Introducing Apiiro Guardian Agent: Preventing Vulnerable and Non-Compliant Code from Ever Being Created

The Era of Prevention

Apiiro started by building our foundational pillars for delivering secure, compliant code – Deep Code Analysis (DCA) and the Software and Risk Graphs™. These enabled us to move security processes from reactive to proactive. From intermittent to continuous. With this Apiiro Data Fabric in place, we went AI-native; embedding AI in our core technology and user experience. 

We launched the Apiiro AutoFix Agent in Oct 2025. As we worked alongside our design partners we validated our core thesis: AI needs a map. Context is the critical ingredient for effective application security. We applied the power of the Apiiro Data Fabric – linking code architecture, data flows, and runtime mappings – to provide accurate, effective fixes for real world production risks. 

But as AI-driven development accelerates, we came to a realization: remediation alone is no longer sufficient. AI-assisted coding and vibe coding are driving an unprecedented expansion of the attack surface, rendering reactive scanning and fixing loops obsolete. 

To stay ahead, application security must evolve further – from Detection to Prevention.

The Problem: Speed is the New Risk

AI coding agents like GitHub Copilot and Cursor have boosted the speed and efficiency of software delivery. However, this speed comes with a tradeoff.

The current cycle of application security is fundamentally reactive: Code is written → code is scanned for risks and vulnerabilities→ code is (auto-)fixed. This creates a “backlog debt” that grows faster than any team can manage. Furthermore, existing tools lack critical context when suggesting fixes – they can read code, but they can’t guess the intent of the developers. This leads to a flood of false positives and developer friction, and a big challenge: How to keep scaling development at the speed of AI, without sacrificing security.

Our answer: Secure-by-Design from the first keystroke. 

We must create secure, compliant code from the second it is written – before the first commit – ensuring it is free from vulnerabilities, and aligned with both general secure coding practices, and organization-specific risk policies.

Today, we are thrilled to announce our solution for AI-Native, 0-Vuln, Fully Compliant Code: Apiiro Guardian Agent.

From Detection to Prevention

Apiiro Guardian Agent is an AI AppSec agent acting as an always-on, Principal Application Security Engineer – operating 24/7 across the SDLC. With Guardian, we are adding – for the first time – a Prevention layer for application security: proactively and contextually preventing risk before AI generates the code. Guardian achieves 3 main objectives:

• Prevents AI coding agents from generating vulnerable and non-compliant code — Our patent-pending technology dynamically infuses developer prompts with security guidelines derived from your threat models, compliance policies, and actual software architecture. This ensures that AI coding assistants generate secure-by-default code that is compliant with your specific standards.
• Automatically fixes code risks — vulnerabilities and non-compliance for existing code
• Automates AppSec manual processes — threat models, security reviews, incident response and other application security todo lists that cannot scale with AI-driven development

Guardian Agent embeds security and compliance directly into how modern software is designed, developed, and delivered. 

The Secret Sauce: “Secure Prompts” Stop Risk at the Source

The centerpiece of Guardian Agent is the patent-pending Secure Prompt technology. While traditional tools scan code after it’s written, Secure Prompts guide AI to write secure code the first time.

The Anatomy of a Secure Prompt: The 3-Step Prevention Loop

1. Contextual grounding: As a developer prompts an AI assistant (like GitHub Copilot or Cursor), Guardian intercepts the request. It instantly cross-references the developer’s intent with the Apiiro Data Fabric (Software and Risk Graph™) to understand the application’s specific architecture, threats, and organization compliance guidelines.
2. Prompt infusion: When needed, Guardian dynamically “wraps” the developer’s prompt with specialized security instructions, organizational policies, and relevant code patterns – all while prioritizing the developer’s intent.
   The security guidelines are dynamic and contextual – fusing the actual intent, the application software architecture, threat modeling for the new feature, and the organization’s risk policies. Guardian “whispers” the correct security requirements to the AI model before it responds – automatically, continuously, transparently, on every (relevant) prompt. 
3. Secure generation and validation: The coding agent generates a response compliant with your security standards, eliminating the need for a “fix” later in the cycle. Guardian can be configured to validate the generated code, to doubly verify the output aligns to the guidelines. 

Secure Prompting in Action: Prevention vs. Rework

Scenario	Raw Developer Prompt (Without Guardian Agent)	Secure Prompt (With Guardian Agent)
New API endpoint	“Implement DELETE /risks/{id} API endpoint to delete a risk by its ID.”	“Implement DELETE /risks/{id} API endpoint to delete a risk by its ID. – Examine <filepath:line> where the api/risk/filters/searchLocation endpoint implements Authorization — follow this exact pattern. – Apply Microsoft AspNetCore Authentication .NET (configured in <filepath:line>) and Authorization to ensure proper access control. – Implement input validation for the {id} parameter as defined in <filepath:line> to prevent IDOR and injection attacks. – Since this repository handles PII data and is internet-exposed, ensure no sensitive data is logged or exposed.”
Data access to user store	“Write a function to fetch user data from the database based on the ID parameter.”	“Write a function to fetch user data from the database based on the ID parameter. – Use existing authentication and authorization patterns (Microsoft AspNetCore + WIF). – Ensure the session token is validated against the UserID. – Implement input validation for the ID parameter. – Use parameterized queries to prevent SQL injection. – Since the repository handles PII data, ensure GDPR-compliant data masking is applied.”
File upload feature	“Build a component to allow users to upload profile pictures to S3.”	“Build a component to allow users to upload images and store them in S3. – Validate file type, size (max 5MB), and sanitize filenames. – Apply least-privilege access and default Private ACL on the S3 bucket. – Scan uploads for malware where possible. – If images contain PII, encrypt them using HashiCorp Vault before storage.”
External provider integration	“Create DataDog API integration.”	“Create a DataDog integration using api_key and app_key headers. – Retrieve credentials securely from HashiCorp Vault — never hardcode. – Use the internal SecretManager class. – Apply outbound HTTP security hardening (TLS, timeouts, header stripping).”
Incremental UI functionality	“In pdf-service.ts, add a helper that captures a graph element as an image and saves it to PDF.”	“Add a helper in pdf-service.ts that captures a graph HTMLElement and saves it to PDF. – Sanitize the fileName parameter following existing validation patterns. – Use approved libraries: html2canvas (ver XX) and jsPDF (ver YY). – Ensure error messages do not expose sensitive system details.”

A Force Multiplier for Developers and AppSec

Guardian Agent is designed to leverage expertise accumulated through analysis of thousands of real-world scenarios from Fortune 1000 Apiiro customers. This allows the Agent to wield the deep knowledge and experience of a Principal Security Engineer, making it qualified to intervene on and secure prompts for all application security tasks across design and development: threat modeling, building secure and compliant code, detecting, triaging, and even fixing code risk.

By grounding the agent in the Apiiro Data Fabric – Software Graph and Risk Graph™ – Guardian Agent delivers specialized value across the entire development lifecycle:

For Developers: Secure-by-Default coding	For AppSec: Automated Governance at scale
Secure coding: Instead of fixing bugs after they are committed, Guardian Agent ensures security is baked in before a single line of code is generated, effectively closing the gap between AI productivity and organizational risk. It contextually rewrites prompts in real-time, to govern and infuse AI coding agents with contextualized security expertise and controls, tailored to your specific organization.	Policy enforcement at the source: Define security requirements and threat models once, and have Guardian automatically inject them into every AI-driven coding session.
Contextual awareness: No more generic security advice. Guardian understands the intent of the code within the specific software architecture, eliminating noisy false positives.	The “Scale” button: One AppSec engineer can now oversee thousands of AI-generated commits, with Guardian handling the first layer of triage and remediation.
Frictionless fix: When a risk is identified, Guardian doesn’t just flag it – it provides the “Why” and the “How-to-Fix,” tailored to the specific application context.	Instant Visibility: Query your entire estate for emerging threats (like the Shai-Hulud attack) and deploy automated fixes across the organization in seconds.

This isn’t just another layer of scanning. By shifting from reactive detection to proactive control, Guardian effectively closes the gap between productivity and risk. It empowers developers to move faster without looking over their shoulder, and it allows security teams to transition from manual ticket-pushing to high-level strategic governance.

Agent Architecture: Built on the Apiiro Data Fabric

Guardian Agent consolidates the essential AppSec pillars of ASPM, AST, SSCS, threat modeling, secure coding and more into a single agent. It has access to all the tools and capabilities required to perform these tasks – built in Apiiro, or integrated with any 3rd party security tool already in our customers’ environment.

In order to achieve ASPM success criteria effectively, Guardian relies on a robust data foundation – enabling it to reason and act autonomously, with confidence. The Apiiro Data Fabric supplies that foundation:

• Software Graph: An accurate, complete, reliable and continuously-generated context of the software architecture unique to every organization’s environment.
• Risk Graph: A continuous mapping of key extra-code signals that inform code how to operate – from development to runtime. Generated across the SDLC with code-to-runtime context.
• Organizational Memory: The repository of knowledge concerning a specific organization’s policies, standards, ownership protocols and processes. Supports organizational-level and user-level memories, allowing Guardian to learn, adapt, and attune its behavior with continued use.

Guardian serves multiple personas – AppSec teams, developers and pentesters. 

It is exposed via multiple parallel interfaces, meeting users where they are:

• An MCP server – for chat interface – as part of developers IDE/coding agents 
• An IDE plugin – for in-IDE code view
• A dedicated Web UX chat and visual experience within the Apiiro portal 

Real-World Impact: Guardian in Action

We worked closely with our design partners to test Guardian against the most complex security challenges. Here is how it reimagines the day-to-day work of engineering and security teams:

Secure Development

• The challenge: A developer prompts an AI to “Integrate Stripe for credit card transactions.” The AI generates standard code that might violate internal PII handling policies.
• The Guardian way: Guardian identifies the intent to handle sensitive data. It infuses the prompt with the organization’s specific architecture (e.g., “Use our Spring Boot framework”) and specific policies (“Encrypt PII at rest”). The AI generates code that is already tailored to the environment, preventing PII violations.

Auto-Fixing Vulnerabilities

• The challenge: Critical vulnerabilities in existing services are ignored because the original authors are gone, and new developers fear breaking the code.
• The Guardian way: Promoted to “Remediate all SQL injection vulnerabilities in the checkout service,” Guardian doesn’t just find the bugs; it understands the code context and generates a precise, valid fix for the risk while ensuring non-breaking changes.

Automated Threat Modeling 

• The challenge: Engineering teams move faster than AppSec can review. Security reviews become a bottleneck.
• The Guardian way: “Analyze this ticket for security risks and build a prevention plan.” Guardian automatically builds a threat model based on the ticket description and the organization’s current software inventory, providing a list of countermeasures for the developer to implement before they start coding.

Incident Response to a New Worldwide Attack

• The challenge: A new zero-day vulnerability (like the Shai-Hulud 2 attack) is announced. AppSec teams spend days manually searching repositories to understand exposure.
• The Guardian way: Prompted to “Check if I am vulnerable to Shai-Hulud and plan what to remediate,” Guardian queries the Risk Graph to map IOCs across the organization’s entire application estate, identifies every vulnerable service, and generates a prioritized remediation plan in minutes.

Using New Security Technologies

• The challenge: A developer prompts an AI coding agent: “I need to add a new secret. Store it securely.” The coding agent suggests standard generic methods like a local .env variable or a generic vault, not configured for the company’s infrastructure.
• The Guardian way: Guardian identifies the KMS used in the project (e.g., GCP KMS) and provides a relevant code snippet for storing and retrieving the secret securely, demonstrating its knowledge of both the organization’s codebase, and general security best practices.

Scope a Pen-Testing

• The challenge: Traditional pentest scoping is a manual, point-in-time exercise. AppSec teams struggle to prioritize testing because they lack visibility into what has actually changed – new APIs, dependencies, or secrets – across a massive attack surface.
• The Guardian way: Promoted to “Scope a pentest based on new features and material changes in the last two weeks,” Guardian identifies new sensitive APIs and modified dependencies, then automatically generates a contextual Python script to run tests against these endpoints based on the OWASP Top 10.

Agent Capabilities: Scaling Every Phase of the SDLC

Guardian capabilities are constantly evolving, as it hones the skills of a Principal AppSec Engineer across the SDLC. Here is a glimpse of its current capabilities::

Capability	SDLC Phase	What It Enables
Automated Threat Modeling	Design	Automatic threat modeling and generating contextual countermeasure recommendations per ticket, or per feature – based on the customer Software Graph inventory and risk policies.
Secure Prompt	Design	Prompts are transparently enriched with Software Graph inventory, risk policies, and runtime context before reaching the LLM and generating the code.
Software Graph	Design & Development	Ask anything about the software architecture, software inventory and code-to-runtime: e.g. where are APIs deployed? What is the framework for logging? What code module? Did someone develop a simpler API in this framework before? Is this API internet-exposed, and what security controls apply?
Risk Graph	Development	Ask anything about the risks in the existing code base: e.g. what are the most critical risks, how to fix them, what is the SLA to fix? Perform actions on risks: e.g. change severity, accept risk.
Inline code scan	Development	Detect new risks in your local changes and see what will block your PR: AI SAST, SCA, Secrets, IaC, etc.
AutoFix	Development	Contextual fixes for SAST, SCA, Secrets, and API security aligned with your software graph inventory, company risk policies and runtime context.
Incidents exposure	Governance	Understand your exposure over your entire applications portfolio to ongoing incidents (e.g. Shai-Hulud).
Repository / Code Module / Application / Team risk profile	Governance	Risk-based prioritization aligned with organizational frameworks.
Ownership identification	Governance	Quickly find responsible engineers per code resource (e.g. API, OSS, AI model, secrets) and/or risk for faster remediation.

The Final Word 

Guardian Agent marks the shift from intermittent, reactive scanning to continuous, proactive prevention. It eliminates the unnecessary tradeoff between security and speed by embedding a security-first mindset directly into AI-assisted development. 

Guardian is the culmination of Apiiro’s approach to autonomous application security: AI needs a map – and fixes alone are no longer enough.

With Guardian Agent, prevention becomes the default – and secure, compliant software becomes the natural outcome.

A private preview of Guardian Agent is available today for all Apiiro customers. See it for yourself.