As leaders, we cannot apply a legacy budgeting model to a machine-speed paradigm. To protect the business, preserve insurability and ensure continuity, we must fundamentally rethink how and where we deploy our cybersecurity capital.
The core problem: The economics of asymmetry
To understand the necessary budget shift, we have to look at the math. The Mythos capability isn’t just fast; it possesses agentic reasoning. Threat actors are no longer manually probing our networks; they are using autonomous AI agents to stitch together low-level bugs into critical exploits in hours, not months.
This asymmetry creates a crushing economic burden on our side of the ledger. Historically, a standard enterprise team of 100 software engineers could conservatively spend about 17,700 hours per year triaging code and addressing bad-code issues – a baseline direct labor cost of roughly $708,000 at a conservative US blended $40 hourly rate. In the era of frontier AI models such as Mythos, that hidden labor pool becomes a strategic budget parameter. That’s because AI may accelerate discovery, but enterprises still need the skills of expert technical talent to validate, prioritize and remediate what AI finds.
