AWS Continuum integrates with OpenAI Codex and Anthropic Claude Code in major AI security push

Amazon Web Services is threading its AI-powered security infrastructure directly into the coding environments built by two of its fiercest rivals - and in doing so, it is making a bold bet that controlling the security layer matters more than controlling the model. AWS announced at Black Hat USA 2026 this month that its Continuum platform for code vulnerabilities will integrate directly into Anthropic's Claude Code and OpenAI's Codex, alongside AWS's own Kiro IDE. The move embeds AWS security tooling at the point where developers write code, regardless of which AI model they use to do it. Simultaneously, AWS expanded Security Hub Extended - its curated, single-bill security marketplace launched in February - with a 10th security category focused on supply chain protection, bringing in Chainguard and Socket as partners. Together, the announcements are AWS's most sweeping attempt yet to position itself as the default security control plane for enterprise software development in the AI era - a role that carries enormous commercial implications as the global cloud infrastructure market surpasses $143 billion per quarter, according to Synergy Research Group. Why frontier AI models turned the vulnerability backlog into a five-alarm fire The urgency behind both launches traces back to a single inflection point that reshaped enterprise security earlier this year. Claude Mythos Preview, announced by Anthropic in April, is a general-purpose AI model that during testing revealed striking cybersecurity capabilities far exceeding any prior system. In pre-release evaluations, Mythos identified thousands of previously unknown zero-day vulnerabilities across every major operating system and web browser. More than 99% of those vulnerabilities remain unpatched by their maintainers, and the median time from vulnerability discovery to weaponized exploit - already collapsed from 771 days in 2018 to under four hours by 2024 - is projected to reach under one hour by the end of 2026. Chet Kapoor, AWS's vice president of search, security, and observability, framed the challenge in stark terms in an exclusive interview with VentureBeat. "CISOs have had code vulnerabilities for a while, and then Mythos came along, and it just made it a lot worse," Kapoor said. "They already had a backlog. Now the backlog is 5x more, and that causes a problem." That problem - the exponential growth in known vulnerabilities outpacing any organization's ability to triage and fix them - is precisely what Continuum is designed to address. Kapoor described AWS's broader security vision as a shift from "telemetry, storage, query, dashboards for humans to telemetry, context, reasoning, and actions by agents." The shorthand for that vision is a phrase AWS repeated throughout Black Hat: autonomous security at machine speed. Inside Continuum's four-phase system for finding and fixing code flaws automatically Continuum operates as what AWS calls an “agent-team loop architecture” - a sophisticated orchestration harness that selects the right AI model for each task, connects to a customer's environment, and delivers validated secure code. Under the hood, it runs through four distinct phases. Kapoor broke them down for VentureBeat. Discovery uses multiple frontier AI models to scan code and ingest a customer's existing vulnerability backlog. Prioritization - which Kapoor called "one of our biggest value adds" - contextualizes each finding against a customer's actual environment and business risk. "You go from 100 to 2,000, and now you're like, whoa, I didn't even know which 100 to focus on," he said. Validation then builds reproducible exploits in an isolated sandbox to confirm whether a vulnerability is genuinely exploitable. "Once I do them, how will it behave?" Kapoor explained. "You create a sandbox to go off and make that happen. So you can figure out what the blast radius is." The validation phase covers both first-party code that customers wrote themselves and third-party open source code they depend on. Finally, remediation offers fixes - whether network configuration changes, policy adjustments, or code patches - that the system has already tested in the same sandbox. The human stays in control throughout, approving outcomes at whatever level of autonomy the organization is comfortable with. The commercial model is equally deliberate. Customers pay AWS a single price for Continuum. AWS absorbs the underlying token costs for whichever frontier model performs best at each phase of the scan. "The customer purchases Continuum, period," Kapoor told VentureBeat. "We optimize on which model to use for what because, quite frankly, GPT Cyber is good at some things, Mythos is good at some things." How AWS convinced OpenAI and Anthropic to open their coding tools to a rival's security layer The most strategically striking element of the announcement is the integration with OpenAI Codex and Anthropic Claude Code. AWS competes directly with both companies across cloud AI services. Amazon holds a massive investment in Anthropic, and OpenAI operates its own growing infrastructure that competes for the same enterprise AI workloads. Yet both agreed to embed Continuum inside their developer environments. When VentureBeat asked Kapoor directly about the competitive dynamics, he pushed back on the framing entirely. "Who is the competitor?" Kapoor said. "I can keep thinking about Anthropic and OpenAI to be partners. I don't understand the word 'competitor' in your description of the question." He added: "They're partners with us. We use their models. We plug into their environments. Which is why we actually brought them together to do this." Kapoor argued that working with a single model provider would be insufficient. "I don't think it's good enough to just do it with one company," he said. "Everybody is going to leapfrog each other over a period of time." By absorbing token costs and presenting a single bill to the customer, AWS positions Continuum as infrastructure - not a model wrapper. The harness, not the engine, becomes the durable competitive asset. As Kapoor wrote in his blog post announcing the partnership: "An AI harness is the orchestration layer that wraps around a model to connect it to tools, guardrails, memory, and workflows, so it delivers outcomes. Think of the model as the engine and the harness as everything around it. You need both to have a high-performance car." AWS partners echoed the logic. "Model choice was never the hard part for enterprises. Trust in what the model does in production is," said Val Henderson, CEO of AWS Premier Partner Caylent, in comments reported by CRN. AWS adds supply chain security to its curated marketplace as open source threats intensify The second prong of AWS's Black Hat announcements extends Security Hub Extended into supply chain security as its 10th category, with Chainguard and Socket as curated partners. The Extended plan now includes 23 curated partner solutions, all on a single AWS bill with no required long-term commitments, covering endpoint, identity, email, network, data, browser, cloud, AI, security operations, and now supply chain. Michael Fuller, AWS's director of security services, told VentureBeat that the addition was driven entirely by customer demand. "Over the last six to eight months, it's gotten quite a bit of news around what's happening in the supply chain space, with the fact that everybody builds on open source," Fuller said. "Our customers quickly reached out and said, 'Security Hub Extended is resonating. We would love to see a supply chain security category with some key players there because it's a hot topic for us.'" The two partners were chosen to be complementary rather than duplicative. Chainguard focuses on providing hardened, secure-by-default container images and packages rebuilt from verified source code. Socket performs behavioral monitoring of packages as they are pulled into a developer's environment, detecting threats like typosquatting, maintainer account takeover, and obfuscated malicious code. "Together, between the three of us - us with consolidating that, ChainGuard providing really good hardened and cleaned images and packages, and then Socket providing a behavioral analysis over the top - gives customers a really good holistic supply chain security offering," Fuller said. The complementary approach addresses two distinct attack vectors. An attacker can publish a malicious package that contains no known vulnerabilities - Chainguard's clean-build approach defends against that. Separately, an attacker can compromise a legitimate maintainer's account and push a tainted update to a trusted package - Socket's behavioral detection catches that. Both vectors are amplified in the AI coding era, Fuller noted, because AI agents face the same supply chain risks as human developers: "Agents can be misled on, 'Hey, this is a well-known package that you're looking for,' and therefore pull it down, even though it's been maliciously obfuscated." Why AWS chose two partners per category instead of building a security marketplace The partner selection strategy behind Security Hub Extended reveals a deliberate philosophy that distinguishes it from the AWS Marketplace, which already hosts tens of thousands of security offerings. Fuller told VentureBeat that customers articulated clear principles for what they wanted. "One was don't give me hundreds of offerings. We already have the AWS Marketplace," he said. "Two was give me a sweet spot. Our customers were saying, give me two in each category, and when you look at those two, don't give me head-to-head competitors. Give me one that I may know well, that is an established player, and give me one that's taking a different approach." Fuller pointed to the security operations category as the template. "You have Splunk, hard to argue not an established leader in security operations, and then you have Seven AI that's kind of taking a very different approa