Open Frameworks, Closed Implementation
Knowledge should be accessible. Mastery requires partnership.
Liberation Patterns are freely shared methodologies for AI-augmented security analysis. We publish the frameworks, cost structures, and implementation guidance. You can implement them yourself, or engage us to tailor them to your domain.
This is not open-source software. This is open intelligence—structured approaches to complex problems that democratize access to sophisticated analysis techniques.
An AI Analysis Pattern is a structured, repeatable approach to solving a specific class of problems using a combination of AI models, programmatic elements, and human expertise.
Unlike simply prompting ChatGPT or Claude, patterns provide:
Patterns turn "AI might help with this" into "here's exactly how to do it, what it costs, and how to tune it."
Every Liberation Pattern addresses the what, how, how much, and how well of implementation.
The specific problem being optimized. Could be life management (task/calendar workflows) or domain-specific analysis (supply chain risk, scientific research, regulatory compliance).
Example: "How do I verify authenticity of 10,000 hardware components without manual inspection?"
Detailed guidance for implementation across AI platforms (OpenAI, Anthropic, etc.) with specific model recommendations for different workflow stages.
Defines the mix of AI models and programmatic elements, optimizing for performance and bounded-time responses where critical.
Per-instance run-rate calculations with diverse model examples, allowing implementers to budget for 1,000s of instances.
Example: "Tier 1: $0.0001/item, Tier 2: $0.01/item, Total: $0.13/chip average"
Supervisory patterns for monitoring long-running workflows (hours or days), enabling redirection or termination when needed.
Includes output format recommendations optimized for different stakeholder roles.
Industry-specific adaptations for generic patterns (regulatory compliance, supply chain risk, etc.).
Example: Defense/aerospace requires 99%+ verification; consumer electronics optimizes for cost at 90% detection.
We're building a growing library of freely available patterns. Some are production-ready. Some are experimental. All are documented with implementation details, cost structures, and lessons learned.
Detect counterfeit chips through multi-tier analysis: cryptographic signatures, die photo AI analysis, electrical fingerprinting. Optimized for defense/aerospace (99%+ detection) and consumer electronics (90% at cost).
Cost: $0.13/chip average
Accuracy: 99.2% (defense tier)
Status: Production-ready
Analyze millions of events without drowning in false positives. Three-tier cascade: programmatic filtering → lightweight AI triage → deep analysis. Reduces analyst burden by 87%.
Cost: $0.003/event
Detection Rate: 90-95%
Status: Production-ready
Find ALL attack vectors, not just obvious ones. Hybrid approach: programmatic graph traversal + AI reasoning about interaction effects. Discovered 47 vectors vs 12 manual baseline.
Cost: $2.50/system
Coverage: 3.9x improvement
Status: Beta
Achieve comprehensive test coverage at scale. AI-generated test cases, programmatic execution, intelligent result analysis. Increased coverage from 40% to 85%, 40x faster, 96% cost reduction.
Cost: $0.05/test
Speed: 40x faster than manual
Status: Production-ready
Multi-agent systems working together for hours on complex research problems. Supervisory patterns for checkpoint monitoring, divergence detection, and graceful termination.
Cost: $15-50/research problem
Runtime: 2-8 hours typical
Status: Experimental
AI Cortex for task and calendar management via MCP connectors. Intelligent prioritization, context-aware scheduling, natural language task capture. For individuals managing complex workflows.
Cost: $0.001/interaction
Integration: Calendar, Reminders, Tasks
Status: Beta
Knowledge wants to be free. Expertise is what you pay for.
Recently, I was asked: "Is it possible to derive an equation that represents the resilience of a system or function such that a resilience threshold could dictate whether a system or function could stay online during a function degradation attack?"
This isn't a ChatGPT question. This is a multi-day research problem requiring mathematical modeling, system dynamics understanding, attack scenario analysis, and iterative refinement.
I deployed five specialized agents that worked together for six hours, exploring the problem space, validating approaches, and converging on solutions.
This is where Liberation Patterns differentiate from simply having access to Claude or GPT. The pattern provides the structure, supervision, and orchestration that makes complex, long-running analysis tractable.
And this pattern will be freely available for anyone to implement.
The pattern library is under active development. Early access is available for organizations committed to implementing and providing feedback.
Email: patterns@sanctumsec.com
Subject line: "Pattern Library Access"
Include: Your domain, specific challenges, implementation intent