Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The U.S. Department of Defense (DoD) introduced its Cybersecurity Maturity Model Certification (CMMC) program in early 2020 to strengthen cybersecurity across the Defense Industrial Base (DIB) and ensure that contractors handling Controlled Unclassified Information (CUI) meet strict cybersecurity standards defined by the National Institute of Standards and Technology (NIST).

Security teams are at a critical inflection point. AI-enabled adversaries now operate at machine speed, automating phases of the kill chain and scaling attacks faster than human-only workflows can respond. Yet most SOCs still depend on manual triage and investigation processes that cannot keep pace.

Organizations across finance, healthcare, retail, and especially AI-driven sectors are facing increasing pressure from global regulators. The rapid expansion of AI, the growth of cross-border data flows, and the rise of new privacy frameworks all contribute to a landscape that demands more structure and accountability. In this environment, regulatory compliance and data tokenization are becoming inseparable.

A recent Dark Reading article highlighted a sobering shift in how nation-state threat actors are gaining access to critical infrastructure. According to reporting on a new Amazon Threat Intelligence disclosure, Russian actors affiliated with the GRU have spent years refining a campaign that increasingly bypasses traditional vulnerability exploitation altogether. Instead, they are walking straight through the front door left open by misconfigured network edge devices.

Large data breaches rarely begin with dramatic system failures. More often, they start with sustained, unauthorized access to sensitive data that goes undetected for months. The recent breach at Coupang, South Korea’s largest e-commerce platform, illustrates this pattern clearly. Nearly 34 million customer records were likely exposed over an extended period before detection.

2025 exposed a shift that had been forming for years. Security operations were not slowed by limited visibility or weak tooling. They were slowed because the effort required to interpret growing volumes of data increased faster than staffing, budgets, or governance frameworks could support. Alert queues expanded, dashboards multiplied, cloud bills shaped retention choices, and AI arrived before most organizations had clear policies to supervise it. It was not a talent problem.