Built for the Moment: Two ArmorCode Patents That Anticipated the Frontier AI Cyber Era

Built for the Moment: Two ArmorCode Patents That Anticipated the Frontier AI Cyber Era

In June 2022, ArmorCode filed two provisional patent applications. This spring, the United States Patent and Trademark Office granted both. These patents arrive at a pivotal moment for exposure management and AI vulnerability prioritization.

• US Patent 12,613,973: System and Method for Managing Knowledge for Solving Vulnerable Issues Using Organizational Knowledge Base (granted April 28, 2026).
• US Patent 12,619,737: Method and System for Security Risk Identification and Controlling Release Management of Software Application with Vulnerable Codes (granted May 5, 2026).

Organizational-Context Prioritization refers to the method of assessing vulnerabilities within the specific context of an organization’s operations and priorities. SLA-Based Release Gating is a mechanism that enforces service level agreements to control the release of software with known vulnerabilities. Together, they provide an exposure management and vulnerability remediation architecture tailored for today’s rapidly evolving technological landscape. Frontier AI is generating, scoring, and chaining vulnerabilities at a pace human teams were never designed to match.

Key Takeaways: AI Vulnerability Prioritization and What These Patents Deliver

• Contextual prioritization replaces raw severity scores at AI scale. Frontier AI vulnerability discovery can generate thousands of candidate findings against a codebase in an afternoon, rendering a generic CVSS rating insufficient for prioritization. The organizational context, such as public-facing aspects, data processing, and applicable compliance rules, is what determines priority.
• ArmorCode holds two patents tailored for this era: organizational-context prioritization (US 12,613,973) and SLA-based release gating (US 12,619,737), both filed in June 2022 and granted in spring 2026.
• A programmatic release gate serves as the ultimate defense line. Shipping despite critical issues to meet deadlines is a common failure mode. Enforceable, hierarchical SLAs in the CI/CD pipeline prevent non-compliant builds from reaching production before an AI-assisted adversary can exploit them.
• Context must automatically reach the developer. By tagging findings with the right knowledge base articles and attaching them to tickets through runbook automation, the platform provides developers with prior remediation history, avoiding reliance on generic reference pages.

How these patents address frontier AI vulnerability discovery

When our inventors filed the provisionals on June 6, 2022, the dominant application security conversation was still tool sprawl, alert fatigue, and the perennial tension between shipping fast vs. shipping safe software. We were focused on a deeper problem we believed was coming: what happens when the cost of finding a vulnerability goes down to zero, while the cost of fixing the right one stays about the same?

Four years later, that future has arrived faster than most forecasts predicted. Anthropic’s Project Glasswing and its Claude Mythos frontier-model preview, along with comparable efforts across the industry, are putting frontier AI vulnerability discovery into the hands of a small number of trusted defenders. Inevitably, capabilities of the same class are reaching well-resourced adversaries. The economics of attackers as well as defenders have shifted in two specific ways:

1. AI agents now enable machine-speed discovery. They can read a codebase, infer security-relevant invariants, and produce candidate vulnerabilities in minutes, overwhelming any backlog designed for human-paced triage.
2. AI has simplified chaining. What was once a senior red-team skill, correlating several low- and medium-severity findings into a single critical exploit path, is now something AI can autonomously plan, score, and test.

In this environment, the bottleneck is no longer knowledge about a particular vulnerability. It’s about identifying which vulnerability in our code, in our environment, with downstream users, is critical today. And once you’ve identified it, the next question is equally urgent: can you stop a non-compliant build from shipping before an AI-assisted adversary exploits it? That’s where vulnerability release gating moves from a nice-to-have to a hard requirement—a programmatic enforcement layer that sits in the CI/CD pipeline and prevents vulnerable code from reaching production regardless of deadline pressure.

These questions are precisely what these two patents address: one governing how organizational context transforms AI-scale findings into actionable priorities, and the other governing how vulnerability release gating enforces the policies that protect production.

Patent 1: Organizational-Context Prioritization for AI Vulnerability Triage (US 12,613,973)

A vulnerability without organizational business context is just noise. A CVE rated 9.8 in the NVD might be a P0 emergency for one company and a non-issue for another, depending on whether the vulnerable component is public-facing, the data it processes, which compliance requirement applies, and how the team has historically resolved similar issues. AI-driven scanners only widen that gap by producing findings at a rate that makes a generic severity score useless on its own. Effective exposure management demands more than raw severity rankings—it requires organizational context to separate signal from noise at scale.

US 12,613,973 describes a system that solves this by binding the platform’s knowledge base directly to the organization’s authoritative documents such as policy documents, technical specifications, runbooks, and prior remediation history. It then weaves that contextual knowledge through the entire vulnerability lifecycle, giving exposure management programs a structured foundation for deciding what actually needs attention and when.

The granted patent covers:

• Tagging CVEs and CWEs to KB articles with a standardized taxonomy, so the system can connect any new finding to the organization’s actual prior knowledge of how that class of issue has behaved in a specific environment.
• Automatic suggestion of the most relevant KB articles when a ticket is created, so a developer assigned a security ticket lands directly on the correct context and not a generic OWASP page.
• Automated workflows that attach the right context to the right ticket without the need for manual curation, across ticketing systems including JIRA, ServiceNow, GitLab, and Azure.

The key takeaway: this is the foundation that makes AI-discovered findings actionable and transforms exposure management from a reactive exercise into a disciplined, context-driven program. Frontier AI can tell you that a thousand things might be wrong. Organizational business context tells you which three to fix this sprint and who resolved something similar last quarter.

Patent 2: SLA-Based Vulnerability Release Gating (US 12,619,737)

Patent 1 governs prioritization and triage, Patent 2 governs release management—and it’s the enforcement layer that frontier AI vulnerability discovery makes non-negotiable.

US 12,619,737 describes a release-management system that lets security and engineering leaders define enforceable Service Level Agreements (SLAs) at three distinct levels. The system enforces them in the CI/CD pipeline through an embedded script and a release-gate API.

The three SLA tiers each do real, distinct work:

• Level 1: Vulnerability count thresholds. Critical, high, medium, and low ceilings, beyond which a build cannot ship. This is a straightforward tier that most organizations still don’t enforce consistently—a gap that becomes dangerous when frontier AI vulnerability discovery can surface hundreds of new findings against a codebase before a single ticket is triaged.
• Level 2: Required tool execution. A list of designated critical scanning tools that must have run against the change set. This SLA is based on the premise that untested code has unknown security posture. In an AI-assisted adversary environment, a missed scan is not a defensible position.
• Level 3: Flexible risk parameters. Threat-intelligence scoring and compliance deviance against frameworks like OWASP Top 10, CWE Top 25, PCI DSS, HIPAA, and ASVS. This is the level that can block a release based on real business context—for example, because of a regulatory deviation even when the raw vulnerability counts look acceptable.

The system also captures every failed release attempt, supports authorized exception approvals via a baseline mechanism (so that legacy debt does not permanently block forward progress), and applies SLAs globally or to specific products, sub-products, microservices, and environments. This hierarchy maps cleanly to how modern enterprises actually ship software.

In a world where frontier AI vulnerability discovery means an adversary’s tooling can be probing a freshly deployed build before your on-call engineer has opened their laptop, vulnerability release gating becomes the last line of defense.

What we anticipated when we filed the patents

A few things look obvious in hindsight but were less obvious in mid-2022:

Severity scores don’t work with AI-scale discovery. When an AI can produce ten thousand candidate findings against your codebase in an afternoon, “fix everything above 7.0” stops being a remediation strategy. Patent #1 was a bet that contextual risk prioritization would become the only kind of prioritization that mattered.

Detection without enforcement won’t work. Detecting that a release has critical issues, and shipping it anyway because of the deadline, is a common security-program failure mode. Patent #2 was a bet that vulnerability release gating would need to become programmatic, hierarchical, and tracked with exceptions—not a manual checkpoint that deadline pressure could override, but an enforceable layer embedded directly in the CI/CD pipeline.

Knowledge needs to live within the workflow. Confluence pages that aren’t linked to tickets become scattered knowledge that teams are unable to use consistently. Patent #1 was a bet that the right context has to be routed to the right ticket automatically, or it won’t get used.

None of these were minor calls. They shaped the ArmorCode Agentic AI Platform and it is the reason that ArmorCode customers are ready for the frontier AI moment.

The bottom line for security leaders

The frontier AI cyber moment is here. The defenders who fare best in the next 24 months will not be the ones with the most scanners, they will be the ones whose platforms can absorb a firehose of AI-discovered findings, route the few that matter to the right developer with the right context, and avoid shipping a build that violates policy.

That is the architecture US 12,613,973 and US 12,619,737 describe. We are happy they are now part of the public record, and proud of the inventors whose work in 2022 made them possible: Anant Misra, Nikhil Gupta, Praneet Khare, Deepak Yadav, and Mark Lambert. Both granted patents are public on the USPTO Patent Center under their grant numbers, and full text is available on Google Patents.

If you would like to see how Organizational-Context Prioritization and SLA-Based Release Gating work, we can show you how customers have operationalized these capabilities into their exposure management efforts. Get in touch with us.