NIST AI Risk Management Framework: A simple guide to smarter AI governance
Private sector investment in AI topped $100 billion in 2024 in the U.S. alone, topping China’s $9.3 billion and the U.K.’s $4.5 billion. Organizations are racing to adopt AI tools as countries scramble to regulate them. Amidst the shuffle, the NIST AI Risk Management Framework (AI RMF) has emerged as a gold standard and valuable tool for complying with other leading regulations.
As PwC says, “Federal policies often shape corporate norms, especially in an area such as AI risk management, where many organizations have been seeking clarification on expectations at the federal level while sorting through a patchwork of state AI laws.”
Released in 2023 with updated iterations on the way, the NIST AI RMF is one such attempt at shaping corporate norms related to AI risk. Here, we’ll dig deeper into the framework, including:
- What the NIST AI Risk Management Framework is and its purpose
- Who needs NIST AI RMF, and who owns its adoption
- The evolution from NIST AI RMF 1.0 to 2.0
- The four key components of the NIST AI Risk Management Framework
- Common use cases by industry
- The pros and cons of implementing the framework
- Its role in the broader regulatory landscape
- How leveraging technology to comply can future-proof your AI risk management strategy
What is the NIST AI Risk Management Framework?
The NIST AI Risk Management Framework is a voluntary, widely recognized guide developed by the National Institute of Standards and Technology to help organizations manage risks throughout the artificial intelligence (AI) lifecycle. As the leading U.S. government-originated framework for AI risk management, it provides a structured, flexible approach to developing, deploying and using AI systems responsibly and effectively.
Purpose of the AI RMF
The primary goal of the AI RMF is to support trustworthy and responsible use of AI by helping organizations identify, assess, prioritize and manage risks. Rather than serving as a regulation, the framework offers practical tools and processes that can be adapted across industries to promote safer, more ethical AI practices.
According to PwC’s regulators’ take, “By calibrating governance to the level of risk posed by each use case, it enables institutions to innovate at speed while balancing the risks — accelerating AI adoption while maintaining appropriate safeguards.”
NIST AI risk management framework overview: Key pillars
The framework is grounded in principles that are essential for trustworthy AI:
- Transparency: Ensuring AI systems are understandable and their operations can be meaningfully explained to relevant stakeholders.
- Fairness: Actively addressing and mitigating bias to promote equitable outcomes across diverse populations.
- Accountability: Establishing clear roles, responsibilities and governance structures for managing AI risks.
- Robustness: Building AI systems that are secure, reliable and resilient against potential failures or adversarial threats.
Together, these pillars guide organizations in building AI systems that align with ethical standards and societal values, while effectively managing the complex risks that AI can introduce.
Why organizations need an AI risk framework like AI RMF
Global legislative mentions of AI have risen 21.3% across 75 countries since 2023, a ninefold increase since 2016. Both the public and private sectors have also continued to invest in AI at a breakneck pace, increasing the stakes for managing AI-related risks proactively and effectively.
Without a structured approach, risks tied to emerging technologies — unintended bias, security vulnerabilities, regulatory compliance and reputational harm — can quickly escalate.
- Rising regulatory scrutiny: Governments around the world are increasing oversight of AI. The European Union’s AI Act, executive orders in the U.S. and other emerging AI regulations signal that AI accountability is no longer optional. Organizations need to be prepared to demonstrate how they assess and manage the risks tied to AI systems.
- Managing complex, emerging risks: AI systems can behave unpredictably, rely on a “black box” of algorithms or amplify social biases, making traditional risk management approaches insufficient. A dedicated AI risk management framework helps organizations systematically identify, measure and mitigate these unique risks across the entire AI lifecycle, from development to deployment and ongoing monitoring.
- A globally recognized standard: The NIST AI Risk Management Framework has become a gold standard for AI governance, gaining traction not only among U.S. government agencies and contractors but also among private companies, international organizations and industry leaders worldwide. Its practical, adaptable approach makes it valuable for organizations of all sizes looking to build trustworthy, ethical and legally definable AI practices.
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Read nowWho needs the NIST AI Risk Management Framework?
The NIST AI Risk Management Framework is a critical resource for multiple functions across an organization, not just data scientists or AI engineers. Successfully managing AI risk requires cross-functional awareness, engagement and accountability at every level.
- General counsel and head of risk: With AI still in its infancy, many GCs and heads of risk lack the AI expertise to effectively integrate it into enterprise risk management (ERM) processes or justify investing in AI controls and oversight. The AI RMF gives them the tools to quickly identify where AI risk is concentrated and take practical steps toward managing and mitigating it compliantly.
- Risk managers and legal counsel: For the risk boots on the ground, uniting AI with fragmented, manual processes is a burden, as is getting visibility across all AI risks. The AI RM provides a structured approach to identifying, prioritizing and mitigating AI risks that could impact operations, compliance or reputation.
- Board members and audit committees: Boards are already overseeing AI tools, yet many don’t fully understand the associated risks, opportunities or regulatory implications. Familiarity with the AI RMF enables board members and audit committees to ask the right questions, evaluate risk exposure and fulfill their fiduciary responsibilities.
- Chief Information Security Officers (CISOs): CISOs must address the security vulnerabilities that can arise in AI systems, from adversarial attacks to data leakage. The AI RMF helps security leaders incorporate AI-specific considerations into cybersecurity strategies and incident response plans.
Who should own AI RMF adoption?
While many roles across an organization need to engage with the NIST AI Risk Management Framework, clear ownership is essential for successful adoption and sustained impact. Without defined accountability, AI risk management efforts can become fragmented or deprioritized, leaving the organization exposed.
Primary ownership: Risk or legal leaders
In most organizations, primary ownership of AI RMF adoption should sit with the General Counsel, CISOs, Head of Risk or Chief Risk Officer. These leaders are best positioned to:
- Operationalize AI risk management as part of the broader enterprise risk strategy
- Interpret emerging regulatory requirements and assess legal exposure
- Establish policies that align AI development and deployment with organizational risk tolerance
These leaders can also champion the framework across departments, ensuring it doesn’t get confused to technical or compliance teams alone.
Cross-functional collaboration
AI risk management is inherently cross-disciplinary. Although risk or legal leaders should own the framework, successful implementation requires a close partnership with:
- CISOs to address AI-specific security vulnerabilities
- AI/ML and data science team to apply risk mitigation strategies during system design and deployment
- Compliance, data governance and ethics teams to ensure that AI systems meet regulatory, privacy and ethical standards
- Product, operations and IT teams to embed AI risk management into development and deployment workflows
- The board and audit committee will ensure leadership is adequately resourced to implement AI governance and has thoroughly assessed AI system design, risk exposure and third-party AI usage
Tracing the evolution of NIST AI RMF 1.0 and 2.0
The NIST AI Risk Management Framework is designed to evolve alongside advances in AI. As such, NIST has released iterative versions that aim to keep the framework practical, relevant and forward-looking as organizations’ AI maturity grows.
NIST AI RMF 1.0
Released in January 2023, NIST AI RMF 1.0 introduced the foundational structure for managing AI risks. It provided a voluntary, flexible framework that organizations could use to:
- Identify, assess and manage risks across the AI lifecycle
- Promote trustworthy AI by focusing on key characteristics like transparency, fairness, accountability and robustness
- Build shared language and best practices across industries, policymakers and technical teams
Version 1.0 emphasized broad applicability and was intentionally designed to be technology-neutral and adaptable across sectors. It quickly gained traction as a leading U.S. and global reference point for AI governance.
NIST AI RMF 2.0
NIST released AI RMF 2.0 in February 2024, marking its first major update. This version builds on early adoption experiences of 1.0 and adapts to AI paradigms that have evolved since, like generative AI and advanced automation.
- Enhanced governance guidance, with stronger alignment to enterprise risk and cybersecurity processes
- Sector-specific processes, including a Generative AI Profile in July 2024
- Improved tools and resources designed to support both small-scale pilots and enterprise deployments
- Closer alignment with global regulations, making it easier to comply with frameworks like the EU AI Act
The 4 key components of the NIST AI Risk Management Framework
There are four core functions within the NIST AI Risk Management Framework that guide organizations through identifying, assessing, managing and continuously improving AI risk management practices. These components are designed to work together as a flexible, iterative process that can be adapted across different industries and AI maturity levels.
| Component | What it is | Why it matters |
|---|---|---|
| Map | Establishes the context, scope and purpose of the AI system, including its intended use, stakeholders and potential impacts. | Mapping helps organizations understand where risks can emerge across the AI lifecycle and ensures that all stakeholders are identified and considered early |
| Measure | Involves assessing, analyzing and quantifying AI-related risks based on system behavior, data quality, fairness, security and more | Measuring provides the evidence needed to make informed decisions about risk levels, trade-offs and mitigation strategies. It helps prioritize the most significant risks. |
| Manage | Focuses on implementing risk controls, mitigation actions and continuous monitoring processes to reduce identified risks. | Managing helps actively address AI risks, not just document them. It creates accountability and supports responsible deployment. |
| Govern | Establishes oversight, policies, procedures and roles for ongoing AI risk management and accountability. | Governance drives sustained adoption, aligns AI practices with organizational values and integrates AI risk management into enterprise-wide risk and compliance systems. |
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Discover moreHow to implement the NIST AI RMF in your organization
Implementing the AI RMF doesn’t require a one-size-fits-all approach. The framework is flexible and scalable, bringing parity to AI operations in organizations large and small.
NIST AI RMF for small businesses
Small businesses often have limited resources and smaller AI footprints, but they still face significant AI-related risks, especially when adopting third-party AI tools or customer-facing applications.
Key steps for small businesses:
- Start with mapping: Clearly define the purpose of AI use, the stakeholders impacted and where AI is integrated into your operations or services. Build a basic inventory of AI systems and document their intended use to understand potential risks.
- Focus on measurable risks: Even simple assessments of fairness, data quality and security vulnerabilities can help prevent major issues. Create a checklist or forecast different scenarios to flag risks that need the most attention.
- Leverage external resources: Use NIST’s published guidance, toolkits and templates to reduce the burden of building processes from scratch.
- Assign clear ownership: Even in small teams, someone should be responsible for overseeing AI risk management, typically the business owner, head of IT or general counsel. One person should regularly review AI risks and analytics and track any updates to regulations or technology shifts.
- Prioritize high-impact use cases: Focus on the AI systems that could most affect your customers, employees or regulatory exposure; consider AI-driven decisions that impact hiring, customer data, pricing or safety first.
- Adopt purpose-built tools: Spreadsheets can be a helpful starting point for risk management, but AI and the AI RMF demand a more sophisticated approach. Tools like Diligent AI Risk Essentials equip teams to manage AI risks quickly and effectively, in a platform that won’t overwhelm small teams.
NIST AI RMF for large enterprises
As organizations grow, so does their infrastructure. Larger organizations often have a patchwork of complex AI ecosystems, regulatory scrutiny and corresponding operational risks. For them, AI risk management must be formalized and integrated across business units.
Key steps for larger enterprises:
- Establish cross-functional governance: Form dedicated AI risk committees that include legal, compliance, security, AI/ML and executive leadership. Get buy-in from the top, ideally the board and executive leaders, to start creating formal decision-making and escalation processes.
- Embed risk management across the lifecycle: Adopt the map, measure, manage and govern processes as you develop, procure, deploy and monitor AI models. Integrating AI risk assessments within key checkpoints can help identify risks before a model moves beyond mission-critical stages, like design or launch.
- Scale with tools and automation: AI governance platforms can help manage enterprise-scale AI risks efficiently. Systems that provide continuous risk monitoring and automatically flag issues can eliminate blind spots and sharpen your risk management strategy.
- Conduct regular audits and reporting: Formalize internal audit procedures and provide regular risk updates to executive teams and boards. Leveraging dashboards and reporting templates to deliver AI risk status and compliance progress can push real-time reporting further, leading to better decision-making.
- Prepare for global compliance: Align NIST AI RMF processes with other global frameworks to streamline governance across jurisdictions. Read more on this below.
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Request a demoNIST AI RMF use cases by industry
While the NIST AI RMF is adaptable, how it works in practice can vary depending on the unique risks and regulatory challenges an industry faces related to AI adoption. Below are key examples of how the framework can help manage AI responsibly.
Finance
Many financial institutions already rely on AI for credit scoring, fraud detection, algorithmic trading and personalized customer services. The risks associated with these tools can affect regulatory compliance, customer trust and financial stability, making it essential to adopt AI frameworks like AI RMF.
- Map: Identify where AI influences high-stakes decisions like loan approvals or transaction monitoring, and map potential impacts on fairness and security.
- Measure: Assess risks related to bias in lending algorithms, model drift and vulnerabilities that could be exploited for fraud.
- Manage: Implement guardrails to detect discriminatory outcomes, monitor financial models in real time and secure sensitive customer data.
- Govern: Assign responsibility for AI-driven decisions to specific people, emphasizing their role in explaining AI models you develop. Align these steps with financial regulators, like the Consumer Financial Protection Bureau (CFPB) or the Federal Reserve.
Healthcare
AI is already supporting clinical decision-making, diagnostic imaging, patient triage and even personalized treatment plans. The decisions AI makes in each use case can have life-or-death consequences and implications for personal health information, making rigorous risk management essential.
- Map: Identify where AI and patient care overlap, especially in highly sensitive and emotional areas like diagnosis and risk prediction.
- Measure: Biased training data, inaccurate predictions and potential harm to patients can all pose risks. Evaluate them carefully to forecast their impact.
- Manage: Rigorously validate risk scores, lean on human oversight and create feedback loops to detect model failures before integrating an AI tool in patient care workflows.
- Govern: Safeguard patient safety and privacy by aligning your organization’s use of AI with HIPAA, FDA guidance and ethical standards.
Government
Government agencies leverage AI to allocate resources, keep the public safe, prevent fraud and provide citizens with essential services. Prioritizing fairness, transparency and public accountability in all AI systems is critical to maintaining constituent trust and avoiding harming vulnerable populations.
- Map: Examine where and how AI influences program eligibility decisions, security protocols or access to public resources.
- Measure: Analyze risks around algorithmic biases, privacy violations and the potential for systemic discrimination in public programs, all of which can interfere with government agencies’ duty to serve all people.
- Manage: Build transparent processes, provide feedback channels and create mechanisms to regularly audit and update AI systems.
- Govern: Develop policies that align AI use with public trust, civil rights protections and emerging federal AI guidelines.
Pros and cons of implementing the NIST AI risk management framework
Adopting the NIST AI RMF offers significant benefits, but it also comes with challenges worth considering. Below is a balanced look at both to help you evaluate whether the NIST AI RMF is right for you.
| Pros | Cons |
|---|---|
| Proactive risk management | Resource-intensive for some organizations |
| Helps organizations identify and mitigate AI risks before they become critical issues. | Smaller organizations may face capacity constraints when trying to apply all components of the framework. |
| Regulatory readiness | Requires cross-functional collaboration |
| Positions organizations to comply with emerging AI regulations and global standards. | Success depends on collaboration across legal, risk, technical and executive teams, which can be difficult to manage. |
| Builds trust and transparency | Evolving framework requires continuous attention |
| Improves stakeholder confidence by promoting fairness, accountability and explainability in AI systems. | The framework is iterative; staying aligned with updates (like the transition from AI RMF 1.0 to 2.0) requires ongoing effort. |
| Industry flexibility | Complex implementation |
| Adaptable to a wide range of industries, use cases and organization sizes. | For organizations with complex AI ecosystems, initial mapping and integration may be challenging to scope and execute. |
| Global recognition | Not a compliance guarantee |
| Widely respected beyond the U.S., making it a valuable framework for multinational organizations. | While it supports regulatory alignment, using the framework alone does not guarantee legal compliance with all jurisdictions. |
The role of NIST AI RMF in the broader regulatory landscape
Governments around the world are moving to regulate artificial intelligence. This shift can be overwhelming for organizations already struggling to keep up with the pace of innovation. The AI RMF can help. While it is a voluntary framework, its principles align closely with the direction of global AI regulations, positioning it as a practical and highly relevant tool for responsible AI governance.
EU AI Act
The European Union’s AI Act is the world’s first comprehensive AI regulation, introducing a risk-based classification system that sets strict requirements for high-risk AI applications. The Act focuses on:
- Transparency
- Data quality
- Human oversight
- Risk management throughout the AI lifecycle
The NIST AI RMF aligns with many of these requirements, especially in its emphasis on mapping, measuring and managing risks in a structured and auditable way. Implementing it constitutes a significant and, in many ways, simpler step toward compliance with the EU AI Act.
U.S. Executive Order 14110
Signed in October 2023, this Executive Order signals the U.S. government’s growing focus on AI accountability, including mandates for:
- Advancing AI safety standards
- Protecting civil rights and privacy
- Increasing transparency and fairness in government AI use
The order specifically highlights the importance of risk management and responsible AI development in the same way as the NIST AI RMF, making the framework a key reference point for organizations aiming to align with U.S. federal expectations.
Japan’s AI Promotions Bill
Japan has long engaged in conversation about AI regulation. Its 2019 Social Principles of Human-Centric AI and voluntary corporate governance and implementation guidelines prioritized the human impact of AI. In February 2025, Japan passed a landmark AI Promotion Bill; while light on direct regulation, it does mandate cooperation with the government on safe AI development and marks the creation of Japan’s first comprehensive AI law.
Australia’s AI Ethics Principles and AI Safety Standards
Adopted in 2019 and 2024, respectively, these voluntary tools include practical guardrails around transparency, human oversight, bias mitigation, testing and accountability. A proposal paper released in September 2024 included additional mandatory guidelines for high-risk AI systems requiring:
- Risk management and performance testing
- Human oversight and transparency
- Recordkeeping and challenge mechanisms
How Australia integrates these guidelines is still evolving, but implementing the NIST AI RMF can help organizations proactively comply with key aspects of the guardrails.
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Download nowFuture-proofing your organization with the NIST AI RMF
The NIST AI Risk Management Framework is about risk control, but it is also a jumping-off point to future-proofing your ERM strategy. Waiting for shifting AI regulations to finalize can leave your organization scrambling to retrofit systems and processes under tight deadlines. By adopting NIST AI RMF now, your organization can build regulatory resilience and stay ready for what’s next.
- Regulatory alignment across borders: The core principles of the NIST AI RMF closely align with emerging requirements in the EU, the U.S, Australia, Japan and more. Setting AI RMF implementation in motion can give you a significant advantage in complying with other guidelines.
- Technology-neutral foundation: The NIST framework is adaptable enough to apply to various AI technologies, whether machine learning models or cutting-edge generative AI. This flexibility reduces the risk of compliance gaps as your organization adopts new AI solutions.
- Operational efficiency: Building processes around the NIST AI RMF now can minimize costly, disruptive overhauls later. Proactive risk management leads to faster regulatory response times, streamlined audits and more substantial stakeholder confidence.
- Global scalability: For multinational organizations, the NIST AI RMF provides a consistent governance structure that can scale across regions and regulatory environments, reducing duplication of efforts and supporting more efficient cross-border operations.
Turn the NIST AI Risk Management Framework into strategic action
While the NIST AI RMF provides the “what” and the “why” of AI risk management, successful implementation also requires the right tools to manage AI risks at scale. This is especially true for organizations using complex AI systems or operating in highly regulated industries.
Diligent AI Risk Essentials provides a single platform for all risk, audit and compliance activities, including implementing the NIST AI Risk Management Framework. Benchmark AI risks using peer data, centralize risk management and finally retire manual spreadsheets — all accelerating both NIST compliance and your evolution as an AI-savvy enterprise.
However, finding the right tool to put the NIST framework into practice can feel overwhelming. You’ll need to consider:
- AI risk assessments
- Model monitoring and bias detection
- Documentation and audit management
- Vendor risk management for third-party AI providers
Not sure where to start? Download our AI buyer’s guide to discover clear, actionable evaluation criteria to guide your search.
FAQs
What is the NIST AI Risk Management Framework?
The NIST AI Risk Management Framework is a voluntary, widely recognized guidance developed by the National Institute of Standards and Technology to help organizations identify, assess, manage and monitor risks across the artificial intelligence (AI) lifecycle. Released in 2023, the framework promotes trustworthy AI by focusing on core principles like transparency, fairness, accountability and robustness. It’s designed to be flexible and scalable, making it applicable across industries, AI use cases and organization sizes.
How do I implement the NIST AI RMF in my organization?
To implement the NIST AI RMF in your organization, map your AI systems, their purposes and stakeholders. Next, measure the potential risks, including bias, security vulnerabilities and regulatory impacts. Develop strategies to manage those risks with appropriate controls, continuous monitoring and human oversight. Finally, governance policies should be established to ensure accountability and long-term AI risk management.
The framework is scalable, so you can tailor your approach based on your organization’s size and complexity. Many organizations pair the framework with AI risk management tools for optimal results. See our AI software buyer’s guide for help finding the right solution for your organization.
Who should use the NIST AI RMF?
The NIST AI RMF should be used by various roles and teams involved in AI development, deployment, and governance. Key users include:
- General counsel and heads of risk
- Enterprise risk management teams and legal counsel
- Board members and audit committees
- CISOs and cybersecurity leaders
- AI/ML, data science and engineering teams
- Compliance, data governance and ethics teams
- Executive leadership
The framework is valuable for any organization using AI, whether in finance, healthcare, government or other sectors, and it supports cross-functional collaboration to ensure responsible AI use.
How does the NIST framework compare to the EU AI Act?
The NIST AI RMF and the EU AI Act share common goals — promoting safe, transparent, and accountable AI — but they serve different purposes.
- The NIST AI RMF is voluntary guidance that helps organizations proactively manage AI risks across the lifecycle.
- The EU AI Act is a binding regulation that classifies AI systems by risk level and imposes specific legal obligations on high-risk AI use cases.
While the NIST framework provides flexibility, the EU AI Act sets mandatory requirements with penalties for non-compliance. However, using the NIST AI RMF can position organizations for future compliance with the EU AI Act and other global regulations.
Is the NIST AI RMF legally binding?
No, the NIST AI RMF is not legally binding. It is a voluntary framework intended to help organizations manage AI risks responsibly. Although it is not enforced by law, the NIST AI RMF is widely adopted as a best-practice standard for AI governance. Using the framework can help organizations prepare for compliance with emerging AI regulations and reduce legal and reputational risks.
Does the NIST AI RMF only apply to US government contractors?
No. While the NIST framework originated to support U.S. government agencies and contractors, it is increasingly used by private companies, global organizations and cross-industry leaders worldwide. The NIST AI RMF is considered a “gold standard” for responsible AI governance and is applicable to any organization seeking to manage AI risks and build trustworthy AI systems.
How does Diligent help with NIST AI RMF implementation?
Diligent toolkits offer controls mapped to the NIST AI RMF, step-by-step onboarding and templates to simplify adoption — whether you’re starting out or scaling up.
What if we want to align with multiple frameworks (EU AI Act, ISO, NIST)?
Diligent pre-maps controls to multiple standards, so you can benchmark, audit and report across requirements without duplicating effort.
What are the risks of managing AI risk via spreadsheets?
Managing AI risk via spreadsheets comes with a high risk of data silos, errors, missed updates and poor visibility across teams. Modern ERM tools automate and centralize this work for stronger compliance and insights.
NIST resources
Topic: NIST 800-53A
Who is it for: Compliance teams, audit professionals, risk managers
Resource type: Blog
Summary: Achieve stronger cybersecurity and compliance. This step-by-step blog explains how to conduct NIST 800-53A audits, outlines key control updates, and provides a practical checklist for organizations looking to move from reactive to proactive cyber risk management.
Link: NIST 800-53A audit and assessment checklist
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Topic: NIST Cybersecurity Framework 2.0
Who is it for: Compliance teams, risk managers, security leaders
Resource type: Blog
Summary: Stay ahead of evolving cyber threats with the latest update on NIST CSF 2.0. This blog unpacks the major enhancements — including the new “govern” function — reveals how the framework boosts organizational-wide risk management, and outlines practical steps for building a tailored, proactive, and board-ready cybersecurity program.
Link: NIST CSF 2.0
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Topic: NIST 800-171
Who is it for: Compliance teams, IT managers, federal contractors
Resource type: Blog
Summary: Strengthen your organization’s approach to handling controlled unclassified information (CUI). This blog provides a practical NIST 800-171 checklist, breaks down the 14 control families and 110 required controls, and offers actionable steps to help organizations assess, document, and improve their compliance program—protecting sensitive data and minimizing legal and reputational risks.
Link: NIST 800-171 checklist
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Topic: NIST SP 800-53 Rev. 4
Who is it for: Compliance teams, risk managers, IT security professionals
Resource type: Blog
Summary: Explore the foundations of modern cybersecurity with NIST SP 800-53 Rev. 4. This blog breaks down the framework’s 18 control families and key attributes, offering practical guidance for building resilient systems that address emerging threats — including mobile, cloud, and privacy risks. Understand how the revision shaped IT risk management and discover the evolution to newer standards.
Link: NIST SP 800-53 Rev. 4 Security Controls
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Topic: IT Risk Management Solution
Who is it for: Compliance teams, risk managers, IT leaders
Resource type: Solution page
Summary: Proactively identify, assess, and manage cyber risks — like ransomware and data loss — while enabling leaders with real-time insights. Diligent's solution unifies IT risk workflows, streamlines reporting, and reduces manual effort for a more resilient and efficient risk program.
Link: Diligent IT Risk Management solution
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Topic: IT Compliance Solution
Who is it for: Compliance teams, IT managers, audit professionals
Resource type: Solution page
Summary: Streamline and automate IT compliance — across frameworks like NIST, PCI, and ISO — on a single platform. Diligent’s solution centralizes compliance, automates evidence collection, supports continuous controls monitoring, and enhances executive visibility for a stronger, more efficient compliance program.
