Anthropic secures its AI-native software development lifecycle

How Anthropic secures its AI-native software development lifecycle

Anthropic Deputy CISO, Jason Clinton, details how the Security Engineering team secures a SDLC that has AI authoring 80% of merged code.

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Claude Code

Enterprise AI

Agents

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Claude Code

Claude Tag

Claude Enterprise

Date

July 21, 2026

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https://claude.com/blog/how-anthropic-secures-its-ai-native-software-development-lifecycle

At Anthropic, the amount of code and velocity of deployment have scaled exponentially. Our software engineers on average ship 8x as much code per quarter as they did from 2021 to 2025.

Our reviews, monitoring, and other security processes needed to scale alongside this increased pace. Otherwise it becomes a formula for bottlenecks (Amdahl’s Law).

Our software development processes have changed drastically as well. Claude has evolved from coding assistant to primary creator and reviewer. Claude authors about 80% of the code merged into our codebase today.

More than half of all code is being merged by our internal version of Claude Tag while human engineers focus on directing, setting intent, and owning final approval.

This means our security team must defend a rapidly expanding surface area and harden a lifecycle with non-deterministic, constantly evolving agents at its heart. In this article, I cover strategies to secure the software development lifecycle (SDLC).

(This is intended to be combined with the Zero Trust for Agents framework we recently published; everything in this article uses security design ideas from that framework in the implementation).

The threats we're designing against are specific: a compromised or prompt-injected agent introducing a malicious change; supply-chain and dependency poisoning that an agent ingests as trusted input; and the more familiar classes of application vulnerability now arriving at higher volume. Every control that follows maps to at least one of those.

There are several overarching strategies we’ve deployed to accomplish this without significantly throttling dev velocity including:

Shifting security left and fully integrating with the code development stage;

Using hard access and identity boundaries to contain the blast radius;

Combining automated deterministic and agentic reviews before and after production; and

Inserting humans in the loop at the highest leveraged points.

In this article, we’ll cover the security processes we have implemented at specific stages of the software development lifecycle as well as the core principles behind them. These principles are more enduring as security teams must reexamine, and often reinvent, their processes as model capabilities evolve.

The evolving software development lifecycle

Our development team has covered the changes to their software development lifecycle at length, so this will be a brief primer before we dive into each stage.

At a high level, our software development lifecycle is compressed. It is driven by prototypes and internal adoption (dogfooding) more than lengthy planning cycles. Ideation comes from all corners of the organization and traditional roles (frontend, backend, design) are blurred. Reviews and approvals still have humans in the loop, but are also driven by agentic loops.

While each stage has been fundamentally transformed and accelerated by Claude Code and Claude Tag, the names and purposes of each stage wouldn’t look alien to a developer coming from a more traditional organization. These are natural gates that we also use as part of our security processes for an AI-native SDLC.

Plan

One of our first security automations ever was a simple Claude Opus powered PSR (project security review) web application. It ingested a project design document and analyzed it against the MITRE ATT&CK framework to identify potential vulnerabilities and suggested mitigations.

We’ve significantly enhanced the system by connecting it to an internal knowledge index that provides much deeper context across our organization-wide policies, past decisions, and related systems.

The process internally at Anthropic for an automated PSR.

This gives us a better understanding of potential risk, and it also captures information missing from the PSR. This one implementation saved the majority of the AppSec team’s time. Once we gained confidence that Claude was accurate in assessing risk, we allowed teams to approve their own project, if Claude deemed the launch low enough risk.

Here we can see one of the first key adaptations to an AI-native SDLC. A PSR was originally designed to catch security issues before the lengthy and expensive coding process. Catching an issue at this stage saved months of re-development.

Today, multiple prototypes of major features can be created in hours, making detailed architectural review a less critical gate. Connecting our PSR application to our knowledge index captures context that could otherwise be missed without creating an unnecessary speed bump. Creating a Claude Code skill allowed Claude to further fan out and capture additional context wherever it lived.

Enduring Principle: Connect security agents to organizational context. As the planning cycle compresses, it is much more effective to bring these agents to where the context already lives – chat threads, prior reviews, the codebase – rather than forcing detailed documentation at stages that may no longer require them. Either way, agents need context outside of the code itself.

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Code

Security professionals within an AI-native engineering organization have a new lever: they can directly shape how code is created, helping to prevent vulnerabilities at the source.

Previously, teams observed recurring vulnerabilities and created secure coding guidelines to address them, but those guidelines were difficult to enforce and rarely standardized.

At Anthropic, those guidelines are encoded in CLAUDE.md files and references to org-wide skills so the code follows these best practices the minute it's generated. This is done as part of a closed loop. Once an agent discovers a bug class, the relevant file is updated to prevent it recurring in future code.

Of course, that doesn’t mean all code comes out perfect. Our team started with a CLAUDE.md file that instructs the agent to run /security-review as a final step before opening a PR. This generally available command, the productized version of our team's internal review workflow, looks for places where potential attacker-controllable input enters, scans for suspicious links, and then verifies its findings.

Today, these reviews take place while Claude generates the code. Once a security guidance plugin is installed, Claude reviews the conversation and code as it goes. It suggests security improvements and addresses common vulnerabilities in the same session as it generates the code.

Other nudges at PR-time push internal, non-technical teams towards hosting their app on our low-code app-hosting platform, avoiding shadow IT that had traditionally plagued security teams.

Some of our customers choose to integrate /security-review with a PreToolUse hook, which makes this step a harder gate. That is also effective, but our team has chosen to incorporate our hard code review gate at the test/CI stage of the cycle.

In addition to shaping and reviewing code, containing the blast radius is one of our primary concerns at this stage. We do this by setting hard boundaries around identity (more on that in the monitor section) and setting our devs up to code on virtual machines.

Moving our coding to remote VMs was a relatively painless shift and gave us increased control and visibility compared to laptops alone. Agent traffic on these VMs is egress-allowlisted.

These tight egress controls matter especially when the agent is reading untrusted input which can carry a prompt-injection payload. An injected instruction can’t reach arbitrary destinations on the internet: exfiltration paths are limited to a small set of monitored services.

Here again you can see a clear adaptation for an AI-native SDLC. Remote coding was previously used mainly to contain IP, and today we’re seeing more mature AI coding teams adopt these environments as a means to contain agents.

Enduring Principle: Shifting left in an AI-native engineering organization means closing the loop between vulnerability discovery and updating instructions to customize how Claude generates code. Limit the blast radius (Principle of Least Agency) and what an agent can access with hard boundaries as appropriate.

Test (CI)

In my experience, the test or CI stage quickly becomes the most painful bottleneck for engineering teams in the midst of an AI-native transformation. At Anthropic, once most developers were using agentic coding tools and running multiple agents at one time, it quickly became obvious the team could only move as quickly as humans could review code.

Let’s be clear: human accountability is still central to our process. What we did was accelerate the review process by combining automated agentic and deterministic reviews, while reserving human review for regulated or truly critical code.

Historically, human code review has been held as the standard, yet the empirical evidence has shown it is not perfect. Security bugs regularly ship in software across the world. Our review process is able to review more code and catch particularly complex issues, helping to reduce these risks.

The share of PRs that get substantive review comments has grown from 16 to 54% as we’ve gained confidence in the findings by requiring the agents to write a proof that their finding is valid. We’ve also determined that approximately a third of the bugs behind past claude.ai incidents would have been caught by the automated processes we have now implemented.

We’re not the only organization that has found this to be true. Intercom has shared it auto-approves 19% of its PRs. Deployment doubled while downtime from breaking code changes dropped 35%. CircleCI reached a similar conclusion building Chunk, an autonomous agent on Claude that resolves CI/CD maintenance issues and validates its own fixes before a human ever sees them. The approach doubled the rate at which agent tasks convert into completed pull requests.

When a PR is opened at Anthropic, multiple agents automatically review it. Each review agent is designed and scoped to a specific, narrow focus and leverages RAG for additional context and memory surrounding past incidents.

This is much more effective than one mega-prompt or super security agent for a few reasons:

They do not share biases and blindspots

If one is compromised or makes a mistake, it can be caught by other reviewers

Effort isn’t spread too thinly across multiple focus areas

To be clear, agents aren't merging code to production unchecked. We tier our codebase by risk, and make deliberate decisions on what parts to automate. Entire codebases have strict human approval processes.

Human accountability is still central for code that is reviewed and merged by Claude.  Every approval is logged with the signals and reasoning behind it, and a risk-weighted sample is reviewed by humans. Another round of testing focuses on invariants like “user A can never read user B’s data,” and triggers additional manual reviews.We combine our agentic scans with SAST tools as well, which post directly on PRs.

Most scanning approaches, whether agentic or deterministic, are consumption based. Costs will increase as code throughput increases, and teams will need to decide what level of coverage is appropriate for them.

At Anthropic, we accept costs here will grow as our code velocity increases, but anticipate unit cost will fall. Models today are much better at coding than all models

[truncated for AI cost control]