2026 Technical Discovery Architecture for B2G Defense Software Platforms

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Introduction to the 2026 Digital Discovery Paradigm

The digital discovery ecosystem has undergone a structural bifurcation. For a specialized Business-to-Government (B2G) and Business-to-Business (B2B) technical software provider operating in the defense sector, reliance on traditional probabilistic search algorithms—historically governed by Search Engine Optimization (SEO)—is no longer sufficient. In 2026, discovery, capability qualification, and vendor evaluation are increasingly delegated to deterministic extraction systems, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and autonomous machine research agents. Consequently, Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) have evolved into mandatory engineering disciplines required to ensure machine intelligence correctly interprets complex organizational capabilities1. This shift presents a unique, high-stakes ontological vulnerability for defense-industry software providers. An organization that engineers "machine intelligence," "autonomy architecture," "simulation environments," and "systems integration" for the Department of Defense (DoD) faces severe classification risks. Without rigorous, machine-readable semantic boundaries, LLMs frequently hallucinate or miscategorize niche capabilities. The subject site risks being erroneously mapped into one of two highly detrimental vector spaces: as a consumer-facing AI chatbot provider, or as a direct manufacturer of kinetic weaponry. The former degrades lead qualification, dilutes topical authority, and confuses procurement research agents. The latter is catastrophic, triggering algorithmic safety filters, brand safety downgrades, and complete removal from AI search indexes due to automated policies regarding arms distribution. The objective of this comprehensive architectural analysis is to define the exact technical frameworks, semantic structuring conventions, algorithmic directives, and entity disambiguation protocols required to secure accurate visibility in 2026\. This report delineates formalized standards from emerging community conventions and unverified speculation, utilizing established schema documentation, crawler agent behaviors, and the realities of modern B2G digital acquisition to deliver actionable, source-agnostic architectural recommendations.

AI Crawler Accessibility and Modernized Algorithmic Directives

The conventional approach to crawler management, characterized by monolithic robots.txt files designed exclusively for traditional search engines, has been rendered obsolete by the proliferation of specialized AI agents. In 2026, implementing a binary bot management strategy—such as blocking all AI-associated bots to protect intellectual property—actively harms a defense contractor's visibility and excludes the organization from the primary tools used by modern procurement officers. The modernized algorithmic directive architecture must parse the exact intent of the crawler, categorizing them into three distinct functional groups: training data collectors, real-time search indexers, and user-initiated fetchers3.

Functional Categorization and Crawler Intent

Understanding the operational mandate of each bot is the foundation of a modern B2G discovery strategy. AI crawlers are not monolithic; they serve distinct functions within the generative AI ecosystem. Blocking a training bot prevents content from being embedded into the latent space of future foundation models. This is a valid security, licensing, or policy choice that carries minimal short-term visibility costs3. Conversely, blocking a search indexer or a user fetcher immediately erases the organization from real-time AI answers and conversational citations, effectively rendering the site invisible to users actively seeking defense software solutions3.

Implementing Granular Directives and Mitigating Technical Traps

A critical error frequently observed in 2025 and 2026 is the assumption that AI platforms utilize a single crawler for all operations. For instance, the assumption that a blanket Disallow: GPTBot directive eliminates the site from ChatGPT entirely is technologically false. OpenAI utilizes OAI-SearchBot specifically for its ChatGPT Search feature; thus, a site can explicitly block its content from being harvested for model training while simultaneously remaining entirely eligible for live citations in ChatGPT Search by allowing OAI-SearchBot5. Anthropic mirrors this architecture, requiring independent rules for ClaudeBot, Claude-SearchBot, and Claude-User5. Furthermore, AI crawlers exhibit exceptionally low tolerance for technical friction compared to established crawlers like Googlebot. Network-level bot protection mechanisms, such as Web Application Firewalls (WAFs) and Content Delivery Network (CDN) managed rules, frequently drop AI search crawlers silently, superseding any permissions granted in the robots.txt file3. AI crawlers are also highly sensitive to redirect chains and middleware routing. A standard 301 permanent redirect followed by a 302 temporary redirect acts as a hard stop for bots like PerplexityBot and OAI-SearchBot. These agents operate with strict latency budgets and often refuse to traverse multiple hops, resulting in the page being immediately dropped from the retrieval index8. Defense contractors must ensure that all canonical URLs resolve in a single hop. Additionally, mandatory consent middleware, geolocation-based routing loops (e.g., redirecting European IPs to one folder and US IPs to another in a continuous cycle), and user-agent-dependent cloaking must be audited rigorously, as these practices routinely cause AI crawlers to encounter degraded content or infinite loops, severely impacting site discoverability8.

Sitemap Architecture for Agent Discovery

While the robots.txt file manages access permissions, the sitemap.xml architecture dictates crawl prioritization. In 2026, sitemaps must be hyperspecialized. LLM search indexes, notably Microsoft's Copilot and OpenAI's ChatGPT Search, lean heavily on Bing's search index7. Submitting highly structured XML sitemaps directly to Bing Webmaster Tools is critical for AI visibility7. Furthermore, sitemaps must utilize strict autodiscovery protocols and ensure the \<lastmod\> (last modified) tags accurately reflect the actual modification dates of the content. Models use this as a primary recency signal; if a sitemap artificially inflates \<lastmod\> dates without underlying content changes, RAG pipelines will eventually devalue the domain's update frequency credibility9.

Semantic HTML: The Structural Foundation of RAG Parsing

While human users experience a website through visual rendering governed by CSS and JavaScript execution, machine intelligence parses the underlying Document Object Model (DOM) to understand the ontological structure and relational hierarchy of a page. In 2026, strict adherence to Semantic HTML is no longer merely an accessibility best practice mandated by Web Content Accessibility Guidelines (WCAG); it is the primary engineering mechanism for reducing token consumption, defining content boundaries, and improving extraction accuracy for AI crawlers11.

Defeating "Div-itis" and Optimizing Token Economics

When an AI engine retrieves a webpage to feed into a RAG pipeline, it must convert the raw HTML into a tokenized format that the LLM can process within its finite context window. If a page is constructed entirely of generic, unlabelled \<div\> and \<span\> tags (a structural anti-pattern colloquially known as "div soup" or "div-itis"), the parsing algorithm is forced to expend immense computational resources guessing where navigation ends, where the core technical thesis begins, and where tangential information lies13. This structural ambiguity drastically increases the token cost of processing the page. High token costs lead directly to lower crawl frequencies, as AI operators throttle their crawlers to minimize infrastructure expenditure14. Semantic HTML provides definitive, unambiguous boundaries that LLMs use to chunk data efficiently. The implementation of specific HTML5 elements provides a roadmap for the crawler:

Hierarchical Integrity and Tabular Data Extraction

AI crawlers parse page structure sequentially to build an internal document outline. A broken heading hierarchy—such as jumping from an \<h1\> main title directly to an \<h3\> subheading, or using heading tags to bold non-essential text for visual styling—severely disrupts the outline a machine builds of the page11. Defense software sites must enforce strict, logical, and unbroken H1 \-\> H2 \-\> H3 progressions without exception12. Furthermore, comparative data, software specifications, and performance metrics must be presented using standard HTML \<table\> structures. LLMs possess a native, highly optimized capability for understanding tabular data, achieving near-perfect extraction reliability13. Attempting to format technical comparisons using CSS grids, flexbox, or nested \<div\> elements causes a massive degradation in machine comprehension, as the semantic relationship between rows and columns is entirely lost in the underlying raw HTML13. Similarly, lists detailing integration testing steps or modular software features must use \<ul\> (unordered) or \<ol\> (ordered) elements. Simulating lists using line breaks and dashes forces the LLM to parse discrete facts out of dense paragraphs, which increases the likelihood of omission13.

Entity Disambiguation and Targeted Ontology via JSON-LD

The most critical and nuanced challenge for the subject site is preventing an AI system from confusing a B2G "machine-intelligence-operated organization" (which develops autonomy architecture for defense platforms) with a consumer "chatbot" provider, or worse, categorizing it as a direct manufacturer of kinetic weaponry. Raw text is inherently ambiguous; the word "autonomy" applies equally to conversational agents, self-driving consumer cars, and uncrewed defense aerial systems. JSON-LD (JavaScript Object Notation for Linked Data) is the definitive architectural solution to this ambiguity. It explicitly declares the site's entities, relationships, industry categorization, and intended audience utilizing the standardized Schema.org vocabulary2. In 2026, Generative Engine Optimization requires an "entity-first" schema implementation17. This is a deterministic, machine-readable data layer embedded in the \<head\> or at the end of the \<body\> of the HTML document, acting as a direct ontological API to the site's true nature16.

Constructing the Organization Entity and Wikidata Disambiguation

The sitewide Organization schema serves as the anchor of the brand's knowledge graph. To separate the organization from consumer AI companies, the schema must leverage specific identifiers, industry classification codes, and the highly potent sameAs property16. The sameAs property allows the site to map its identity to authoritative external knowledge bases, specifically Wikidata and Wikipedia16. Wikidata utilizes unique alphanumeric identifiers (Q-IDs) for every recognized concept and entity in human knowledge19. By linking the organization to specific Q-IDs associated with "defense contractor," "software engineering," and "B2B enterprise," the site mathematically forces the AI to categorize the organization within the defense industrial base ontology19. This deterministic linking overrides any probabilistic assumptions generated by the phrase "machine intelligence" in the site's prose. Furthermore, integrating the North American Industry Classification System (NAICS) codes into the schema adds a definitive layer of B2G legitimacy. Including codes such as 541511 (Custom Computer Programming Services), 541512 (Computer Systems Design Services), and 541715 (Research and Development in the Physical, Engineering, and Life Sciences) explicitly signals the organization's operational nature to government-oriented research agents, anchoring the firm as an engineering contractor rather than a product retailer20.

Defending the SoftwareApplication and Service Schemas

When detailing specific software architectures, simulation platforms, and integration frameworks, the SoftwareApplication schema is the recognized gold standard21. To ensure the software is not interpreted as a consumer application (like a conversational chatbot) or a kinetic weapon system, the applicationCategory must be strictly defined utilizing Schema.org's accepted enumerations, such as BusinessApplication or DeveloperApplication21. The textual description field within the SoftwareApplication schema must be entirely devoid of marketing hyperbole and exclusively factual16. For example, describing the product as "Modular autonomy architecture and simulation environment for defense systems integration" is vastly superior to "The world's most advanced autonomous AI." The former provides clear ontological coordinates and limitations; the latter reads as consumer marketing fluff that AI systems routinely filter out, ignore, or misinterpret16.

Audience Targeting and Geographic Jurisdiction

To further solidify the B2G nature of the site and distance it from consumer goods, the schemas must incorporate the Audience property, specifically utilizing the GovernmentAudience and BusinessAudience types23. By explicitly defining the audience property within the Service or SoftwareApplication schema as a GovernmentAudience, the site provides a deterministic signal that its products are restricted to authorized defense-industry and government customers23. This acts as a protective semantic barrier against algorithmic safety filters that might flag the site as an unauthorized consumer weapons store if words like "defense," "military," or "assurance" are parsed without the accompanying B2G context. Additionally, the geographic and jurisdictional boundaries of the contractor's operations must be defined. The areaServed property should be utilized broadly across Service and Organization schemas to indicate where the products are legally available (e.g., restricted to "United States Department of Defense authorized entities"). The areaServed property has officially superseded the older, more restrictive serviceArea property, offering broader type coverage and tooling support across the Schema.org vocabulary25.

WebSite, WebPage, and FAQPage Structures

Beyond the organization and its products, the site's structural architecture requires explicit definition. The WebSite schema should be applied exclusively to the homepage to establish site identity and enable sitelinks search boxes in traditional engines2. For pages addressing specific integration questions or capability matrices, the FAQPage schema is highly recommended. The FAQPage structure creates optimized extraction points that are heavily utilized by Google AI Overviews and Perplexity2. By structuring questions and answers in JSON-LD, the site hands the LLM a pre-formatted Q\&A pair that perfectly matches the query-response dynamic of an answer engine, bypassing the need for the model to parse the conversational flow of the page body.

Structured Glossaries and Defense-Industry Terminology

The defense software sector is heavily reliant on specialized acronyms, compliance frameworks, and open architecture standards. Government procurement officers and defense prime contractors utilize AI agents to map supplier capabilities against specific Department of Defense mandates. If the site's content does not structurally align with these frameworks, it will be omitted from the retrieval set during automated vendor down-selection.

Establishing Canonical Terminology with DefinedTermSet

To manage complex, industry-specific terminology, the site must implement structured glossaries utilizing the DefinedTermSet and DefinedTerm schema types2. This semantic markup tells search engines and LLMs, "This page is the authoritative definition of a named entity within our organizational context"27. When the site mentions critical frameworks such as the Modular Open Systems Approach (MOSA), the Sensor Open Systems Architecture (SOSA), or the Future Airborne Capability Environment (FACE), these should not exist merely as raw text acronyms. They must be mapped using DefinedTerm to explicitly declare the organization's alignment \[cite: 104-121\].

• MOSA (Modular Open Systems Approach): A DoD-mandated acquisition and design strategy required by United States law for new military systems. It emphasizes open standards, severable modules, and highly cohesive system structures to prevent vendor lock-in and facilitate technology refresh28.
• SOSA (Sensor Open Systems Architecture): An Open Group technical standard focusing on hardware and interface interoperability, software portability, and modularity specifically for C5ISR (Command, Control, Communications, Computers, Cyber, Intelligence, Surveillance, and Reconnaissance) sensor systems31.
• FACE (Future Airborne Capability Environment): An open avionics environment for military airborne platforms, establishing guidelines for software portability, interoperability, and reusability across different airborne systems34.

By structuring a glossary that explicitly defines how the organization's software architecture aligns with SOSA electrical/mechanical profiles or FACE software conformance, the site captures high-value semantic real estate. When a machine research agent queries, "Which defense software engineering firms provide SOSA-aligned autonomy simulation?", the presence of deeply structured DefinedTerm data allows the agent to extract the site as a primary, definitive source, recognizing the site's nuanced understanding of the open standards ecosystem32.

Compliance Frameworks as Machine-Verifiable Trust Signals

In the B2G space, compliance is synonymous with trust and eligibility. AI agents evaluating defense contractors heavily weight references to the Cybersecurity Maturity Model Certification (CMMC), NIST SP 800-171, and the Defense Federal Acquisition Regulation Supplement (DFARS)36. NIST SP 800-171 governs the protection of Controlled Unclassified Information (CUI) and serves as the technical backbone for CMMC Level 237. DFARS 252.204-7012 mandates these requirements for all DoD contractors and subcontractors37. The site must weave these compliance markers seamlessly into both the human-readable text and the machine-readable Organization schema. Dedicating specific \<section\> blocks (complete with semantic HTML and appropriate schema) to the firm's NIST 800-171 posture and CMMC readiness signals to AI evaluators that the organization operates strictly within the legal and regulatory strictures of the defense industrial base. This further distances the organization from unregulated consumer technology startups and explicitly defines its operational theater37.

Machine-Readable Discovery Conventions: The llms.txt (v2) Specification

One of the most significant architectural developments in AI discovery for 2026 is the rapid maturation of the llms.txt convention. Originally proposed in September 2024 by Jeremy Howard of Answer.AI, llms.txt is a plain-text Markdown file served at the root path of a website (e.g., https://example.com/llms.txt). It functions as a highly curated, machine-friendly table of contents, directing LLMs to the most critical, high-signal pages without forcing them to expend a render budget on parsing bloated HTML navigation, JavaScript, or CSS40.

Separating Standard from Speculation

It is vital to separate formalized standards from community conventions and industry speculation. As of mid-2026, llms.txt is not an official standard backed by the IETF, W3C, or any recognized standards body41. Furthermore, Google has explicitly stated that it does not use llms.txt for Google Search or its AI Overviews, rendering it irrelevant for traditional SEO40. However, for agentic discovery—specifically AI coding assistants (like Cursor and GitHub Copilot), autonomous research agents, and custom RAG pipelines utilized by enterprise and government entities—llms.txt is highly valuable43. A 2026 industry analysis found an adoption rate of roughly 10% across general domains, but over 50% among technology-oriented and documentation-heavy sites41. It is a high-yield, low-cost implementation for B2B/B2G technical sites.

The Shift to the August 2026 v2 Specification

While the original v1 specification merely provided a list of links, the August 10, 2026, version 2 (v2) update transformed llms.txt into a comprehensive discovery protocol44. The v2 specification requires a fundamental shift in how documentation and technical specifications are served. It introduces explicit link relations that must be embedded in the HTML: rel="alternate" type="text/markdown" and rel="describedby"44. Under the v2 protocol, a defense software site must not only maintain the llms.txt index at the root but also ensure that every critical technical page (e.g., API documentation, autonomy architecture overviews, MOSA alignment matrices) is available as a clean Markdown file (.md)44. The HTML version of the page must contain a \<link\> element in its \<head\> pointing to its Markdown twin. For example: \<link rel="alternate" type="text/markdown" href="https://example.com/autonomy-architecture.md"\>44. When an AI evaluation agent or a defense procurement RAG pipeline hits the HTML page, the rel="alternate" tag immediately redirects the machine to the .md file. Markdown is the native lingua franca of LLMs. By providing pre-parsed, raw Markdown, the site eliminates HTML tag noise, drastically lowers the token footprint, and ensures that complex architectural concepts are ingested flawlessly into the model's finite context window40.

Structuring the llms.txt Payload and Negative Constraints

The llms.txt file itself must adhere strictly to CommonMark standard Markdown. The structure must include:

1. H1 (Required): The project or organization name1.

2. Blockquote Summary: A concise definition of the organization. This is a critical disambiguation point. It must explicitly utilize negative constraints to prevent hallucination: "\> \[Organization\] provides software engineering, autonomy architecture, and integration for authorized government and defense industry customers. We do not sell consumer products or weaponry."40.

3. H2 Groupings: Link lists categorized by logical functions (e.g., \#\# Architecture Specifications, \#\# Compliance & NIST 800-171, \#\# Integration API). Each link must follow the format: \Page Title\: Brief one-line description.1.

4. Optional Section: A specific \#\# Optional heading allows the LLM to deterministically drop secondary content if it is running out of context window space1.

For deep ingestion scenarios, the site may also provide a companion llms-full.txt file. This file concatenates the complete Markdown body of every critical page into a single document41. This allows an AI agent to ingest the entirety of the firm's technical documentation in a single network pass. However, strict attention must be paid to token counts; llms-full.txt should be kept under 50,000 tokens to avoid context overload, making it suitable for targeted technical documentation rather than the entire corporate website43.

Evidence Provenance, Software Supply Chain, and Manifests

In the defense sector, claims of software assurance, security, and autonomy architecture cannot merely be asserted; they must be backed by verifiable provenance. AI answer engines are increasingly programmed to seek out primary source evidence and structured metadata to combat hallucination and verify factual claims14.

Software Bill of Materials (SBOM) Integration

A critical trust signal for government software in 2026 is the presence and machine-readability of a Software Bill of Materials (SBOM)47. By publishing metadata regarding the software supply chain, the organization proves transparency and compliance with federal cybersecurity mandates. Two dominant standards dictate SBOM formatting: SPDX (Software Package Data Exchange) and CycloneDX (an OWASP standard providing advanced supply chain capabilities for cyber risk reduction)47. Both formats support JSON serialization49. By embedding links to SPDX or CycloneDX JSON files within the SoftwareApplication schema or the llms.txt file, the site allows autonomous machine research agents to independently verify the software's component integrity, license compliance, and vulnerability posture without human intervention47. This machine-verifiable evidence elevates the organization's topical authority and trustworthiness exponentially in the eyes of defense-focused AI evaluators.

Release Manifests, TechArticle, and SoftwareSourceCode Schema

For technical pages featuring code examples, simulation APIs, or integration scripts, the SoftwareSourceCode schema must be deployed9. This schema allows the organization to tag the programming language and the relationship between the code snippet and the runtime environment, providing crucial context to AI coding assistants9. When publishing architectural whitepapers or engineering documentation, the TechArticle schema is the preferred type. It explicitly tells the model the page is a technical document containing steps or examples, rather than a generic blog post9. Furthermore, maintaining structured release manifests—documenting updates, patches, and version histories with precise dates—feeds directly into the dateModified properties of these schemas. LLMs utilize dateModified as a primary recency signal9. Stale documentation is frequently down-weighted or discarded by retrieval engines. Ensuring that release notes and technical articles are timestamped with full ISO 8601 formatting (including the timezone offset, e.g., 2026-09-07T17:54:59-05:00) prevents parsers from dropping ambiguous dates and ensures the AI recognizes the software as actively maintained9.

Content Formulation, Topical Authority, and Duplication

The final layer of discovery architecture involves the formulation of the prose itself and how it is constructed to interface with retrieval mechanics. Writing for AI search in 2026 requires abandoning fluid, meandering marketing copy in favor of dense, fact-heavy, highly structured knowledge transmission.

The Information Density Threshold and Direct-Answer Content

AI systems favor text that provides immediate, unambiguous answers. Content must be designed for extraction. A prevailing standard in 2026 is the "one-fact-per-200-words" rule, wherein every semantic chunk of text contains specific, verifiable data—such as a specific technical metric (e.g., latency \< 50ms), a reference to a defense standard (e.g., SOSA Technical Standard Edition 1.0), or a specific year of compliance15. Vague claims lacking quantifiable backing are assigned low weight by LLMs15. When structuring content, the human-readable text should follow an "answer-first" inverted pyramid structure: state the direct response to a query in the first sentence, unencumbered by introductory fluff, followed by the supporting technical context43. This structural approach perfectly mirrors the chunking strategies used by vector databases during data ingestion, ensuring the core answer is embedded cleanly without semantic noise.

Managing Content Duplication, Citations, and Canonicalization

AI crawlers are hypersensitive to duplicate content. If a model encounters three variations of the same architectural whitepaper across different URLs (or syndicated across different domains), it wastes the crawler's token budget and severely penalizes the domain's overarching topical authority due to perceived redundancy14. Flawless canonicalization is non-negotiable. Every page must contain a self-referencing \<link rel="canonical"\> tag, and parameterized URLs must be strictly managed to prevent index bloat14. Furthermore, outbound citations and evidence provenance must be explicitly coded using the isBasedOn and citation schema properties14. When the site publishes analysis on DoD mandates, utilizing isBasedOn to link directly to the official .mil or .gov source proves evidence provenance. It links the site's proprietary analysis back to authoritative government sources, establishing a verifiable chain of custody for the information. This elevates the E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) signals, which are heavily relied upon by AI citation networks to determine which source to ultimately quote in a generated answer2.

Second and Third-Order Strategic Implications

The synthesis of these technical implementations yields effects that compound well beyond basic discoverability, reshaping how the defense software provider interacts with the broader procurement ecosystem.

1. The Ontological Moat: By strictly defining entities with Wikidata Q-IDs, NAICS codes, and GovernmentAudience schemas, the organization builds a highly defensible "ontological moat." It effectively trains the foundation models to recognize that any query regarding the firm must be processed through the semantic vector space of the "defense industrial base," rather than the highly crowded, hallucination-prone vector space of "generative AI startups." This absolute isolation drastically reduces the risk of reputational damage caused by AI miscategorization, ensuring that brand safety algorithms never confuse the firm's simulation capabilities with unauthorized weapons distribution.

2. Procurement Agent Automation: As the DoD and prime defense contractors increasingly deploy autonomous AI agents to conduct market research and vendor capability assessments, the implementation of llms.txt v2 and JSON SBOMs creates a frictionless procurement pathway. An autonomous agent can ingest the firm's entire technological capability, verify its NIST 800-171 compliance, and confirm its SOSA alignment in milliseconds, without requiring human intervention. Vendors lacking this machine-readable infrastructure will simply be bypassed in the initial automated down-selection phases, as the cost of extraction for legacy HTML sites will be deemed too high by the agentic systems.

3. The Feedback Loop of Topical Authority: Clean semantic HTML and robust JSON-LD reduce the computational cost for LLMs to crawl and parse the site. Lower processing costs lead to higher crawl frequencies and deeper indexing. This, combined with high-density, fact-based content and structured glossaries (DefinedTermSet), ensures the organization becomes a primary source node in the AI's knowledge graph. Over time, the AI engines begin to preferentially cite the site not just for its specific products, but for general definitions of defense autonomy architecture, thereby cementing absolute topical authority and market dominance in the B2G digital theater.

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50. Understanding SBOM Standards: A Look at CycloneDX, SPDX, and, https://www.aikido.dev/blog/understanding-sbom-standards-a-look-at-cyclonedx-spdx-and-swid

51. CycloneDX Tool Center, https://cyclonedx.org/tool-center/

52. SBOM Standards: Complete Guide to Software Bill of Materials, https://www.kiuwan.com/blog/sbom-standards/