Beyond SCA: Generating SBOMs, CBOMs, QBOMs, and AIBOMs at Runtime

Your SBOM is only telling you half the story here’s what it’s missing

Somewhere in your production environment right now, code is running that your SBOM doesn’t capture. A library is loaded that never made it into your inventory. A cryptographic routine is executing without ever being audited. An AI model serves requests from an untracked registry. Meanwhile, your SCA tool the one behind that reassuring green dashboard has no visibility into any of it.

Here’s the uncomfortable reality: a BOM generated from source code or build artifacts is a snapshot of intent, not truth. It reflects what your software was supposed to contain at build time, not what’s running today and in 2026, that blind spot isn’t theoretical it’s exactly where breaches take hold.

The Comfortable Illusion of SCA

Software Composition Analysis tools are sophisticated, mature, and genuinely useful. They scan your repositories, parse your manifests, trace your dependency trees, and produce inventories that satisfy auditors and feed vulnerability dashboards. They’re a reasonable first step.

But here’s what they fundamentally cannot see: runtime.

SCA operates on static artifacts source code, lock files, container images at rest. It has no visibility into:

• What your application loads dynamically
• What libraries get pulled at execution time
• What cryptographic primitives get invoked in the hot path
• What AI model gets initialized when your inference endpoint receives its first request

This matters more than most CTOs realize. Studies consistently show that 30–40% of components active at runtime were never captured in static analysis not because the tools are broken, but because that’s simply not what they’re designed to do. Dynamic loading, plugin architectures, JIT compilation, sidecar injection, and remote model serving are invisible to a scanner that never watches your software actually run.

The question isn’t whether your SCA tool is good. The question is: what is it missing, and how much of that missing surface area is exploitable?

Four BOMs, Four Blind Spots

The industry has moved well beyond the original SBOM mandate. Today, a complete software inventory picture requires four distinct bill-of-materials artifacts each capturing a different dimension of risk. And each one has its own critical blind spot when generated purely from static analysis.

📦 Software Bill of Materials (SBOM)

The foundation. An SBOM catalogs the components, libraries, and dependencies in a piece of software. Under CycloneDX or SPDX formats, it traces lineage, licenses, versions, and known vulnerabilities.

The Static Blind Spot

Dependency pinning in manifests doesn’t mean those exact versions are what’s running in production. Containers get rebuilt and cached. Dependency resolution behaves differently at runtime than at build time. Side loaded plugins, dynamically linked libraries, and vendor-injected components never appear in your source tree but they absolutely appear in your process memory.

What Runtime Adds

Observing the actual process at execution time reveals every shared library loaded, every module initialized, every dependency resolved exactly as it runs not as it was built. The SBOM you get from runtime observation is frequently different from the one you get from your CI pipeline. That difference is your blind spot.

🔐 Cryptography Bill of Materials (CBOM)

The CBOM is an emerging standard (formalized under CycloneDX 1.6) that catalogs every cryptographic algorithm, protocol, key size, and cipher suite actively in use across your software. As quantum computing timelines compress and post-quantum migration mandates accelerate — from NIST’s PQC standards to CISA’s guidance the CBOM has gone from nice to have to a board level conversation.

The Static Blind Spot

Cryptographic libraries are referenced in manifests. But which algorithms are called, with which parameters, in which contexts? That you cannot see without watching the software run. A project may import OpenSSL but use only a handful of its hundreds of functions. Another may rely on an obscure elliptic curve chosen by a developer three years ago, buried inside a logging library, never reviewed, never updated.

What Runtime Adds

Tracing cryptographic function calls at the system level reveals the exact primitives in use: TLS versions negotiated, cipher suites selected, hash functions called, RSA key sizes passed. This is the only way to produce a CBOM that reflects reality and the only way to know which of your systems are genuinely post-quantum vulnerable versus which ones merely use a crypto library.

For any organization facing PQC migration requirements, a static CBOM is not a CBOM. It’s a guess.

⚛️ Quantum Bill of Materials (QBOM)

The QBOM builds on the CBOM to specifically assess quantum vulnerability — cataloging which cryptographic assets are at risk from Harvest Now, Decrypt Later attacks and which require priority migration to quantum-safe alternatives.

The Static Blind Spot

A static scan can tell you that your codebase references RSA-2048 somewhere. It cannot tell you whether that reference is in a code path that encrypts data with a 20-year retention requirement the kind of data that matters most to a nation-state threat actor harvesting today for decryption later.

What Runtime Adds

Runtime observation provides context that static analysis cannot. It can differentiate between a cryptographic reference that fires on every authenticated API call carrying sensitive data versus one that fires in an internal health check. Context determines quantum risk priority. Without it, your PQC migration roadmap is built on incomplete information and migration efforts are expensive enough that you cannot afford to get the prioritization wrong.

🤖 AI Bill of Materials (AIBOM)

The newest BOM category and, arguably, the one with the largest gap between what organizations think they know and what’s actually happening. An AIBOM catalogs AI and ML models, their provenance, training data lineage, fine tuning history, serving infrastructure, and the inference pipelines that wrap them.

The Static Blind Spot

AI models are not classical software dependencies. They’re not declared in a requirements.txt. They’re loaded from model registries, object storage, or remote endpoints often at inference time, often dynamically, often by ML engineers who operate in a separate workflow from the application teams who own the SBOM. The model a system is configured to use in version control may not be the model it’s serving under load.

What Runtime Adds

The only way to know what model is running is to observe the inference runtime directly to see what artifact gets loaded, from where, what version hash it carries, and what it’s been called with. This is especially critical as regulatory frameworks like the EU AI Act begin imposing transparency and auditability requirements on AI systems deployed in high risk applications. An AIBOM you can’t generate from runtime observation is an AIBOM you can’t trust.

Why “No Source Code” Is a Feature, Not a Workaround

There’s a common assumption that runtime analysis is a fallback something you do when you can’t get access to the source. That framing gets it exactly backward.

Consider the software that actually runs your business:

• Vendor-supplied binaries with no source available
• Commercial off the shelf applications
• Containerized workloads built from upstream base images maintained by teams you don’t control
• Legacy applications whose developers left years ago
• Acquired companies integrated into your environment before the M&A team thought to ask about SBOM practices

In real enterprises, a majority of running software has no available source code. SCA tools produce nothing useful for these workloads. The only reliable path to a complete, accurate, continuously updated inventory is to observe what’s running at the system call level, the process level, the network level and derive the BOM from observed behavior.

Runtime BOM generation without source code isn’t a workaround for edge cases. It’s the only method that works universally across your entire estate.

The Architecture of Runtime BOM Generation

Runtime-native BOM generation operates at a level below the application using kernel-level instrumentation such as eBPF probes, process inspection, and network flow analysis to observe software behavior without modifying it and without requiring any agent embedded in the application itself.

This approach produces BOMs that are:

The result is a living, accurate inventory of your entire software estate not a snapshot of what you intended to build, but a continuous record of what’s running.

What CTOs Should Be Asking

If you’re leading an engineering organization and your current BOM strategy is built entirely on SCA and build-time scanning, here are the questions worth sitting with:

• Do you know what’s running in production right now not what was deployed, but what’s loaded?
• Can you produce a CBOM that accurately reflects which cryptographic algorithms are active in your highest-risk systems?
• Do you know the provenance and version of every AI model serving requests in your environment at this moment?
• Can you generate compliant BOMs for the vendor and commercial software in your stack where you have no source access?

If the honest answer to any of these is “not really” you’re not alone. But you’re also not compliant, and you’re not protected.

Closing the Gap with IntelliNative

IntelliNative was built specifically to address this gap. It generates SBOMs, CBOMs, QBOMs, and AIBOMs directly from runtime observation No source code required, No agents embedded in applications, No dependency on build pipelines.

It works across your entire estate: cloud-native workloads, containerized services, vendor software, legacy applications, and AI inference infrastructure. It produces continuously updated, format compliant BOMs that integrate with the vulnerability management, compliance, and governance tools your teams already use.

The mandate to know what’s in your software is real and accelerating. The question is whether your BOM strategy is built to meet it or built to create the appearance of meeting it.

Your SCA tool is a strong start. IntelliNative is what makes it complete.

Get in Touch

• 📧 Email: contact@intellixbom.com
• 🌐 Contact form: www.intellixbom.com/contact

About IntelliXBOM: IntelliXBOM is a Software Bill of Materials intelligence platform built for engineering, security, and compliance teams who need more than a list. It generates accurate, standards-compliant SBOMs at every stage of the software development lifecycle and enriches them with license context, vulnerability data, and policy intelligence. Learn more at intellixbom.com.

Tags: SBOM · CBOM · QBOM · AIBOM · software supply chain security · SCA · runtime security · post-quantum cryptography · AI governance · CycloneDX · SPDX