Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

I spent nearly a decade in the U.S. Federal Government, including roles at the White House, the U.S. Department of Commerce, and the U.S. Senate. I later advised public sector clients on technology and strategic growth problems at Accenture. The same pattern showed up everywhere I went. Agencies invest in sophisticated network defenses.

AI agents have developed advanced capabilities faster than most would have imagined. In enterprise contexts, workforces are delegating more and more tasks to them. While the promise of increased productivity is enticing, the shift from deterministic automated tools to agentic autonomous systems introduces security risks that most enterprises haven’t prepared for.

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NIST’s recent update to the National Vulnerability Database (NVD) marks a turning point for enterprise vulnerability management teams. It’s not broken; it hit scale limits that NIST was forced to address. Now, every vulnerability management program built around it has a problem.

The latest National Institute of Standards and Technology (NIST) draft guidance on mobile driver’s licenses(mDLs) is about more than one use case or credential type. While the draft primarily focuses on the financial sector due to its high-assurance requirements, the bigger takeaway is that government-issued identity can be cryptographically verified and shared more selectively.

While inherently critical to today’s businesses that run on data, implementing and enforcing data security and privacy has never been straightforward. Between collecting different types of sensitive data and deploying unique architectures, organizations cannot adopt a one-size-fits-all solution, meaning that every security architecture is unique.

AI agents are likely already running inside your infrastructure. They triage alerts, remediate incidents, provision resources, and make decisions without waiting for a human to approve each step. For teams aligned to NIST’s Cybersecurity Framework (CSF) 2.0, this creates a problem: the framework assumes human actors, human-speed decisions, and human-readable audit trails. Autonomous systems break all three assumptions. The good news is that CSF 2.0 was designed to be adapted.

NIST CSF 2.0 expands the Cybersecurity Framework into a broader, risk-based model centered on governance, making leadership accountable for cybersecurity as an enterprise risk. It introduces a sixth core function, enhances supply chain and privacy integration, and improves usability for organizations of all sizes. Profiles, tiers, and new implementation resources help align security efforts with business objectives and evolving threat landscapes.

For many engineering and security teams, NIST SP 800-218 (Secure Software Development Framework, or SSDF) compliance feels like a hurdle that is too difficult to overcome. To meet these and other emerging regulations and be effective in today’s DevSecOps environment, organizations are moving toward codifying these standards into machine-readable rules, also known as Policy as Code (PaC).

NIST Special Publication 800-171 defines a precise set of security requirements for organizations that handle Controlled Unclassified Information (CUI) outside of federal systems. For defense contractors, subcontractors, and their engineering teams, these controls are non-negotiable with the advent of the Cybersecurity Maturity Model Certification (CMMC) program, which dictates how CUI must be accessed, logged, transmitted, and protected across every system in scope. That scope is shifting.