Akamai Code Guardian, powered by Apiiro, is a security solution for AI-assisted software development designed to prevent insecure code preproduction, auto-remediate vulnerabilities, and provide complete code-to-runtime visibility.
Key takeaways
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Velocity without governance creates critical exposure.
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AI coding tools accelerate code output and expand the attack surface; proactively preventing vulnerabilities in preproduction restores security control before high-speed development reaches runtime.
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Reactive code scanning can no longer keep up with modern software development.
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Rapidly shrinking exploitation windows mean security teams can no longer rely on reactive scanning; continuous architectural context ensures teams prioritize and mitigate real-world business risks immediately.
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Visibility across the entire lifecycle is required to eliminate blind spots.
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Complex software architectures obscure developer changes and supply chain dependencies; deep code-to-runtime mapping eliminates blind spots so security teams maintain control across every asset.
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Remediation delays leave systems exposed to zero-day exploits.
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Manual patch creation cannot keep pace with AI-driven offensive tools; developer-native automated fixes eliminate vulnerabilities during development to shorten window-of-exposure metrics.
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Democratized application building introduces unpredictable risk.
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Nontechnical users leveraging natural language prompts lack deep security awareness; automated prevention and inline guardrails enforce consistent enterprise compliance without hindering business innovation.
Frequently Asked Questions (FAQ)
Frequently Asked Questions (FAQ)
AI coding assistants accelerate developer velocity and democratize software creation, but attackers simultaneously leverage AI-powered offensive tools to discover and exploit vulnerabilities at unprecedented speed, collapsing the median time to exploit to zero days.
It is powered by Apiiro’s AppSec Data Fabric, which combines a Software Graph (mapping software architecture across repositories, APIs, infrastructure, dependencies, pipelines, and runtime) and a Risk Graph (evaluating custom policies, compliance, and business risk).
The engine consists of a layer that enriches AI prompts and runs threat modeling to prevent insecure code generation, and a layer that automatically generates context-aware fixes for vulnerabilities before code reaches source control.
It uses AI context from the AppSec Data Fabric to generate developer-native pull requests and code fixes, allowing developers to review and apply safe, compliant patches directly within their existing workflows.
Key capabilities include deep code-to-runtime visibility, deep context risk prioritization, automated fix generation, shift-left policy enforcement, and AI agent and pipeline governance.
It establishes guardrails and governance around natural language prompts, AI tools, and infrastructure templates, ensuring nontechnical users creating applications adhere to consistent security practices and corporate standards.
Traditional scanning focuses on identifying bugs after code is written, creating backlog fatigue and lagging behind attacker speeds; proactive prevention stops risks before they enter production or reach runtime.