AI Security from Code to Runtime: Harness Secures AI Apps

Securing AI and Securing With AI: AI Security from Code to Runtime With Harness | Harness Blog

\ \ Renny Shen\ \ All this author’s posts](/content/authors/renny-shen/index.html)

\ \ Rahul Sood\ \ All this author’s posts](/content/authors/rahul-sood/index.html)

AI is changing both what you build and how you build it - at the same time. Today, Harness is announcing two new products to secure both: AI Security, a new product to discover, test, and protect AI running in your applications, and Secure AI Coding, a new capability of Harness SAST that secures the code your AI tools are writing. Together, they further extend Harness's DevSecOps platform into the age of AI, covering the full lifecycle from the first line of AI-generated code to the models running in production.

In November, Harness published our State of AI-Native Application Security report, a survey of hundreds of security and engineering leaders on how AI-native applications are changing your threat surface. The findings were stark: 61% of new applications are now AI-powered, yet most organizations lack the tools to discover what AI models and agents exist in their environments, test them for vulnerabilities unique to AI, or protect them at runtime. The attack surface has expanded dramatically — but the tools to defend it haven't kept up.

The picture is equally concerning on the development side. Our State of AI in Software Engineering report found that 63% of organizations are already using AI coding assistants - tools like Claude Code, Cursor, and Windsurf - to write code faster. But faster isn't safer. AI-generated code has the same vulnerabilities as human-written code, but now with larger and more frequent commits. AppSec programs that were already stretched thin are now breaking under the volume and velocity.

The result is a blind spot on both sides of the AI equation - what you're building, and what you're building with. Today, Harness is closing that gap.

What Makes Harness Different?

Most security vendors are stuck in their lane. Shift-left tools catch vulnerabilities in code before they reach production. Runtime protection tools block attacks after applications are deployed. And the two rarely talk to each other.

Harness was built on a different premise: real DevSecOps means connecting every stage of the software delivery lifecycle, and closing the loop between what you find in production and what you fix in code.

That's what the Harness platform does today. Application Security Testing brings SAST and SCA directly into the development workflow, surfacing vulnerabilities where they're faster and cheaper to fix. SCS ensures the integrity of artifacts from build to deploy, while STO provides a unified view of security posture — along with policy and governance — across the entire organization.

As code ships to production, Web Application & API Protection monitors and defends applications and APIs in real time, detecting and blocking attacks as they happen. And critically, findings from runtime don't disappear into a security team's backlog — they flow back to developers to address root causes before the next release.

The result is a closed loop: find it in code, protect it in production, fix it fast. All on a single, unified platform.

Today, we're extending that loop into AI - on both sides. AI is reshaping what you build and how you build it simultaneously. A platform that can only address one side of that equation leaves you exposed on the other. Harness closes both gaps.

Introducing AI Security

In the State of AI-Native Application Security, 66% of respondents said they are flying blind when it comes to securing AI-native apps. 72% call shadow AI a gaping chasm in their security posture. 63% believe AI-native applications are more vulnerable than traditional IT applications. They’re right to be concerned.

Harness AI Security is built on the foundation of our API security platform. Every LLM call, every MCP server, every AI agent communicating with an external service does so via APIs. Your AI attack surface isn't separate from your API attack surface; it's an expansion of it. AI threats introduce new vectors like prompt injection, model manipulation, and data poisoning on top of the API vulnerabilities your teams already contend with. There is no AI security without API security.

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With the launch of AI Security, we are introducing AI Discovery in General Availability (GA). AI security starts where API security starts: discovery. You can't assess or mitigate risk from AI components you don't know exist. Harness already continuously monitors your environment for new API endpoints the moment they're deployed. Recognizing LLMs, MCP servers, AI agents, and third-party GenAI services like OpenAI and Anthropic is a natural extension of that. AI Discovery automatically inventories your entire AI attack surface in real time, including calls to external GenAI services that could expose sensitive data, and surfaces runtime risks, such as unauthenticated APIs calling LLMs, weak encryption, or regulated data flowing to external models.

Beyond discovering and inventorying your AI application components, we are also introducing AI Testing and AI Firewall in Beta, extending AI Security across the full discover-test-protect lifecycle.

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AI Testing actively probes your LLMs, agents, and AI-powered APIs for vulnerabilities unique to AI-native applications, including prompt injection, jailbreaks, model manipulation, data leakage, and more. These aren't vulnerabilities that a traditional DAST tool is designed to find. AI Testing was purpose-built for AI threats, continuously validating that your models and the APIs that expose them behave safely under adversarial conditions. It integrates directly into your existing CI/ CD pipelines, so AI-specific security testing becomes part of every release — not a one-time audit.

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AI Firewall actively protects your AI applications from AI-specific threats, such as the OWASP Top 10 for LLM Applications. It inspects and filters LLM inputs and outputs in real time, blocking prompt injection attempts, preventing sensitive data exfiltration, and enforcing behavioral guardrails on your models and agents before an attack can succeed. Unlike traditional WAF rules that require manual tuning for every new threat pattern, AI Firewall understands AI-native attack vectors natively, adapting to the evolving tactics attackers use against generative AI.

Harness AI Security with AI Discovery is now available in GA, while AI Testing and AI Firewall are available in Beta.

Introducing Secure AI Coding

"As AI-assisted development becomes standard practice, the security implications of AI-generated code are becoming a material blind spot for enterprises. IDC research indicates developers accept nearly 40% of AI-generated code without revision, which can allow insecure patterns to propagate as organizations increase code output faster than they expand validation and governance, widening the gap between development velocity and application risk."

— Katie Norton, Research Manager, DevSecOps, IDC

AI Security addresses the risks inside your AI-native applications. Secure AI Coding addresses a different problem: the vulnerabilities your AI tools are introducing into your codebase.

Developers are generating more code than ever, and shipping it faster than ever. AI coding assistants now contribute to the majority of new code at many organizations — and nearly half (48%) of security and engineering leaders are concerned about the vulnerabilities that come with it. AI-generated code arrives in larger commits, at higher frequency, and often with less review than human-written code would receive.

SAST tools catch vulnerabilities at the PR stage — but by then, AI-generated code has already been written, reviewed, and often partially shipped. Harness SAST's new Secure AI Coding capability moves the security check earlier to the moment of generation, integrating directly with AI coding tools like Cursor, Windsurf, and Claude Code to scan code as it appears in the IDE. Developers never leave their workflow. They see a vulnerability warning inline, alongside a prompt to send the flagged code back to the agent for remediation — all without switching tools or even needing to trigger a manual scan.

"Security shouldn't be an afterthought when using AI dev tools. Our collaboration with Harness kicks off vulnerability detection directly in the developer workflow, so all generated code is screened from the start." — Jeff Wang, CEO, Windsurf

What sets Secure AI Coding apart from simpler linting tools is what happens beneath the surface. Rather than pattern-matching the AI-generated code in isolation, it leverages Harness's Code Property Graph (CPG) to trace how data flows through the entire application - before, through, and after the AI-generated code in question. That means Secure AI Coding can surface complex vulnerabilities like injection flaws and insecure data handling that only become visible in the context of the broader codebase. The result is security that understands your application - not just the last thing an AI assistant wrote.

We Had the Same Problem

When we deployed AI across our own platform, our AI ecosystem grew faster than our visibility into it. We needed a way to track every API call, identify sensitive data exposure, and monitor calls to external vendors — including OpenAI, Vertex AI, and Anthropic — without slowing down our engineering teams.

Deploying AI Security turned that black box into a transparent, manageable environment. Some milestones from our last 90 days:

We now track 111 AI assets and monitor over 4.76 million monthly API calls, giving our security team a granular, real-time map of our entire AI attack surface.
We now run 2,500 AI testing scans a week and have remediated 92% of the issues found, including critical weak authentication and encryption gaps in MCP tools.
We identified and blocked 1,140 unique threat actors attempting more than 14,900 attacks against our AI infrastructure.

The shift wasn't just operational — it was cultural. We moved from reactive monitoring to proactive defense. As our team put it: "Securing AI is foundational for us. Because our own product runs on AI, it must be resilient and secure. We use our own AI Security tools to ensure that every innovation we ship is backed by the highest security standards."

Ready to Secure Your AI?

AI is moving fast. Your attack surface is expanding in two directions at once - inside the applications you're building, and inside the code your teams are generating to build them.

Harness AI Security and Secure AI Coding are available now. Whether you're trying to get visibility into the AI running in your environment, test it for vulnerabilities before attackers do, or stop insecure AI-generated code from reaching production, Harness’ platform is ready.

Talk to your account team about AI Security. Get a live walkthrough of AI Discovery, AI Testing, and AI Firewall, and see how your AI attack surface maps against your existing API security posture.

Already a Harness CI customer? Start a free trial of Harness SAST - including Secure AI Coding. Connect it to your AI coding assistant, and see what's shipping in your AI-generated code today.

\ \ Renny Shen\ \ All this author’s posts](/content/authors/renny-shen/index.html)

Renny Shen is a 25-year veteran of the technology industry and has deep experience both building and marketing a broad range of products and solutions.

\ \ Rahul Sood\ \ All this author’s posts](/content/authors/rahul-sood/index.html)

Rahul Sood is the General Manager for Application Security at Harness, where he leads the company's AppSec portfolio. He is focused on building security for the AI era, integrated directly into modern DevOps workflows. Before joining Harness, Rahul was Chief Product Officer at Pindrop.

Securing AI and Securing With AI: AI Security from Code to Runtime With Harness | Harness Blog

What Makes Harness Different?

Introducing AI Security

Introducing Secure AI Coding

We Had the Same Problem

Ready to Secure Your AI?

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