Mythos Readiness Program
The Vulnerability Tsunami is Coming.
Is Your Security Ready For Mythos?
Claude Mythos found thousands of previously unknown zero-day vulnerabilities across every major OS and browser — in weeks. When those findings reach your backlog, the operational discipline of prioritizing, triaging, and remediating will matter more than ever. ArmorCode is built for this moment.
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The Vulnerability
Tsunami is Coming
Foundational Discovery
(Today)
Mythos finds thousands ofvulns in foundational software
The Downstream Flow
(Near-Term)
Published CVEs from Mythos discoveries flow into every enterprise scanner signature
Backlogs Grow
(Near-Term)
Suddenly, every organization running standard software has new critical findings
Enterprise Proliferation
(6–18 Months)
Enterprises gain direct access to Mythos-class AI models to scan their own proprietary codebases
The Tsunami
Vulnerability backlogs explode by orders of magnitude. Manual triage mathematically breaks.
Three Forces. One Reckoning.
Discovery is solved.
What comes next isn’t.
AI can now surface vulnerabilities at a scale and speed no human team can match. The harder work still sits with you: deciding what matters and fixing it fast.
Volume
AI coding assistants are generating code faster than any human team could. Every line is attack surface.
Density
AI-generated code has a different risk profile. Pattern reuse and insecure defaults compound the problem.
Unknowns
Mythos-class AI models surface zero-days that were always there. Unknown risk becomes known risk. Fast.
Zero-days discovered by Mythos in weeks of pre-release testing
Working exploits reproduced on the first attempt
Until Mythos-class capabilities proliferate to other AI labs
Longest-surviving vulnerability uncovered (OpenBSD)
The Mythos Challenge
Why doing nothing is not an option for security leaders
Discovery is not remediation. The best vulnerability discovery engine in the world doesn’t help if your team can’t act on what it finds. Here’s what every CISO is now facing.
Downstream CVE flood
Every Mythos-discovered flaw in Linux, a browser engine, or a common OSS library becomes a published CVE that lands in your scanner queue — multiplying your backlog by orders of magnitude.
The 6–18 month window
OpenAI and other labs are building comparable capabilities. Within 12–24 months, AI-powered discovery will scan your proprietary code directly. The time to build operational muscle is now.
Context is the bottleneck
AI assesses technical severity brilliantly. It doesn’t know which asset handles PII, which service is internet-facing, or which team owns the fix. Without business context, discovery becomes noise.
Manual triage breaks down
Security teams already juggle a dozen scanners and distributed dev orgs. When findings volume doubles or triples, spreadsheet-driven prioritization and ticket routing collapse under the load.
Governing the AI agents
Mythos-class agents deployed inside your environment need oversight: approvals, access controls, audit trails. AI doing the discovering needs governance too — or shadow AI becomes your next breach vector.
Board-ready reporting, faster
CISOs will be asked “what’s our exposure to the latest Mythos-discovered flaws?” within hours of each disclosure. Teams without unified data and persona-aware views won’t be able to answer.
The Hard Truth
Waiting is a strategic decision — and it’s the wrong one.
Every week without an operational plan is a week your backlog grows faster than your team can respond. The organizations that will thrive in the post-Mythos landscape are building the muscle to prioritize, orchestrate, and govern at scale — starting now.
The Readiness Gap
Teams are already over capacity.
The wave hasn’t even hit.
run 11+ distinct security tools
say disconnected tools hurt prioritization
waste time on vulns that don’t matter
until Mythos-class Al is everywhere
Purple Book Community, “The State of AI Risk Management 2026 report,” March 2026.
Anthropic estimate for open-source equivalents of frontier AI discovery models, April 2026.
Live Webinar
What Mythos Means for Your Security Program
Join ArmorCode and industry experts for a candid discussion on what Claude Mythos changes, what it doesn’t, and the concrete steps security leaders should take in the next 90 days.
Wednesday, May 6, 2026
11:00 AM PT / 2:00 PM ET
Industry experts + ArmorCode leadership
The ArmorCode Approach
Turn AI-scale discovery into enterprise-scale risk reduction
ArmorCode sits at the intersection of discovery and action. We ingest, normalize, and prioritize findings from your entire security stack — and orchestrate the remediation workflows that turn them into measurable risk reduction.
The Discovery Engine
Autonomously finds 0-days at scale
Assesses technical severity of the flaw
Hardens foundational open-source and commercial software
Generates massive volume of raw findings
Together closing the loop from finding to fixing
The Operationalization Engine
Assesses true business risk and context
Routes fixes to specific development teams
Tracks SLAs and verifies patches autonomously
Governs the AI agents doing the finding
Mythos finds what’s broken. ArmorCode decides what matters, routes the fix, and verifies it’s done.
1
Unified findings ingestion at scale
200 billion+ findings processed annually through 350+ native integrations. Mythos disclosures flow into the same unified view as SAST, DAST, SCA, CSPM, and pentest output.
2
Contextual risk via the Risk Intelligence Graph
Correlate findings with asset criticality, data classification, internet exposure, and compensating controls. A zero-day in an air-gapped test system scores differently than the same CVE in a production PII service.
3
Persona-aware intelligence with Anya
Same data, role-specific answers. CISOs get board-ready exposure summaries. Engineers get correlated findings. Developers get code-level fix instructions tied to their repo.
4
Automated remediation orchestration
No-code workflow engine routes findings to Jira, ServiceNow, GitHub — with context attached. SLA tracking, escalation, and verification ensure findings don’t just get assigned but actually resolved.
5
AI Code Insights for root-cause
Trace one Mythos-discovered OSS flaw to every app that uses it. Identify the responsible team. Generate repo-specific fix guidance — turning an advisory into a targeted action.
6
AI Exposure Management (AIEM)
Govern the AI agents doing the discovering. Inventory AI usage, MCP servers, and shadow AI. Enforce policies and produce board-ready evidence of AI risk governance.
Customer Proof
How ArmorCode customers are already preparing
Global 2000 enterprises across financial services, healthcare, retail, and technology are unifying findings, prioritizing by business context, and orchestrating remediation at AI scale — today.
1
Unified findings ingestion at scale
200 billion+ findings processed annually through 350+ native integrations. Mythos disclosures flow into the same unified view as SAST, DAST, SCA, CSPM, and pentest output.
2
Contextual risk via the Risk Intelligence Graph
Correlate findings with asset criticality, data classification, internet exposure, and compensating controls. A zero-day in an air-gapped test system scores differently than the same CVE in a production PII service.
3
Persona-aware intelligence with Anya
Same data, role-specific answers. CISOs get board-ready exposure summaries. Engineers get correlated findings. Developers get code-level fix instructions tied to their repo.
4
Automated remediation orchestration
No-code workflow engine routes findings to Jira, ServiceNow, GitHub — with context attached. SLA tracking, escalation, and verification ensure findings don’t just get assigned but actually resolved.
5
AI Code Insights for root-cause
Trace one Mythos-discovered OSS flaw to every app that uses it. Identify the responsible team. Generate repo-specific fix guidance — turning an advisory into a targeted action.
6
AI Exposure Management (AIEM)
Govern the AI agents doing the discovering. Inventory AI usage, MCP servers, and shadow AI. Enforce policies and produce board-ready evidence of AI risk governance.
Get your free Mythos Readiness Assessment
A no-cost, tailored evaluation from the ArmorCode team. We’ll map your current AppSec posture against the coming AI-scale disclosure wave and give you a concrete 90-day action plan.
Stack and coverage audit
We review your current scanner mix, integration gaps, and exposure signals.
Context readiness score
How well your asset inventory, ownership, and business context will hold up at AI-scale finding volumes.
Workflow stress test
Modeled impact of 3–10× finding volume on your triage, routing, and SLA performance.
90-day action plan
Prioritized, expert-delivered recommendations to close the gaps before the tsunami hits.
Request Your Readiness Assessment
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