Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As someone who has spent decades working at the intersection of cybersecurity, innovation, and operational resilience, I’ve seen firsthand how critical it is to build trust in a rapidly evolving digital landscape. At Fireblocks, that trust is not just a goal—it’s a standard we continuously push ourselves to meet and exceed.

As organizations increase their reliance on cloud services, remote work tools, IoT devices and smart infrastructures, and the use of third-party vendors, their exposure to cyber threats increases. Traditional approaches to vulnerability management are unable to keep up with rapidly changing business needs and an expanding attack surface. While scanning and patching known vulnerabilities remains critical, today’s complex threat landscape demands a more comprehensive strategy.

A lot goes into protecting the information security of the nation. The National Institute of Standards and Technology, NIST, maintains a list of security controls under the banner of NIST SP 800-53, Security and Privacy Controls for Information Systems and Organizations. Meanwhile, the Federal Risk and Authorization Management Program, or FedRAMP, sets up a framework that makes those security controls apply to governmental agencies and the third-party cloud service providers that work with them.

Just when CIOs and CISOs thought they were getting a grip on API security, AI came along and shook things up. In the past few years, a huge number of organizations have adopted AI, realizing innumerable productivity, operational, and efficiency benefits. However, they’re also having to deal with unprecedented API security challenges. Wallarm’s Annual 2025 API ThreatStats Report reveals a staggering 1,025% year-on-year increase in AI-related API vulnerabilities.

The explosion of AI has led to the creation of tools that make it more accessible, leading to more adoption and more numerous, less sophisticated users. As with cloud computing, that pattern of growth leads to misconfigurations and, ultimately, leaks. One vector for AI leakage is exposed Ollama APIs that allow access to running AI models. Those exposed APIs create potential information security problems for the models’ owners.