Common Discovery Challenges

1. Scope and visibility are inherently incomplete

The first challenge is deciding what “in scope” means. Cryptography may occur in hardware and software modules, libraries, embedded code, network protocols, applications, firmware, software updates, and CI/CD dependencies. It can also support key establishment, key management, and protection of data at rest, in transit, and in use. An inventory that covers only servers or application source therefore risks omitting relevant uses. The joint CISA, NSA, and NIST fact sheet specifically cautions that organizations may be unaware of the breadth of application and functional dependencies on public-key cryptography in deployed products, applications, and services.1

A practical mitigation is to define scope as a coverage model rather than a single scan: enumerate business services, hardware, software, services, network flows, repositories, endpoints, CI/CD systems, certificate and key-management systems, and suppliers. Correlate discovery with existing asset, identity and access, endpoint, and continuous-diagnostics inventories. Record the source, time, confidence, and known exclusions for every result. This creates a defensible basis for risk decisions, but it does not eliminate unknown assets, undocumented uses, or systems outside organizational control.12

2. Shadow, ephemeral, and opaque assets

Shadow assets include unmanaged applications, personal or separately administered devices, supplier services, cloud-based components, and products whose internal implementation is not exposed to the customer. Ephemeral assets add another difficulty: containers, short-lived workloads, build artifacts, and dynamically created services may exist only during a deployment or transaction. A periodic scan can miss an asset that was absent when the scan ran. Managed applications may also be governed through mobile application-management systems, while unmanaged applications are outside that control boundary.45

Use inventory integration and event-driven collection where available: ingest deployment, registration, procurement, endpoint, service, and CI/CD records; retain observations after an asset disappears; and associate short-lived instances with a durable application, service, owner, and environment. Require suppliers to disclose relevant cryptography and request component information when embedded cryptography cannot be identified by discovery tools. The mitigation improves continuity and accountability, but it depends on trustworthy registration, supplier cooperation, and retention of historical evidence.1

3. Abstraction layers, unavailable source, and difficult binaries

Cryptographic operations may be hidden behind APIs, libraries, frameworks, service meshes, container platforms, firmware, or vendor-managed components. Static analysis can examine source, bytecode, or binary code, but the available representation may be incomplete. Stripped, packed, obfuscated, generated, or proprietary binaries can make algorithm and usage inference difficult. Embedded cryptography is an explicit discovery limitation identified in the joint fact sheet: tools may not identify cryptography used internally within products.6

Mitigate by combining source and binary analysis with runtime, configuration, dependency, protocol, vendor, and product documentation evidence. Ask vendors for lists of embedded cryptography and distinguish vendor statements from independently observed use. For abstraction layers, map the application or service to the cryptographic provider, policy, protocol, certificate, key, or algorithm where that relationship is observable. The residual limitation is that an interface call may reveal the provider but not the final algorithm or runtime path, while a vendor disclosure may be incomplete or change across versions.15

4. Encrypted traffic, managed services, and third-party dependencies

Encrypted traffic can conceal the endpoints, applications, and payload context that discovery teams need. In container environments, an encrypted overlay network may appear to existing tools as packets between hosts rather than traffic between actual container endpoints; this can create network “blindness.” Shared virtual networks can also make it difficult to associate traffic with a particular application or sensitivity level.7

A practical response is to combine network metadata with service, workload, certificate, endpoint, and configuration inventories, and to preserve application-level ownership and sensitivity labels when traffic is observed. Do not infer that an encrypted flow is harmless or that its algorithm is known solely from transport visibility. Protocol-specific evidence, endpoint inspection, configuration review, or provider documentation may be required. The limitation is that stronger encryption and managed network boundaries can intentionally restrict inspection, so collection must respect authorization and operational constraints.78

Third-party dependencies create a related gap. An SBOM can support risk-based decisions, but its default interpretation should be that data is incomplete when dependencies are not fully listed; essential dependencies may also be redacted. Require version-specific component information, identify known unknowns, request explanations for redactions, and correlate supplier services with the organization’s service and data inventories. This reduces uncertainty without treating an SBOM as proof that all transitive cryptographic dependencies are present.3

5. Ambiguous ownership, duplicates, stale data, and inaccurate findings

Discovery becomes operationally weak when a finding cannot be assigned to an accountable team. Ownership can be unclear across platform, application, infrastructure, procurement, supplier, and business-service boundaries. The CSF 2.0 calls for inventories of hardware, software, services, systems, supplier-provided services, and network communication or data flows, with assets prioritized according to importance and risk strategy.9

Mitigate by assigning a durable service or product owner, technical custodian, supplier, and risk approver; define escalation rules; and include ownership fields in the normalized inventory. Deduplicate by stable identifiers such as service, asset, component, version, location, certificate, key, or observed flow, while retaining separate observations and timestamps. Reconcile changes and corrections rather than overwriting history. CISA’s SBOM guidance likewise expects authors to correct errors promptly and organizations to accommodate updates.3

False positives arise when a weakness is reported although the conditions needed for it do not exist; the NIST preliminary draft notes that weaknesses may manifest only under certain conditions, creating the potential for many false positives. False negatives arise when a tool cannot see embedded, unavailable, opaque, ephemeral, or otherwise hidden cryptography. Validate important findings through independent evidence, document the reason for dismissal or acceptance, and treat “no finding” as bounded by the method and scope.21

6. Secret-handling, performance, and legacy constraints

Discovery can expose sensitive material such as private keys, credentials, key metadata, certificates, configuration values, or proprietary code. The mitigation is to collect the minimum necessary evidence, avoid copying secret values into ordinary inventory records, restrict access, authenticate users, log administrative activity, and separate discovery metadata from secret custody. NIST’s key-management guidance emphasizes authorization, authentication, constrained permissions, inventory of keys and certificates, responsibility assignment, and auditing.10

Performance and availability are practical constraints. Deep inspection, binary analysis, runtime instrumentation, or broad network collection can consume resources, disrupt sensitive systems, or be unsuitable for operational technology and safety-critical environments. Prioritize collection by criticality, use maintenance windows and representative sampling where necessary, and coordinate with system owners. Record what was not inspected and why. This reduces operational impact, but sampling and exclusions necessarily leave residual uncertainty.5

Legacy systems amplify every preceding challenge: source may be unavailable, ownership may have changed, dependencies may be undocumented, interfaces may be proprietary, and replacement may take years or decades. Treat legacy discovery as a staged risk activity: identify the system and data, determine where public-key or other relevant cryptography is used, document constraints and dependencies, and establish compensating or transitional actions. The NIST preliminary draft describes migration as logistically challenging and potentially taking years or decades, so discovery should support an evolving roadmap rather than a one-time certification.2

7. Data normalization and remediation handoff

Different discovery methods produce different vocabularies, identifiers, confidence levels, and granularity. One source may identify an algorithm, another a certificate, another a service, and another only a host or dependency. Normalize records into a common model that links the cryptographic use to an asset, application or service, data sensitivity, environment, owner, supplier, evidence source, observation time, algorithm or protocol when known, and uncertainty state. Preserve distinctions between observed facts, inferred relationships, vendor assertions, and unresolved questions.21

A useful handoff includes a risk-based priority, affected data and protection lifetime, accountable owner, proposed action, temporary mitigation if needed, dependencies, acceptance authority, target date, and verification evidence. The SSDF recommends risk-based decisions about remediation, risk acceptance, or risk transference, and planning temporary mitigations when a permanent mitigation is unavailable. The CSF 2.0 also calls for standardized methods to calculate, document, categorize, and prioritize risk, plus communication across the organization and with suppliers.119

Conclusion

Common cryptographic discovery challenges are not solved by selecting a single scanner. Scope gaps, hidden and short-lived assets, abstraction, unavailable source, encrypted traffic, suppliers, uncertain ownership, data-quality problems, secrets, operational limits, and legacy constraints require complementary evidence and explicit uncertainty. A resilient program correlates automated discovery with asset, dependency, network, vendor, key-management, ownership, and risk information; validates and normalizes results; and routes prioritized actions to accountable owners. The resulting inventory is most useful when it records both what is known and what remains unknown.12119