Traditional cryptographic audits rely on manual review — security teams checking configuration files, interviewing developers, and reading documentation. This approach misses the vast majority of cryptographic dependencies. In our experience conducting crypto assessments across dozens of enterprise environments, manual audits typically discover less than 30% of actual cryptographic usage. The remaining 70% is hidden in places nobody thinks to look.
The Discovery Gap
In a typical enterprise environment, cryptography is everywhere — and almost none of it is documented. The obvious places (TLS certificates, VPN configurations, database encryption settings) represent just the tip of the iceberg. Below the surface:
- Third-party libraries: Your application might use OpenSSL, BoringSSL, or a language-specific crypto library through 3-4 layers of dependency. A single npm package can pull in cryptographic code that your developers never consciously chose to use.
- Embedded firmware: IoT devices, network appliances, and hardware security modules contain cryptographic implementations that are invisible to application-level audits.
- API integrations: Every SaaS product you connect to has its own cryptographic configuration. Your data is only as secure as the weakest link in the chain.
- Legacy systems: That Java application from 2012 that nobody wants to touch? It's probably using MD5 somewhere. Legacy systems accumulate cryptographic debt silently.
- Infrastructure-as-Code: Terraform modules, Kubernetes secrets, CI/CD pipeline configurations — all of these contain cryptographic parameters that are rarely audited.
Automated Scanning Approaches
Modern crypto discovery tools can scan multiple layers of your infrastructure simultaneously to build a comprehensive cryptographic inventory. Here's how we approach automated discovery:
Source Code Analysis
Static analysis tools scan your codebase for cryptographic API calls, hardcoded keys, algorithm identifiers, and known vulnerable patterns. This catches not just direct crypto usage but also indirect usage through wrapper libraries and frameworks. We scan across all languages in your stack — Java, Python, Go, JavaScript, C/C++, Rust — because each ecosystem has its own crypto idioms and common pitfalls.
Binary and Artifact Analysis
Not all code is available as source. Compiled binaries, container images, and vendor-supplied software need to be analyzed differently. Binary analysis tools can identify compiled-in cryptographic libraries, extract certificate bundles, and detect algorithm usage patterns even without source code access.
Network Traffic Analysis
Passive network monitoring captures the cryptographic protocols and algorithms actually being negotiated in production traffic. This reveals the gap between what you think you're running and what's actually happening on the wire. It's common to discover TLS 1.0 connections, weak cipher suites, and expired certificates that configuration audits missed.
Infrastructure Configuration Scanning
Cloud configurations, container orchestration manifests, reverse proxy settings, and load balancer rules all contain cryptographic parameters. Automated scanners parse these configurations to identify algorithm choices, key lengths, certificate bindings, and protocol versions across your entire infrastructure footprint.
From Inventory to Action
A cryptographic inventory is only valuable if it drives action. We help organizations prioritize their findings into three categories:
- Critical: Known-vulnerable algorithms (MD5, SHA-1 for signing, DES, RC4) that should be remediated immediately regardless of PQC timelines.
- High priority: Quantum-vulnerable algorithms protecting sensitive, long-lived data that should be migrated to PQC or hybrid schemes as soon as feasible.
- Standard priority: Quantum-vulnerable algorithms protecting short-lived or low-sensitivity data that can be migrated on a normal upgrade cycle.