Quantum computing poses rapidly escalating challenges to many of the public-key cryptographic algorithms currently in use: RSA, ECC, and DSA. Many of the classical public key cryptographic algorithms secure everything that deals with financial transactions, software updates, identity verification, and data encryption.

The National Institute of Standards and Technology (NIST) is a U.S. government agency that develops and promotes standards, guidelines, and best practices to enhance information security and privacy. Recognized globally, NIST frameworks set the benchmark for building secure systems and managing cybersecurity risks.

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.

It’s estimated that cybercrime will cost the world 10.5 trillion annually by 2025. In this digital age, cybersecurity has become an incredibly important factor for almost every business around the globe. Most modern businesses operate online to some degree, and this often involves handling sensitive data. Cybercriminals are always looking for new ways to exploit systems and networks, so keeping data safe must be a priority.

The AI space is developing rapidly but is still largely uncontrolled. According to The State of Trust Report 2024, 62% businesses plan to invest more in AI security in the next 12 months. ‍ The good news is that AI security can now be better implemented with the help of many authoritative new AI standards and frameworks rolled out in the past few years. The aim with any of these standards is to remove the uncertainty around AI systems and ensure responsible implementation.

The NIST AI Risk Management Framework (RMF) is one of the most advanced, globally accepted guidelines for the safe and responsible use of AI systems. If your organization implements AI in any capacity, adopting the NIST AI RMF can be a significant move toward future-proofing your operations and strengthening AI trustworthiness among customers.

According to a recent Gartner report, 88% of boards view cybersecurity as a business risk, not just an IT issue, underscoring the critical need for organizations to adopt robust, scalable frameworks to manage cybersecurity risks. In today’s rapidly evolving threat landscape, frameworks like the NIST Cybersecurity Framework (CSF) are pivotal for safeguarding organizations from vulnerabilities while maintaining alignment with business objectives.

We have seen how cyber attacks have disrupted organisations and businesses repeatedly. Mitigating emerging threats is crucial more than ever, and many organisations are at the forefront of combating them. One such organisation is the National Institute of Standards and Technology (NIST). NIST has released many Special Publications (SP) regulations, each containing guidelines for improving organisations’ security postures.

Zero trust has become one of modern security’s most prominent strategies. Zero trust architecture is based on the fundamental idea that every network, user, and system must be verified consistently, instead of granting trust based on past access. Although zero trust is a commonly accepted practice today, it’s important to understand the pivotal role that the National Institute of Standards and Technology (NIST) plays in defining zero trust architecture and other cybersecurity frameworks.

Businesses are turning to structured cybersecurity approaches like the NIST Cybersecurity Framework (CSF) to protect data from breaches. Especially since the latter’s cost is expected to reach $10.5 million in 2025. The CSF’s core functions (Govern, Identify, Protect, Detect, Respond, Recover) can help mitigate digital risks.