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SOC 2 & NIST CSF in Enterprise Security — Part 2: Challenges & Governance Best Practices

Jason J. Boderebe
7 min read
#soc2 #nist-csf #risk-management #compliance #governance #cybersecurity #enterprise-security
SOC 2 & NIST CSF in Enterprise Security — Part 2: Challenges & Governance Best Practices

Welcome Back!

In Part 1, the core implementation strategies for SOC 2 and NIST CSF were covered — including the Trust Service Criteria, the six NIST CSF functions, how both frameworks map together, and the role of cryptographic controls. At this point, the foundation of both frameworks is in place.

What hasn’t been addressed yet is how organizations actually hold up over time. Passing an audit is one thing — staying compliant, managing third-party risk, and governing AI-assisted tools in the process is another challenge entirely.

This part covers the real compliance challenges organizations face and the best practices for building a governance model that holds up under pressure.

Challenges in Compliance & Risk Management

SOC 2 Compliance Challenges

Working with SOC 2 has shown that even though the framework is clear, actually maintaining compliance over time isn’t always easy. One major challenge is that SOC 2 audits are point-in-time, meaning organizations can pass an audit but still fall short in day-to-day operations. To stay compliant, companies must commit to ongoing monitoring, which can be tough if they don’t already have mature processes.

There’s also the challenge of managing third-party risk. SOC 2 expects organizations to make sure their vendors are secure, too — but with so many external services involved, keeping track of who meets what standards takes serious effort.

For smaller enterprises, the cost and workload of preparing for SOC 2 compliance is another obstacle. Building out documentation, training staff, and preparing for an audit takes time and resources that not every business has. This is especially true for those going through the process for the first time.

NIST CSF Adoption Challenges

NIST CSF is flexible by design, but that flexibility also creates challenges. Unlike SOC 2, NIST doesn’t give a checklist — it’s more of a guide, so organizations have to do a lot of interpretation and customization themselves. That can slow down implementation, especially for teams without dedicated security staff.

Another issue is that there’s no official certification for NIST CSF, which makes it harder for companies to prove progress to leadership or customers. Because of this, some may not invest the same effort into using the framework unless they’re already motivated by internal goals or sector expectations.

Even when companies do implement it, measuring success isn’t straightforward. CSF helps build structure, especially with the new Govern function in version 2.0, but many still struggle with tracking improvements or showing measurable security.

AI-Specific Challenges in Compliance & Risk Management (NIST AI RMF 600-1)

As organizations start using AI tools to support broader business operations, including but not limited to cybersecurity and compliance efforts, new risks are beginning to surface. One major concern is accuracy. Generative AI systems, including large language models (LLMs), can produce content that seems reliable but may contain hallucinated or misleading information. If this output is used in audit preparation or risk documentation, it can create gaps in accountability and trust.

Another challenge is bias and fairness. AI systems are only as good as the data they are trained on. If those datasets are narrow or flawed, it can lead to unbalanced risk assessments or decision-making that overlooks important context. According to NIST, addressing this means focusing on validity, robustness, and reliability throughout the AI lifecycle.

The issue of governance is just as important. AI introduces new questions around who is responsible for outputs, especially when decisions are automated. NIST emphasizes the need for human oversight, recommending organizations maintain clear roles, document how AI is used, and implement risk mitigation strategies that account for both intended and unintended outcomes.

Lastly, there’s a lack of regulation and industry standards for how AI tools should be governed in security and compliance settings. This creates uncertainty. Companies may adopt AI before fully understanding how it will be regulated, potentially leading to long-term compliance risks if policies shift or new requirements are introduced.

Security Governance & Compliance Best Practices

Building a Governance Model That Aligns SOC 2 & NIST CSF

Creating a robust governance model means clearly defining security operations’ roles, responsibilities, and oversight. SOC 2 helps organizations establish these foundations by requiring documented policies for risk management, incident response, and access control. Meanwhile, NIST CSF supports governance through its “Govern” function, introduced in version 2.0, which emphasizes aligning security activities with business priorities and stakeholder expectations.

One important best practice is adopting a risk-based approach. This means organizations should evaluate threats based on their likelihood and impact and allocate resources accordingly. Both SOC 2 and NIST CSF encourage this mindset, helping teams move from a reactive stance to a more strategic and preventive one.

Additionally, communication plays a significant role in governance. Teams need a process for reporting on risk posture and security metrics to executives and board members. This not only improves visibility but also drives accountability across departments.

Leveraging Technology for Compliance & Risk Management

Technology plays an important role in helping organizations maintain compliance and improve their overall security posture. With frameworks like SOC 2 and NIST CSF, manual tracking and documentation can quickly become overwhelming, especially when teams must show evidence of controls, audit trails, or continuous monitoring. That’s where automation tools and centralized platforms start to make a difference.

For example, companies often use compliance automation software to streamline document collection, map security controls, and monitor real-time status. This reduces human error and saves time during audit preparation. Platforms that align with SOC 2 or NIST CSF can flag gaps, assign remediation tasks, and track compliance maturity over time.

In terms of monitoring, tools like SIEMs (Security Information and Event Management) and GRC (Governance, Risk, and Compliance) solutions help teams detect, respond to, and log security incidents. When integrated correctly, these tools offer more visibility and support the “Detect” and “Respond” functions in NIST CSF.

Technology should complement, not replace, security policies and human oversight. Automating a flawed process won’t make it more secure. The most effective approach combines solid governance structures with well-implemented technical tools, ensuring both compliance and operational resilience.

Integrating Cryptographic Best Practices

Cryptography plays a foundational role in securing data, especially when working toward SOC 2 and NIST CSF compliance. These frameworks expect organizations to use encryption and key management practices that protect sensitive data both in transit and at rest. For example, the Confidentiality and Security criteria in SOC 2 directly address the need to safeguard customer information through encryption and access controls.

NIST CSF also emphasizes cryptographic controls under its “Protect” function, encouraging the use of encryption standards that align with organizational needs and regulatory obligations. When combined with guidelines from NIST SP 800-57, which covers key lifecycle management, organizations can take a more structured approach to handling cryptographic materials.

One best practice is to ensure that encryption algorithms and protocols stay up-to-date with current standards. Outdated or unsupported cryptography poses real risks, especially in industries like finance and healthcare. Another critical area is managing cryptographic keys — ensuring they are rotated regularly, stored securely, and backed up according to policy.

Integrating these practices into the broader governance model strengthens security posture and reinforces compliance. When implemented properly, cryptography isn’t just a technical layer — it becomes a strategic asset in maintaining trust and protecting data.

Stay Curious!

Continue reading: Part 3 — AI Governance, Recommendations & Conclusions

References

  1. American Institute of Certified Public Accountants (AICPA). (2017). 2017 Trust Services Criteria (With Revised Points of Focus – 2022)
  2. National Institute of Standards and Technology (NIST). (2024). The NIST Cybersecurity Framework (CSF) 2.0
  3. National Institute of Standards and Technology (NIST). (2024). Artificial Intelligence Risk Management Framework: Generative AI Profile (AI RMF 600-1)
  4. Carbide Secure. Case Study: How Managing Life Achieved SOC 2 Compliance
  5. Withum. Case Study: Building Trust in Fintech - How SOC 2 Compliance Enhanced Client Confidence
  6. UnderDefense. Case Study: NIST Cybersecurity Framework Assessment
  7. Intel. The Cybersecurity Framework in Action: An Intel Use Case
  8. Armis. NIST Cybersecurity Framework Examples and Best Practices
  9. National Institute of Standards and Technology (NIST). (2016). NIST Special Publication 800-57 Part 1: Recommendation for Key Management