The telecommunications industry is considering one of its most consequential security debates in decades. Amid growing network complexity, 1 narrative argues that proprietary software is inherently more secure than its open source counterparts. Its central tenet is that the source code is not publicly accessible. The logic sounds intuitive: If attackers can’t read the code, they can’t find vulnerabilities. In the era of AI , that argument isn’t just outdated, it’s operationally dangerous. This blog challenges that narrative.
It looks at how AI has changed the economics of vulnerability discovery, why software opacity is no longer a reliable security strategy, and what service providers and network equipment providers (NEPs) must do to build genuine resilience across increasingly hybrid software environments. AI has changed how telecommunications providers analyze software. Today's AI reasoning systems can identify insecure logic and surface exploitable conditions without ever seeing the original source. The barrier that closed software has long relied on is dissolving.
This doesn’t infer proprietary software is bad, or that open source is automatically safer. The point is that AI can read a binary as fluently as a person reads documentation, meaning secrecy ceases to be a security strategy. What separates resilient service providers from exposed ones is no longer whether their code is visible. It’s how quickly they can detect, understand, validate, and fix weaknesses across every layer within their network. AI has rewritten the economics of vulnerability discovery.
Finding a vulnerability in a closed radio access network (RAN) or core software implementation once took a rare mix of skill and patience. The obstacles were real: A potential attacker would typically spend months or even years building the context needed to reason about a single subsystem. That inertia was the ultimate security model, as it made finding vulnerabilities time-consuming and expensive.