Old AI Security vs Evo: Watch Agentic Security Replace Weeks of Manual Work

From intelligent chatbots to autonomous agents, innovation has never moved faster thanks to GenAI. But with the rate of velocity comes a massive new challenge: a class of complex, non-deterministic security risks that traditional cybersecurity methods are simply not equipped to handle.

AI-native applications are already running in production. Across industries, teams are deploying copilots, RAG systems, autonomous agents, and AI-powered workflows faster than traditional security processes can keep up. What we’ve heard consistently from customers is simple and urgent:

“We’re shipping AI faster than we can secure it—and the old playbook doesn’t work anymore.”

Your current security stack is likely outmatched, but there's no need to worry. The video below shows exactly why—and what changes when security becomes agentic.

Play Video: YouTube Video: N1zvG7Utxuo

New threats that go beyond traditional AppSec

The traditional approach to application security breaks down completely in the age of agentic AI.

Before Evo, securing AI-native applications looked like this:

• Manual reviews to approve LLMs and AI tools

• Static threat models are created once and rarely updated

• Red teaming is expensive, infrequent, and often too late

• Guardrails configured across multiple disconnected tools

• Monitoring that produces noise, not confidence

These workflows were designed for deterministic software, not non-deterministic agents that evolve continuously and introduce novel attack vectors like:

• Prompt injection: Manipulating an AI model through malicious inputs to extract data or execute unauthorized actions.

• Data poisoning: Contaminating training data to compromise the model's integrity or introduce backdoors.

• Model inversion: Reconstructing sensitive training data from a deployed model's outputs.

• Hallucinations and misinformation: AI is generating factually incorrect information that can be maliciously exploited.

• Supply chain risks: Vulnerabilities hiding within pre-trained models, libraries, and frameworks.

Teams spend weeks of human effort, and yet developers are still frustrated, and security teams expend enormous effort chasing paperwork and tuning alerts, yet still lack real visibility into how AI is actually used in production or whether emerging risks are being caught in time.

The result isn’t security—it’s uncertainty at scale.

The dawn of AI-native security with Evo: An agentic and orchestrated approach

Securing AI requires intelligence within the security system itself. This is the core of AI-native security: building security directly into the entire AI development lifecycle. We call this agentic security orchestration. To keep pace with AI's velocity, we need intelligent security agents that can understand, analyze, and respond to AI-specific risks autonomously.

This isn’t hypothetical. Evo by Snyk is the world's first agentic security orchestration system, purpose-built to meet these unique security demands. Instead of stitching together manual steps, Evo assesses the environment, builds a plan, and executes continuously—adapting as AI systems evolve.

By automating complex security workflows that previously took dozens of man-hours, Evo empowers your security engineers to move at the speed of AI innovation. It transforms AI security from a reactive, guessing game into a proactive, integrated part of your development lifecycle.

What teams are actively using today

Hundreds of early adopters are already utilizing Evo capabilities in real environments, and dozens of large enterprises have helped refine these workflows to production standards. The shift customers describe is immediate: from reviewing AI security to running AI security.

AI Bill of Materials (AI-BOM)

Evo’s Discovery Agent provides full visibility into where AI tools, APIs, and models are being used across development devices and repositories. Teams can automatically generate an AI Bill of Materials (AI-BOM) to highlight AI components and assess potential risks in real time. For many teams, AI-BOM surfaces risk they didn’t know existed—on day one.

MCP Scan CLI:

The MCP Scan CLI checks development devices and connected MCP servers for unsafe or “toxic” components and helps identify risky AI coding tools or dependencies before they are integrated into workflows. This has become a fast way for platform and security teams to regain control without slowing developers down as they are using the MCP scan CLI to:

• Detect AI coding tools and MCP servers

• Identify risky or toxic flows early

• Surface issues before they reach production systems

Automated AI Red Teaming

Instead of one-off exercises, teams are running red teaming as a continuous signal. Using AI-BOM and dynamic threat models, Evo:

• Automatically configures AI-specific red teaming

• Tests against real attacks like prompt injection and data leakage

• Re-runs as applications, prompts, and models change

Customers tell us this is the first time red teaming has kept pace with AI development.

Accelerate innovation with confidence

The urgency isn’t driven by roadmaps—it’s driven by reality. Traditional security methods are becoming increasingly obsolete as AI systems already handle sensitive data, make automated decisions, and interact with users and other systems at scale.

Teams that wait to modernize AI security risk falling behind what’s already running in production. The teams moving fastest share one trait: They’ve stopped trying to adapt old processes and started using agentic security built for AI.

If you’re building or running AI-native applications today, the fastest way to understand the difference is to try it yourself. Old AI security takes weeks. Evo starts working immediately. Start with AI-BOM.