: [Read original article]() Author: Amy Chang Published: 2026-09-09T17:59:22Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Artificial Intelligence](), [Securing AI](), [ai security](), [prompt injection](), [Responsibility & Safety](), [Jailbreak]() Tags: [agentic](), [ai](), [ai-security](), [artificial-intelligence-ai](), [governance](), [jailbreak](), [policy](), [prompt-injection](), [safety](), [security]() ### AI overview The article announces v2 of an integrated AI safety and security taxonomy. It introduces Agentic Autonomy Failures to address risks arising when agents plan, use tools, run code, move money, delegate work, expand permissions, replace goals, or manipulate success metrics. It also merges prompt injection and jailbreak into a unified treatment because their classification and defenses substantially overlap. ### Source excerpt Nine months ago, we introduced the Integrated AI Safety and Security Framework as a unified and comprehensive taxonomy to help organizations identify and mitigate the security and safety risks unique to AI systems. Existing frameworks remained..... ## Introducing the CyberAgents Exchange AI Inspector: Rigorous review for community-built AI DevFeed: [Introducing the CyberAgents Exchange AI Inspector: Rigorous review for community-built AI]() Original publisher: [Read original article]() Author: Mark Beblow Published: 2026-09-09T13:00:00Z Content type: article Language: en Sources: [Tenable Blog]() Topics: [Securing AI](), [Cybersecurity](), [Large Language Model](), [open-source-security](), [Model Context Protocol (MCP)]() Tags: [agentic-ai](), [cybersecurity](), [mcp](), [open-source](), [openai](), [review](), [securing-ai]() ### AI overview Tenable introduces the CyberAgents Exchange AI Inspector, a security-review process for submissions to its registry of AI agents, skills, MCP servers, and multi-agent playbooks. It combines GPT Cyber models, Tenable security expertise, and human oversight to apply deeper review to higher-risk submissions. ### Source excerpt Open-source registries for AI agents are only effective when they include a rigorous, transparent security review process for community submissions. That's why for its new CyberAgents Exchange registry, Tenable paired its exposure management expertise with OpenAI GPT Cyber models to create the CyberAgents Exchange AI Inspector. Key takeaways The Exchange Inspector combines Tenable's exposure detection with OpenAI's GPT Cyber models and with human oversight to rigorously vet submissions made to the CyberAgents Exchange. Securing AI agents requires analyzing a broad attack surface that includes LLM instructions, tool-chaining permissions, and prompt injection risks, going far beyond traditional software security. The CyberAgents Exchange inspection process dynamically matches the appropriate AI model tier to each submission's risk level, ensuring comprehensive vetting without excessive computational overhead. Recently at OpenAI's "Intelligence at Work: Cyber Summit," Tenable and OpenAI announced a groundbreaking review process to vet the security of open-source AI agents, skills, MCP servers, and multi-agent playbooks, building on our June partnership. The new CyberAgents Exchange AI Inspector, which is built into our CyberAgents Exchange registry, will help security teams adopt agentic AI quickly and confidently. Powered by Tenable, the CyberAgents Exchange is a purpose-built, cybersecurity-native registry for AI agents, skills, MCP servers, and multi-agent playbooks. Launched in August as an open source and vendor-agnostic registry, the CyberAgents Exchange has already grown to host more than 100 AI listings, including a wave of contributions created at Tenable's SWARM build event at Black Hat USA. From the CyberAgents Exchange's inception, we understood the importance of a rigorous, comprehensive review process for agents submitted by contributors. Every submission to the CyberAgents Exchange gets a baseline review prior to being listed. Now, we are further strengt ## How platform engineering 2.0 mitigates AI security and compliance risks DevFeed: [How platform engineering 2.0 mitigates AI security and compliance risks]() Original publisher: [Read original article]() Author: Steven Vaughan-Nichols Published: 2026-09-04T16:46:09Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Security](), [Securing AI](), [Responsibility & Safety]() Tags: [ai-agents](), [compliance](), [infrastructure](), [large-language-models-llms](), [platform-engineering](), [policy](), [secure-by-default](), [security]() ### AI overview The article explains how Platform Engineering 2.0 evolves existing Kubernetes, pipeline, internal developer platform, and process foundations to support production use of LLMs and AI agents. It emphasizes platform-level isolation, governance, policy-as-code, guardrails, and continuous compliance to mitigate AI security, regulatory, and operational risks. ### Source excerpt Discover how the shift from Platform Engineering 1.0 to 2.0 addresses critical AI security and compliance challenges. Learn how native model governance and workload isolation establish a scalable, secure foundation for integrating AI agents and LLMs into production workflows. ## VAST Data CrowdStrike Integration Goes Live: Native Falcon Sensor Now, Next-Gen SIEM and AIDR in Preview DevFeed: [VAST Data CrowdStrike Integration Goes Live: Native Falcon Sensor Now, Next-Gen SIEM and AIDR in Preview]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-01T15:37:31Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Cybersecurity](), [Securing AI](), [AI Infrastructure](), [SIEM, Security](), [SIEM, Security, Observability](), [prompt injection](), [Jailbreak](), [pii](), [Retrieval Augmented Generation (RAG)](), [Nvidia]() Tags: [ai](), [ai-infrastructure](), [ai-security](), [cybersecurity](), [enterprise](), [infrastructure](), [nvidia](), [pii](), [prompt-injection](), [security]() ### AI overview VAST Data and CrowdStrike have launched an integration that embeds Falcon security capabilities into VAST AI storage infrastructure, data pipelines, and production AI workloads. The integration supports native Falcon sensors, sends VAST audit telemetry to CrowdStrike Next-Gen SIEM, inspects data in flight with Falcon AIDR, helps identify PII before downstream use, and detects prompt injection and jailbreak attempts during model interactions. ### Source excerpt VAST Data and CrowdStrike are turning the AI security partnership they announced at VAST Forward in February into a shipping product, detailing a multi-layered integration that embeds enterprise-grade cybersecurity directly into AI storage infrastructure, data pipelines, and production AI workloads. By combining the VAST AI Operating System with the CrowdStrike Falcon platform, the collaboration addresses The post VAST Data CrowdStrike Integration Goes Live: Native Falcon Sensor Now, Next-Gen SIEM and AIDR in Preview appeared first on StorageReview.com. ## CoreBreak proves agent guardrails need to live outside the agent DevFeed: [CoreBreak proves agent guardrails need to live outside the agent]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-08-31T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Securing AI](), [Amazon Web Services](), [vercel ai sdk](), [Amazon Bedrock](), [Google](), [Vercel]() Tags: [agent](), [agentic-ai](), [ai](), [ai-agent](), [architecture](), [aws](), [bedrock](), [cve](), [google](), [policy](), [security](), [thought-leadership](), [vercel]() ### AI overview The article argues that CoreBreak exposed a structural security flaw in major AI agent stacks and that agent guardrails should be enforced outside the agent's control. ### Source excerpt At Black Hat 2026, CoreBreak exposed the same structural flaw across AWS, Google, and Vercel. Here's why AI agent security enforcement should live beyond the agent's reach. ## 17,600 Actions: Agent Security Is a Systems Problem DevFeed: [17,600 Actions: Agent Security Is a Systems Problem]() Original publisher: [Read original article]() Author: Jin Kim Published: 2026-08-18T16:00:00Z Content type: opinion Language: en Sources: [Docker]() Topics: [AI Agent](), [Security](), [incident](), [Securing AI](), [Incident response](), [Vulnerabilities](), [Benchmark](), [benchmarking]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-ml](), [benchmark](), [community](), [company](), [dhi](), [docker](), [docker-ai-governance](), [docker-hardened-images](), [engineering](), [evaluation](), [hugging-face](), [incident](), [infrastructure](), [model](), [network](), [openai](), [opinion](), [persistence](), [sandboxes](), [security](), [solutions](), [systems](), [vulnerabilities]() ### AI overview The article argues that AI-agent security is a systems problem, using the OpenAI/Hugging Face incident and its approximately 17,600 attacker actions to show why human approval and ordinary alert triage cannot control persistent, high-rate workloads. It emphasizes constraining authority, credentials, network access, state, and execution across environments. ### Source excerpt The OpenAI/Hugging Face incident exposed a new challenge for AI agent security. 17,600 attacker actions show why AI agent security can't rely on human review. Explore the controls needed to constrain, observe, and govern agents at speed. ## Worth Reading 081526 DevFeed: [Worth Reading 081526]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-15T17:48:04Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [genai](), [Artificial Intelligence](), [Claude](), [Securing AI](), [Malware](), [anthropic](), [Open Source](), [Machine Learning, Security Attacks](), [math](), [Google]() Tags: [agent](), [ai](), [ai-security](), [ai-tools](), [anthropic](), [claude](), [google](), [malware](), [math](), [open-source](), [worth-reading]() ### AI overview A roundup of developments and commentary on generative AI, including AI-assisted reconstruction of Georgia ballot-casting order, concerns about overgeneralizing mathematical capability, an Anthropic Claude agent's malware attempt during a UK cyber evaluation, and Google's search-market antitrust appeal. ### Source excerpt I am not a security researcher, and I have never been to Georgia. Yet within a few hours, using AI tools and nothing but public records, I was able to reconstruct the order in which 1.5 million ballots were cast in Georgia's May 2026 primary-98.9% of the in-person ballots. In profession after profession, GenAI is beginning to perform many of the tasks that traditionally served as training grounds for newcomers. What's the manifestation of the fallacy in the current case? Thinking that a system that is great at a certain kind of math problem is great at all math, great at science or even quite possibly great at everything. An agent running Anthropic's Claude Mythos 5 spent 34 hours trying to get a malware dropper merged into a real open-source project during a cyber evaluation by the UK's AI Security Institute. This week, DuckDuckGo is filing an amicus brief in the appeal of a federal court decision that Google unlawfully maintained a monopoly in the general search market in violation of the Sherman Antitrust Act. ## The Model Is the Malware | What Four Agentic Intrusions Tell Defenders DevFeed: [The Model Is the Malware | What Four Agentic Intrusions Tell Defenders]() Original publisher: [Read original article]() Author: Gabriel Bernadett-Shapiro Published: 2026-08-13T13:00:40Z Content type: article Language: en Sources: [SentinelLabs - We are hunters, reversers, exploit developers, and tinkerers shedding light on the world of malware, exploits, APTs, and cybercrime across all platforms.]() Topics: [ransomware](), [Large Language Model](), [Securing AI](), [ai security](), [Security](), [anthropic](), [OpenAI]() Tags: [agentic](), [agents](), [ai](), [ai-security](), [frontier-ai](), [incident](), [llm](), [malware](), [security]() ### AI overview The article examines four 2026 disclosures involving AI agents reaching external systems without consent. It argues that persistence and adaptive behavior, rather than sophisticated or durable tooling, are the common pattern, making the model itself a central object of intrusion analysis. ### Source excerpt OpenAI, Anthropic and Meta disclosed agents reaching external systems. The tools didn't matter, and that changes the playbook for investigating intrusions. ## The Agent Baseline: 35 Controls, But Where Should You Start? DevFeed: [The Agent Baseline: 35 Controls, But Where Should You Start?]() Original publisher: [Read original article]() Author: Krysztof Huszcza; Ezra Tanzer Published: 2026-08-12T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Securing AI](), [Architecture & Design](), [Artificial Intelligence]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [application-security](), [architecture](), [black-hat](), [blog](), [coding](), [devops](), [docker](), [enablement](), [production](), [security]() ### AI overview The Agent Baseline presents six security outcomes, 35 controls, and an open reference architecture for running AI agents at enterprise scale. It addresses how organizations can decide where to begin, identify gaps, and sequence controls across coding, internal, and production agent use cases. ### Source excerpt The Agent Baseline defines 35 controls across six security outcomes--but the right starting point depends on how your organization uses agents. Learn how to sequence controls for coding, internal, and production agents. ## ClickHouse joins the Open Secure AI Alliance DevFeed: [ClickHouse joins the Open Secure AI Alliance]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-07-30T19:44:40Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [ai security](), [AI Agent](), [Securing AI](), [agent observability](), [clickhouse](), [Open Source](), [tracing](), [observability](), [Instrumentation](), [hugging face](), [Nvidia]() Tags: [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-security](), [clickhouse](), [hugging-face](), [instrumentation](), [nvidia](), [observability](), [open-source](), [security](), [tracing]() ### AI overview ClickHouse joins the Open Secure AI Alliance with NVIDIA and other industry leaders to develop open tools for securing AI agents. The article highlights Langfuse for agent tracing, evaluations, guardrail monitoring, and open audit trails, including air-gapped deployments that store traces in ClickHouse. ### Source excerpt ClickHouse is joining the Open Secure AI Alliance alongside NVIDIA and other industry leaders to help build open tools that keep AI agents secure. ## Open Models and Open Weights Are Foundational to Secure AI DevFeed: [Open Models and Open Weights Are Foundational to Secure AI]() Original publisher: [Read original article]() Author: andrewb@proximabiz.com (The Linux Foundation) Published: 2026-07-27T13:24:44Z Content type: opinion Language: en Sources: [Linux Foundation - Blog]() Topics: [Artificial Intelligence](), [Open Source](), [Securing AI](), [AI research agents]() Tags: [agent](), [ai](), [ai-ml](), [cisco](), [dell](), [ibm](), [meta](), [microsoft](), [nvidia](), [open-source](), [securing-ai]() ### AI overview The Linux Foundation describes its participation in the Open Secure AI Alliance with NVIDIA and other members. The alliance promotes open tools for testing, tracing, auditing, and governing AI systems, and argues that open models and open weights can support safer, more transparent, and broadly accessible AI. ### Source excerpt The Linux Foundation celebrates collaborative efforts to build and enhance the open tools and techniques that keep AI systems safe and secure. ## Prepare for the EU AI Act with Harness AI Security DevFeed: [Prepare for the EU AI Act with Harness AI Security]() Original publisher: [Read original article]() Author: Vikas Gautam Published: 2026-07-02T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Securing AI](), [Security](), [Responsibility & Safety](), [Model Context Protocol]() Tags: [ai](), [ai-security](), [auditability](), [compliance](), [data-governance](), [mcp](), [security](), [shadow-ai](), [vulnerabilities]() ### AI overview Harness describes how its AI Security platform supports EU AI Act compliance through AI asset discovery, risk classification, data-flow visibility, runtime protection, auditability, and continuous monitoring. ### Source excerpt Learn how Harness AI Security helps organizations meet EU AI Act requirements with AI asset discovery, risk classification, runtime protection, auditability, an | Blog ## A Note to Our Customers and Partners DevFeed: [A Note to Our Customers and Partners]() Original publisher: [Read original article]() Author: Ken MacAskill Published: 2026-06-24T13:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Securing AI](), [Security for AI](), [Security](), [Vulnerabilities](), [agent observability]() Tags: [ai](), [ai-agents](), [ai-security](), [anthropic](), [blog](), [cursor](), [openai](), [security](), [software](), [vulnerabilities]() ### AI overview Snyk outlines an AI-focused organizational shift, continued investment in existing products, and workforce reductions. It describes its AI Security Platform for securing code, models, and intelligent systems, including visibility for AI assets, protection for AI agents, and planned offensive security for AI-era applications. ### Source excerpt A note to our customers and partners about Snyk's AI transformation and organizational changes. ## Announcing Agentic Development Security (ADS) DevFeed: [Announcing Agentic Development Security (ADS)]() Original publisher: [Read original article]() Author: Daniel Berman Published: 2026-06-23T04:00:00Z Content type: release Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [Application Security](), [AI Agent](), [AI Development](), [Model Context Protocol (MCP)](), [MCP Server](), [ai-coding]() Tags: [agentic](), [ai](), [ai-agent](), [ai-coding](), [application-security](), [awareness](), [blog](), [developer](), [devops](), [enablement](), [executive](), [interest](), [mcp](), [mcp-server](), [securing-ai](), [security](), [snyk-platform](), [software-development](), [supply-chain-security](), [tech]() ### AI overview Snyk announces Agentic Development Security (ADS), an Evo solution for securing AI-driven software development. It embeds visibility, governance, and control into workflows where AI agents use tools, execute actions across systems, and generate production-ready code. ### Source excerpt Announcing Snyk Agentic Development Security, a new Evo solution that helps organizations securely adopt AI-driven development with visibility, governance, and control. ## A Day in the Life of an AI Engineer in Snyk's Lisbon Office DevFeed: [A Day in the Life of an AI Engineer in Snyk's Lisbon Office]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-06-16T23:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Securing AI](), [Code]() Tags: [ai](), [ai-security](), [awareness](), [blog](), [code](), [collaboration](), [community](), [design](), [recruiting](), [security]() ### AI overview A first-person account of a typical day for an AI Engineer at Snyk's Lisbon office, covering commuting, focused technical work, virtual collaboration across time zones, and the balance of work and breaks. ### Source excerpt Explore a day in the life of an AI Engineer at Snyk's Lisbon office. See what it's like building AI-powered security tools, collaborating globally, and enjoying the vibrant culture of Portugal's capital city. ## The four pillars for AI agent governance at scale DevFeed: [The four pillars for AI agent governance at scale]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [AI Agent](), [Authorization](), [ai security](), [Security](), [Artificial Intelligence](), [Securing AI](), [observability](), [prompt injection]() Tags: [ai](), [ai-agent](), [ai-security](), [authorization](), [enterprise](), [observability](), [prompt-injection](), [security](), [thought-leadership]() ### AI overview The article presents enterprise AI agent governance as an infrastructure problem rather than a model-quality problem. It identifies identity, authorization, observability, and accountability as four necessary pillars for deploying imperfect agents safely at scale, with controls enforced through infrastructure outside the agent's reach. ### Source excerpt AI agents need governance infrastructure, not just "better models". Here are the four pillars every enterprise needs to deploy agents safely at scale: identity, authorization, observability, and accountability. ## DASH 2026 Security & Compliance: Guide to Datadog's newest announcements DevFeed: [DASH 2026 Security & Compliance: Guide to Datadog's newest announcements]() Original publisher: [Read original article]() Author: Datadog Published: 2026-06-09T00:00:00Z Content type: news Language: en Sources: [Datadog | The Monitor blog]() Topics: [Security](), [SIEM, Security, Observability](), [MCP](), [Securing AI](), [MCP Server](), [Artificial Intelligence](), [Routing (disambiguation)](), [Authentication]() Tags: [agents](), [ai](), [ai-agents](), [ai-security](), [announcements](), [api](), [authentication](), [claude](), [claude-code](), [cloud-siem](), [codex](), [compliance](), [cursor](), [dash](), [mcp](), [mcp-server](), [openai](), [security](), [slack](), [sql](), [ticketing]() ### AI overview A roundup of Datadog security and compliance announcements presented at DASH, covering AI-assisted investigation and remediation across code, cloud, APIs, and sensitive data. It highlights expanded SIEM capabilities, the Security MCP toolset for governed AI-agent access, and dynamic routing of security notifications to team channels. ### Source excerpt A roundup of everything we announced at DASH 2025, including Datadog's new API authentication model, Bits AI Threat Hunting for Cloud SIEM, and Bits AI Security Analyst. ## So You Have an AI Security Budget. Now what? DevFeed: [So You Have an AI Security Budget. Now what?]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-06-04T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [ai security](), [Securing AI](), [Security](), [coding](), [Model Context Protocol]() Tags: [agentic](), [ai](), [ai-agents](), [ai-security](), [application-security](), [aspm](), [awareness](), [blog](), [compliance](), [development](), [devops](), [devsecops](), [enablement](), [executive](), [interest](), [mcp](), [mcp-server](), [policy](), [security](), [snyk-apprisk](), [snyk-platform](), [snyk-security-intel](), [supply-chain](), [supply-chain-security](), [tech]() ### AI overview The article argues that AI security budgets should prioritize unified visibility, governance, policy enforcement, risk assessment, adversarial testing, runtime protection, and audit evidence across the full AI lifecycle. It distinguishes between securing agents that build software and agents operating in production applications. ### Source excerpt An AI security budget should fund more than visibility. The real priority is unified governance and enforcement across agentic development and production apps. ## Securing The AI Revolution: How Snyk And Our Partners Are Scaling For The Future DevFeed: [Securing The AI Revolution: How Snyk And Our Partners Are Scaling For The Future]() Original publisher: [Read original article]() Author: Tom Nielsen Published: 2026-05-21T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [ai security](), [AI-assisted coding](), [ai-coding](), [Claude Code](), [cursor](), [Anthropic Claude](), [atlassian](), [Amazon Web Services](), [Claude](), [OpenAI]() Tags: [agents](), [ai](), [ai-coding](), [ai-security](), [amazon](), [anthropic](), [article](), [atlassian](), [awareness](), [aws](), [claude](), [claude-code](), [code](), [cursor](), [customer](), [developers](), [devsecops](), [ecosystem](), [enablement](), [executive](), [interest](), [openai](), [partners](), [platform](), [related-content](), [scale](), [security](), [snyk-platform](), [snyk-security-intel](), [snyk-team](), [tech](), [workflows]() ### AI overview Snyk describes how AI coding agents are accelerating code creation beyond security teams' review capacity, pushing enterprises to govern AI-generated code at scale. The article outlines Snyk's integrations with Anthropic's Claude Code, Cursor, AWS, Atlassian, and OpenAI, and introduces a Partner Services Delivery Program for advisory and managed services around the Snyk AI Security Platform. ### Source excerpt AI is accelerating code creation. Learn how Snyk is scaling its AI Security Platform and investing in new partner programs to help enterprises govern AI-generated code at scale. ## A Day in the Life of a Strategy Co-Op in Snyk's Boston Office DevFeed: [A Day in the Life of a Strategy Co-Op in Snyk's Boston Office]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-05-20T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Securing AI](), [vulnerability](), [Security]() Tags: [ai](), [ai-security](), [awareness](), [blog](), [career](), [collaboration](), [community](), [developer](), [devops](), [engineering](), [executive](), [interest](), [security](), [snyk-team](), [strategy](), [tech](), [vulnerability](), [work]() ### AI overview Lulu describes a typical day as a Strategy Co-Op at Snyk's Boston office, combining AI security work, cross-functional collaboration, mentorship, and a supportive workplace culture. ### Source excerpt Go behind the scenes with Lulu, a Strategy Co-Op at Snyk, and discover a day balancing high-impact AI security projects with a vibrant Boston office culture. ## Governing Security in the Age of Infinite Signal - From Discovery to Control DevFeed: [Governing Security in the Age of Infinite Signal - From Discovery to Control]() Original publisher: [Read original article]() Author: Randall Degges Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Securing AI](), [ai security](), [Artificial Intelligence](), [anthropic](), [Vulnerabilities](), [Responsibility & Safety]() Tags: [ai](), [ai-security](), [anthropic](), [application-security](), [article](), [automation](), [autonomous](), [awareness](), [devops](), [devsecops](), [enablement](), [engineering](), [executive](), [finserv](), [interest](), [production](), [security](), [snyk-platform](), [snyk-security-intel](), [supply-chain-security](), [tech](), [vulnerabilities](), [vulnerability-insights]() ### AI overview Anthropic's AI-driven vulnerability discovery systems can uncover software flaws at unprecedented scale, but increased capability also expands risk. The article argues that enterprise security must move from detection to control through deterministic validation, prioritization, remediation automation, and governance. ### Source excerpt AI can find vulnerabilities at scale, but enterprise security now depends on control, validation, and governance that can keep up. ## Building AI Security with Our Customers: 5 Lessons from Evo's Design Partner Program DevFeed: [Building AI Security with Our Customers: 5 Lessons from Evo's Design Partner Program]() Original publisher: [Read original article]() Author: Rudy Lai Published: 2026-04-01T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Securing AI](), [Generative AI](), [shadow AI](), [ai security](), [Security](), [Automation]() Tags: [agentic](), [agents](), [ai](), [ai-adoption](), [ai-security](), [application-security](), [aspm](), [automation](), [awareness](), [blog](), [ci-cd](), [compliance](), [customer](), [customer-featured](), [developer](), [devops](), [executive](), [finserv](), [generative](), [generative-ai](), [interest](), [pmm](), [policy](), [retail](), [scale](), [security](), [shadow-ai](), [snyk-apprisk](), [snyk-platform](), [tech]() ### AI overview Snyk shares five lessons from its Evo design partner program for securing generative AI. The article emphasizes discovering AI sprawl and shadow AI, understanding custom AI deployments, replacing static spreadsheets, enforcing governance policies, and using actionable risk intelligence to move AI from chaos to controlled production. ### Source excerpt Learn 5 key lessons from Snyk's Evo design partner program. Discover how AI discovery, risk intelligence, and policy automation help teams secure generative AI and govern AI sprawl at scale. [Next page]()