AI coding assistants like GitHub Copilot, Cursor, and Gemini Code Assist are fundamentally changing how software is built. Already by the end of 2024, 30% of code was AI-generated. By 2028, Gartner projects that 75% of enterprise developers will be using AI assistants as part of their daily workflow.

This shift has unlocked significant gains in development speed and productivity, but it’s also created an urgent problem for CISOs: 50% of AI-generated code contains vulnerabilities, with 1 in 10 of them actively exploitable with real business impact. Application security backlogs are exploding while team sizes stay flat, creating an impossible mandate to support ever-faster AI-powered development while ensuring security and compliance don’t get left behind.

We believe that the only way to close this gap and govern AI-generated code is to automate traditionally manual application security controls, like security reviews or threat modelling, and embed them as part of the design and development process. 

Apiiro’s AutoFix Agent achieves this vision by bringing real-time security guidance and automated remediation directly into the developer experience. Unlike AI code assistants that operate without organizational context, our agent runs on unique data about your software architecture, from code to runtime, that other platforms simply don’t have. The result is a force-multiplier for your AppSec team in supporting developer velocity and business growth – while preventing incidents at scale.

Built on Unique, Critical Data, Embedded in Developer Workflows

Generic ‘code-only’ autofix tools often fall short because they operate without a deep understanding of your specific software architecture from code to runtime, custom security policies, or risk acceptance criteria. Most rely on static rules, public training data, or generic  frameworks like the OWASP Top 10 and CVSS – which, while helpful as a baseline, aren’t enough for making precise, context-aware remediation decisions at enterprise scale.

These tools lack visibility into runtime context, existing compensating controls, or policy exemptions, and as a result, they frequently overcorrect: recommending fixes that break application logic, violate internal security standards, and  introducing new vulnerabilities and types of risks. Not least of all, they can trigger unnecessary noise and rework for developers, without reducing business risk.

Apiiro’s AutoFix Agent, in contrast, is powered by unique, critical data that no other platform generates – on your software architecture, runtime environment, developer behavior, and organizational security policies. This is driven by:

• Deep Code Analysis (DCA): Automatically discovers, inventories, and visualizes your evolving software architecture (APIs, code modules, OSS components, PII/PCI data models, AI models, and more).
• Code-to-Runtime (C2R) Matching: Automatically maps runtime components and findings directly to code repositories and owners, reducing remediation time while using runtime context to prioritize risks before releasing software.
• Risk Graph Policy and Workflow Engines: Correlates, deduplicates, and contextualizes findings from 1st and 3rd-party tools (SAST, SCA, Secrets, Containers, etc.) using the your actual software architecture, security policies and standards, code blast radius, toxic combinations, runtime context, and business impact. This enables true risk-based prioritization (not just CVSS, EPSS) and triggers threat modeling or penetration testing workflows based on risky material code changes.

And crucially, this context is delivered where developers and application security engineers work. AutoFix Agent integrates natively through Apiiro’s MCP server with tools like VS Code, Cursor, and Gemini Code Assist. They can ask questions, get guidance, and automate the fixes directly in their IDE, with responses automatically tailored to their environment.

Designed to Answer Real Questions and Reduce Actual Risks

Apiiro’s AutoFix Agent is designed around how AppSec teams actually think about risk: through targeted questions that require deep organizational context.

Questions like:

• “Is this repository deployed in production?”
• “Does this API expose PII or payment data?”
• “Which components are using ungoverned GenAI libraries?”
• “If I have 30 min, what should I prioritize fixing first? And why?”

Each query triggers a response grounded in your actual environment, security policies, and runtime context. When a developer asks about a risk, the agent factors in deployment status and location, existing compensating controls, business impact, and whether the affected asset is internet-facing. This enables the agent to recommend the right action: AutoFix the risk, and generate risk acceptance with evidence for auditors.

Unlike AI code assistants that suggest fixes based on generic patterns, Apiiro weighs reachability, exploitability, business impact, and existing security controls. The agent may decide not to fix a vulnerability and can automatically provide contextual evidence for that decision aligned with your risk acceptance policies.

Let’s walk through a real-world scenario where Apiiro’s AutoFix Agent detects, evaluates, and fixes a critical design flaw – all from within the developer’s workflow.

Step 1: Threat Modeling Before a Single Line of Code Is Written

A developer opens a ticket to implement a new feature: an API endpoint for handling user-submitted content. Before any code is written, Apiiro’s AutoFix Agent proactively runs a STRIDE-based threat model on the ticket. Drawing from the ticketing system, CMDB, runtime context, and organizational security policies, it generates contextual security questions and identifies tampering as a key threat.

Rather than flagging this abstractly, the agent analyzes the developer’s environment and recommends a specific mitigation: input validation logic in C#, tailored for Google Auth in .NET – the exact tech stack used in the codebase. This ensures any mitigations align with internal policies and existing controls.

Step 2: Contextual Analysis of a Runtime Alert

Later, a runtime security tool (e.g., Akamai) reports suspicious behavior tied to the newly implemented API. Apiiro’s AutoFix Agent springs into action.

Behind the scenes, it correlates the alert to the correct code repository and matches it with the relevant endpoint – determining that the API lacks input validation and directly accesses sensitive customer data.

This repository is already tagged in the CMDB as a PCI-regulated application and classified as high business impact. That context raises the criticality of the finding.

But here’s the nuance: the agent detects an input validation control already implemented upstream at the API gateway and aligned with internal security policies. This mitigates exploitability at runtime.

To give the developer confidence, the AutoFix Agent surfaces a real-time software architecture graph, visually confirming that no downstream components are at risk and there’s no blast radius or toxic data combination. The result? The developer sees that nothing needs to be fixed and moves on without wasting time.

Step 3: Fixing a Confirmed Vulnerability, Automatically

Now consider a different finding from a SAST tool, which reports an insecure deserialization vulnerability in a separate service.

Apiiro’s AutoFix Agent evaluates the issue with Deep Code Analysis (DCA) and runtime context. It determines that:

• The code belongs to a high business impact application
• The vulnerability is reachable and exploitable
• No mitigating controls are present

Unlike generic autofix tools that might suggest a boilerplate fix, Apiiro’s agent delivers a policy-compliant fix tailored to the application’s architecture, coding standards, and compensating controls. The AutoFix is then surfaced directly in the developer’s IDE with context, justification, and auditability so it’s easy to review and merge.

Step 4: Managing Risk Post-Deployment

Even after release, when a runtime scanner identifies a potential issue, AutoFix Agent continues to operate. It automatically maps the finding to the relevant repository and code owner. If remediation is needed, it orchestrates a fix aligned with internal SLAs, risk appetite, and policy – ensuring risks are resolved in seconds, not weeks.

AutoFix, AutoGovern, AutoManage – Seamlessly Integrated

Apiiro’s AppSec agent delivers three core capabilities seamlessly integrated with the IDE:

• AutoFix: Automatically assess and fix design and code risks with runtime context. Fixes are delivered as actionable code snippets tailored to your environment, applicable with a single click in supported IDEs.
• AutoGovern: Enforce your security policies, standards, and secure coding guardrails automatically, preventing policy violations before code ships.
• AutoManage: Automated risk lifecycle management and measurement across the SDLC, with built-in SLA tracking and evidence collection.

Crucially, the agent doesn’t act blindly. It weighs each issue against runtime exposure, policy requirements, and architectural context. It then explains why a finding matters, what’s impacted, and how the proposed fix aligns with your internal standards. Developers maintain full control and visibility into each suggestion before implementation.

Enterprise-Grade Security and Privacy

Every interaction with the AutoFix Agent is designed with enterprise security in mind:

• Complete customer data separation: Zero data leakage between customer environments
• Secure AI endpoints: Powered by dedicated Gemini endpoints used exclusively by Apiiro
• AppSec-focused scope: Conversations are limited to application security topics and your organizational context
• Audit trail integration: All decisions and remediations are automatically documented and mapped to ownership for compliance review

Part of the Broader Agentic Application Security Platform

The AutoFix Agent operates as an integrated component of Apiiro’s Agentic Application Security Platform. Through our MCP server, the agent is available wherever developers work (VS Code, Cursor, OpenAI Chat) with consistent intelligence and security posture awareness across all interfaces.

Behind the scenes, every interaction taps into Apiiro’s unique and critical data on your software architecture, ensuring that whether triggered from your IDE or Apiiro’s platform, you get the same context-aware guidance designed to accelerate secure development at scale. Every response contributes to real-time risk posture tracking, SLA reporting, and evidence collection without requiring developers or AppSec teams to manually log actions or updates.

Wrapping Up

CISOs today are expected to be enablers of AI-powered development velocity while simultaneously maintaining strict security, compliance, and risk governance. Traditional approaches like manual reviews, security gates, and contextless scanners simply cannot be the solution.

Apiiro’s AutoFix Agent represents a new paradigm: proactive, automated application security that works at the speed of AI-generated code. By bringing deep software intelligence directly into developer workflows, we’re enabling AppSec teams to clear their backlogs while governing AI-generated code and preventing incidents at scale.

Ready to see AutoFix Agent in action? Learn more and see the demo.