In our first blog in this AI Threat Readiness series, we covered how to reduce critical exposures and scan with AI by mapping your attack surface with Wiz ASM, validating what’s actually exploitable with the Red Agent, and prioritizing based on reachability, exploitability, and business impact.
Finding critical risks at machine speed is a crucial first step. To keep pace with the AI threat landscape, organizations need to close the loop - remediating at the same speed they detect. That means knowing where to fix, who should fix it, and how to fix it, across every layer from cloud configuration to source code.
In this post, we dive into Pillar 2 of the AI Threat Readiness Framework: Accelerate Patching and Response.
Traditional remediation workflows were built for a slower world. A vulnerability gets disclosed, a ticket gets created, it enters a queue, someone triages it, routes it to the right team, and eventually a fix gets deployed. That process might take days or weeks, time that organizations no longer have as the window from detection to exploitation is shrinking.
And the volume is only growing. AI is accelerating vulnerability discovery on both sides: attackers are finding exploitable flaws faster, and defenders now have tools that surface complex logic vulnerabilities traditional scanners miss entirely. More visibility is a good thing, but only if organizations can solve the operational challenges that have slowed remediation down since the cloud era:
Unclear ownership: Security teams find the vulnerability, but they often don't know who pushed the code, who owns the affected resource, or who can actually fix it. The routing process alone can take longer than the fix itself.
Generic remediation guidance: A CVE advisory tells you a vulnerability exists and what version to upgrade to. It doesn't tell you whether to patch the running instance, update the base image, fix the Dockerfile, or change the Terraform template. Without environment-specific context, teams waste time figuring out the right fix path.
Manual processes that don’t scale: When AI-powered scanners surface thousands of findings, manual triage and ticket creation becomes a bottleneck. Teams need automated workflows that can handle volume without sacrificing accuracy.
These aren't new problems, but AI has made them urgent. Pillar 2 is about solving all three.
The goals of this pillar are to:
Establish clear ownership so findings route to the right team automatically.
Identify the root cause of every vulnerability - whether it's a misconfiguration, an outdated dependency, or a code-level flaw.
Determine the optimal fix path based on your environment's specific architecture, from cloud configuration to source code.
Automate remediation workflows to eliminate manual triage and reduce mean time to remediation.
Prevent recurrence by shifting fixes left and embedding guardrails into development pipelines.
In the AI threat landscape, speed is crucial and security teams need to spend time on risk reduction, not tracking down code or resource owners.
Wiz builds a unified ownership model from every source your organization already uses, inside or connected to Wiz:
Service ownership: Take defined services from the Wiz Service Catalog, which auto-discovers services, or pull from your Backstage integration. Resources in Wiz automatically inherit ownership from the service they belong to.
Project ownership: Group resources by business unit, application, or environment with designated owners in Wiz or sync from ServiceNow CMDB to populate automatically. Issues are scoped to the right team based on the project they impact.
Resource-level ownership: Use existing cloud tags or set up Resource Tag Rules to assign owners automatically. Wiz also detects who created a resource from cloud event logs, so even untagged resources have a potential owner when an Issue needs to be assigned.
Code-based ownership: Leverage CODEOWNERS files to define who owns specific code paths, Git Blame to attribute findings to the developer who last modified the affected line, and Wiz’s code-to-cloud mapping to trace deployed resources back to source. Findings can be tied directly to the developer accountable for the code that introduced them.
With all of these signals connected, teams can see ownership across Wiz: on the Security Graph, as suggested assignees on every Issue, and within Green Agent's investigation. When a critical Issue surfaces, teams don't need to hunt for the right owner. Green Agent identifies the most suited owner and teams can assign with one click, and it’s already saving time for customers:
The Green Agent that surfaces the most suited owner has been really helpful. We'd typically have to get the Wiz Issue and then go to our SIEM to look up changes to our cloud resources to find the right owner. With the Green Agent it's surfaced automatically in the Wiz Issue and is incredibly valuable in finding the right person so our developers can quickly remediate.
Eric Tu, Product Security Engineer, Rogo AI
And to automate at scale, Workflows is by your side with a built-in Assign Ownership template to route Critical Issues automatically to the right owner.
When a critical vulnerability is found, the first question is rarely "what's the CVE?", it's "where do I actually fix this?" A vulnerable package might be running on a VM, but the real fix could be in the container image, the Dockerfile, or a dependency declared three layers deep in a Terraform module.
Wiz Green Agent automatically analyzes the context of your environment to determine the fastest and safest remediation path. By leveraging the Security Graph's code-to-cloud mapping, historical remediation patterns, and domain specific investigation, Green Agent traces a runtime vulnerability back to its source, identifying where you need to fix, who needs to fix it, and how.
Green Agent synthesizes information from across the Wiz platform - resource metadata, cloud configurations, identity and access policies, network exposure, ownership information from the Graph and Integrations, and the code-to-cloud pipeline - to evaluate multiple remediation paths and recommend the most efficient strategy.
Teams get environment-specific, actionable remediation guidance in the form of a step-by-step plan tailored to their architecture. And they can act on it immediately: one-click PRs to push a fix directly to the repository, send to a coding agent that creates a GitHub issue and tags their AI agent to write the fix, or via MCP.
And for broader orchestration at scale, teams can route remediation through a Workflow to customize their response based on what makes sense for their organization.
Wiz Workflows provides the orchestration layer for teams to automate the full remediation chain - from investigation to ownership assignment to developer notification - through a repeatable, customizable drag-and-drop canvas.
Workflows automates the entire remediation chain. When a critical Issue is created, a Workflow can automatically trigger the Green Agent to investigate, identify the root cause, determine the right owner, and open a PR if a code fix is available - turning what used to be hours of manual triage into an automated flow that runs in minutes.
By combining platform actions, AI agents, and integrations with your existing tools through the Wiz Integration Network (WIN) ecosystem, organizations can establish repeatable remediation processes that continuously reduce exposure and accelerate response times.
Reducing exposure and accelerating response handles today's risks, preventing them from being introduced in the first place reduces tomorrow's. In the AI threat landscape, organizations need to move from detecting misconfigurations and auditing after deployment to actively blocking them from being deployed in the first place.
Wiz guardrails enables this with a unified policy framework that spans from cloud to pipeline. The same policies that evaluate your cloud environment also run in your CI/CD pipeline - so the vulnerabilities, misconfigurations, and exposed secrets Wiz catches in production are caught and blocked at build time before they're ever deployed.
Those guardrails extend even further left with Wiz Code plugins, which bring the same policy enforcement directly into the developer's IDE in the form of Wiz Skills and Wiz Hooks. The plugins scan at pre-commit and pre-push for secrets, IaC misconfigurations, and vulnerable dependencies, ensuring security gates hold whether a human or an AI agent is writing the code.
And WizOS raises the baseline further, providing hardened container images that remove inherited vulnerabilities before a single line of application code is written, preserve supply chain integrity, and include SLAs for CVE remediation. When CVEs are found on containers, Green Agent follows you there as well, recommending the specific WizOS image to migrate to, and teams can send it directly to a coding agent to apply the swap. Developers start secure by default, reducing the volume of findings that need to be triaged downstream.
Connect your ownership sources including CODEOWNERS files, code repositories, cloud environments, and CMDB/Backstage integrations so Wiz can automatically suggest the right assignee when Issues surface.
Enable Green Agent to automatically analyze your top critical and high Issues with environment-specific remediation guidance and suggested owners.
Build your first Workflow to automate a high-volume remediation process, start with the Assign Ownership template to route critical Issues automatically.
Deploy Wiz Code plugins and WizOS to shift guardrails left and block risks from being deployed in the first place.
Pillar 1 focused on finding and validating the risks that matter most. Pillar 2 ensures those risks get resolved - routed to the right owner, remediated at the source, and automated at scale. Together, they close the loop between discovery and resolution so your organization can keep pace with the AI threat landscape.
Check out our next blog in this series to learn about Pillar 3: Perform AI Code Analysis Natively in Wiz.
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