3 AI Risk Management Frameworks for 2025 + Best Practices

McKinsey reports that 78% of companies now use generative AI. But business leaders are simultaneously working to address safety concerns around cybersecurity, privacy, and accuracy. AI risk management frameworks give these organizations the tools to identify, assess, and minimize these risks without losing AI's competitive advantages.

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In this article, we’ll cover:

• What AI risk management really entails in 2025
• 3 AI risk management frameworks
• AI risks, challenges in managing them, and best practices

Let’s get started.

AI risk management: A 30-second summary

AI risk management is the structured approach to controlling potential threats that emerge when organizations deploy artificial intelligence systems at scale.

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The challenge has intensified with generative AI's rapid adoption in transformation strategies. These systems can produce content, code, and decisions fast, but they can also generate misinformation, expose sensitive data, or amplify existing biases just as quickly. Your organization must manage that risk if you want to use GenAI responsibly and protect customer trust and compliance.

The 5 core risks associated with AI

To manage AI risks effectively, you need to know what you're up against.  Below are five core risks every team should plan for:

Model risk

Model risk refers to performance degradation, drift, and bias that develop over time. Research from MIT, Harvard, the University of Monterrey, and Cambridge in 2022 showed 91% of ML models experience drift within several years of deployment. If your training data had biases, your AI will keep making those same unfair choices

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Set up monitoring to catch when your model starts making worse predictions. Have someone actually review your AI's decisions regularly, especially for anything that affects people's lives.

Data privacy

Data privacy violations happen when AI systems access personal information without proper protection.

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Train your people on what not to feed AI tools. Set up filters that catch sensitive data before it reaches AI systems. Create clear rules about which data AI can and can't access.

Regulatory compliance

Regulatory compliance is non-negotiable if you want to avoid legal trouble. The EU AI Act imposes fines of up to EUR 35 million or 7 percent of worldwide annual turnover, whichever is higher, for the most serious violations. US regulations vary by state.

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You must navigate this patchwork of requirements while maintaining consistent governance across every location you operate in.

Security threats

AI creates new attack vectors that traditional security doesn't cover. Security threats include prompt injection, stealing your AI capabilities, and tricking AI with crafted inputs.

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Treat AI systems like any other critical infrastructure. Monitor who's accessing your AI and how. Train your security team on AI-specific threats. Have a plan for when (not if) something goes wrong.

Ethical use

Ethical use challenges arise when AI systems make decisions affecting human lives without transparency or accountability. For example, Air Canada's chatbot promised discounts that the company didn't offer, and they got sued.

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You need humans in the loop and clear accountability when AI makes consequential decisions.

3 key frameworks for AI risk management

AI risk frameworks help teams categorize risks, define controls, and stay aligned with evolving regulations. 

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Organizations commonly use these three:

1. NIST AI risk management framework

NIST AI Risk Management Framework (AI RMF) is a standard developed by the US National Institute of Standards and Technology to help organizations manage AI-related risks. The framework is voluntary and designed for organizations of all sizes across the public and private sectors.

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It breaks down AI risk management into four core functions:

1. Map: Figure out what AI systems you actually have running
2. Measure: Test your AI systems to see where problems might happen
3. Manage: Fix the problems you found and monitor ongoing performance
4. Govern: Get your leadership team on board and make someone responsible for AI safety

It also defines profiles. These are templates that organizations can adapt based on their goals, sector, or risk tolerance.

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Profiles help teams:

• Align risk management to specific use cases
• Track progress over time
• Communicate risk posture to stakeholders

A healthcare org and a fintech company might both use the AI RMF, but their profiles will look different.  A fintech might focus on bias in lending algorithms, while a healthcare system might prioritize safety in diagnostic AI tools.

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The July 2024 Generative AI Profile added over 200 specific actions addressing unique risks from large language models and generative systems.

2. ISO/IEC 23894

ISO/IEC 23894 is an international standard that helps organizations manage risks associated with AI systems throughout their lifecycle. It adapts traditional risk management practices to address the unique characteristics of AI.

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The standard has 5 key risk management processes:

1. Risk identification: Analyze intended use, potential misuse, data quality, and how the system supports or replaces human decision-making.
2. Risk assessment: Use qualitative and quantitative methods to evaluate the likelihood and impact of risks, including cascading and systemic effects.
3. Risk treatment: Apply controls, modify models, transfer or accept risks, and ensure safeguards are in place over time.
4. Monitoring and review: Track key risk indicators, reassess controls regularly, and adjust as the system evolves.
5. Recording and reporting: Maintain transparent documentation throughout the AI lifecycle.

Unlike the flexible structure of NIST’s AI RMF, ISO/IEC 23894 is more prescriptive and process-driven. It’s also designed to closely align with existing ISO systems. Organizations already using ISO 31000:2018 (general risk management) can extend current processes to cover AI risks without creating parallel governance structures.

3. EU AI Act

The EU AI Act is the European Union’s sweeping regulation for artificial intelligence. It’s the first major legal framework to govern AI across all sectors, focusing on how AI is used in the real world.

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The Act classifies AI systems into four risk categories:

• Unacceptable risk: Banned outright (e.g., social scoring by governments)
• High risk: Strictly regulated (e.g., AI in hiring, credit scoring, or medical devices)
• Limited risk: Requires transparency (e.g., chatbots must disclose they’re bots)
• Minimal risk: No regulation (e.g., spam filters or recommendation engines)

If your AI system falls into the high-risk category, you’ll need to meet specific requirements across data governance, transparency, human oversight, robustness, and security. You’ll also need to register it in an EU-wide database and prepare detailed documentation for audits.

The most common challenges of AI risk management 

From hidden tools to model sprawl and compliance complexity, these are the challenges most teams struggle with day to day:

• Shadow AI: Shadow AI occurs when employees use browser-based AI tools that bypass corporate security. Employees may share sensitive data without realizing that the tools use this data to train external models, and it could leak to competitors. Traditional monitoring tools often can’t detect this activity.‍
• Model sprawl: The average enterprise runs 66 different GenAI apps, with 10% classified as high-risk. Each model update potentially changes behavior or introduces new biases. Without centralized model registries, organizations lose track of which versions run where. That makes it hard to patch vulnerabilities or stay compliant.‍
• Observability gaps: AI systems produce massive amounts of data that traditional tools can’t parse. They miss hallucinations or subtle shifts in model reasoning. Multi-modal systems add more risk. When text, image, and voice models interact, errors often go undetected.‍
• Compliance tracking across jurisdictions: The EU enforces strict AI governance while the US adopts a lighter touch. Global organizations must maintain different AI systems for different markets or risk non-compliance.

How does AI governance work with risk management?

AI governance works with risk management by providing oversight, accountability, and policy enforcement during model development and deployment.

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Here’s how leading teams put AI governance into practice across each stage of the development lifecycle.

Start with clear, enforceable policies

Create a cross-functional AI governance group. Include people from legal, IT, security, compliance, and business units. This group defines high-level policies, while individual teams adapt them to their own workflows.

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Common policies include:

• What types of AI tools employees can and can’t use
• What data is allowed in AI systems
• When AI use requires approval before deployment

Publish these policies in a centralized, accessible place. This could be your company wiki, intranet, or governance portal. Keep them updated and easy to find.

Match human oversight to risk

Implement human oversight at decision points that matter. For example:

• A chatbot can auto-respond to FAQs but should escalate complex cases.
  
  
• A résumé screening model should flag edge cases for human review.

Look at where AI errors would cause the most harm, and require human sign-off in those moments.

Document and version every model

Every AI system must include documentation. At a minimum, record:

• Its purpose and use case
• Key inputs and outputs
• Known risks and limitations
• Change history and approvals

Teams should use version control to track edits and store docs in a shared location for audits, investigations, or compliance reviews. Many teams use MLOps platforms that log model lineage and deployment history automatically.

Set up a model registry and approval workflows

A model registry is your single source of truth for all AI systems in the organization. It tracks what models exist, where they're deployed, who owns them, and their approval status.

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Approval workflows should match your risk tolerance. Low-risk models (like internal tools) may need only a quick review. You may need to fully validate high-risk models (like customer-facing or decision-making systems).

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Each workflow should include:

• Technical review: Does the model work as expected?
• Risk assessment: What could go wrong?
• Business validation: Does it solve the intended problem?

Make sure your registry and workflow tools fit into how teams already ship software. If approvals are slow or disconnected, people will find ways around them.

Real-world AI risks

These four high-profile incidents from 2023 to 2025 highlight common AI failure modes and what others can learn from them:

Samsung: ChatGPT data leak

Samsung engineers used ChatGPT to debug code. In three separate incidents, employees pasted sensitive data, including proprietary semiconductor designs, into the chat. They didn’t realize that their inputs could be used to train future models.

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Samsung’s immediate response was to ban ChatGPT. While drastic, this stop-everything reaction is common after breaches. Ideally, you could install LLMs locally, so your data stays with you. If that’s not an option, invest in employee training on AI use and require a clear AI usage policy or charter that everyone signs.

Air Canada: Chatbot legal liability

An Air Canada chatbot promised a bereavement discount to a customer. The airline refused to honor it. In court, the company argued the chatbot was a separate legal entity. Courts rejected the claim and ruled the airline was liable for promises made by its chatbot.

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This case shows that organizations must treat AI commitments as legally binding.

Evolv Technologies: FTC settlement 

Evolv Technologies marketed its metal detectors as “AI-powered weapons detection.” In reality, the system used basic rules and manual reviews. The company also buried negative security reports.

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The FTC intervened, calling out the unsubstantiated AI claims and reaching a settlement. This signaled regulators' willingness to pursue companies making false AI claims, especially in public safety applications.

5 best practices for managing AI risk in 2025

Leading organizations manage AI risk across people, systems, and processes all at once. The practices below offer a starting point for organizations building mature AI risk programs.

1. Keep a centralized AI system inventory: Track all AI models in one place. Include ownership, purpose, status, and version history. Use this inventory to monitor risk, patch vulnerabilities, and prove compliance.
2. Train employees on safe AI use: Offer mandatory training. Cover privacy risks, prompt safety, and use company-approved tools. Test understanding through real scenarios.
3. Maintain full audit trails: Log every model decision, update, and approval. Store logs in tamper-proof systems. Use them for investigations, audits, and performance reviews.
4. Involve humans where it matters: Insert human review into critical decision paths. Use risk-based rules to trigger review for high-impact outputs. Automate oversight for low-risk use cases.
5. Treat LLMs like APIs: Apply the same discipline to LLMs as you would to any external service. Version them, test outputs regularly, and document their behavior. Track how they’re used across the organization to manage exposure and drift.

6 steps to build a practical AI risk management program

Most organizations build an AI risk management program in stages over months or a few years. This roadmap outlines key phases, deliverables, and tools to guide the process:

1. Assessment and planning

This phase focuses on understanding your current AI ecosystem and establishing initial governance. 

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To get there:

• Run an AI maturity assessment. Document every system in use.
• Form a governance committee with executive sponsorship and cross-functional leads.
• Define risk tolerance. Decide which risks your organization will accept — and which ones require mitigation.

2. Framework selection

Once you’ve established baseline governance, the next step is choosing and tailoring a framework to your environment.

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During this phase:

• Select a governance framework based on business context and regulatory obligations. U.S. organizations often adopt NIST AI RMF. EU operations must align with the AI Act.
• Translate framework requirements into internal policies and procedures.
• Build standardized templates for AI risk assessments.

3. Tool deployment

With policies in place, shift the focus to building the technical infrastructure to enforce them. This includes:

• Deploying monitoring tools to track model behavior.
• Integrating risk tools with your development pipelines.
• Setting up automated alerts for policy violations and performance drift.
• Building centralized dashboards for leadership oversight.

4. Process implementation

Once tools are in place, you can roll out governance workflows organization-wide.

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Key steps here:

• Launch AI risk assessments for all new initiatives.
• Implement incident response playbooks with clear escalation paths.
• Enforce pre-deployment testing for safety, fairness, and robustness.
• Set up documentation workflows that track approvals, changes, and decisions.

5. Training and rollout

People are the core of any governance program. This phase builds internal capabilities through targeted training and staged rollout:

• Train each role group. Engineers need risk modeling skills, while business users need AI literacy.
• Start with pilot teams. Collect feedback, adjust, and scale.
• Set up support channels to help teams navigate the new processes.

6. Continuous improvement 

Ongoing review keeps your program aligned with evolving risks, team feedback, and changing regulations.

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To sustain and improve your program:

• Track KPIs like risk incidents, compliance scores, and user feedback.
• Hold quarterly reviews to adapt processes based on what’s working.
• Stay ahead of new regulations and risks as the ecosystem shifts.

How Superblocks tackles AI risk visibility

Superblocks gives organizations visibility into AI-powered app development by embedding governance, security, and traceability at every layer of the stack.

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Here’s how Superblocks supports AI risk visibility across the stack:

• Central policy enforcement: All AI-generated applications are subject to organization-wide policies, including security, access control, and compliance requirements.
• Clark AI Agent: Superblocks’ AI agent, Clark, routes user requests through a network of specialized AI agents (Designer, IT admin, Engineer, Security Operations, QA) that plan, design, secure, and test applications. Each step enforces enterprise-grade security, data permissions, and design standards.
• Role-Based Access Control (RBAC): Advanced RBAC allows organizations to define custom roles and maintain granular control over who can view, edit, or deploy applications.
• Audit logging: All actions and changes within Superblocks are logged, supporting compliance audits and enabling quick identification of potential security incidents
• Secure data handling: Clark AI only accesses data that users are already permitted to see. No customer data or personally identifiable information (PII) is used to train large language models (LLMs), and neither Superblocks nor its LLM providers retain customer data from AI requests.
• Deployment flexibility: Organizations can host Superblocks in their own VPC or on-premises to maintain full control over sensitive data and application infrastructure.
• Automated QA: Before deployment, Superblocks automatically tests AI-generated applications for functionality, accuracy, and common security flaws.
• Code-level transparency: All generated code is visible, traceable, and available for review by engineering and IT teams.

Keep AI development secure with Superblocks

Superblocks gives teams the structure they need to build AI apps responsibly. You can control who uses AI, what data it touches, and how changes are reviewed before deployment. Everything is logged, versioned, and visible from a single pane of glass.

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Here’s a recap of the key features:

• Flexible development model: Use Clark AI to generate apps from plain English, fine-tune them in the visual editor, or tweak with code when needed — all within a unified workflow.
• Standardized UI components: Build consistent interfaces using reusable elements that align with your design system.
• Full-code extensibility: Leverage JavaScript, Python, SQL, and React to handle complex finance logic and integrations. Connect to Git and deploy through your existing CI/CD pipeline.
• Integration with your existing systems: Connect to databases, SaaS apps, and other third-party platforms using our pre-built connectors.
• Centralized governance: Enforce RBAC, authentication, and audit logs from a single control plane.

• Full portability: Export your app as raw React code and run it independently.

• Fits into existing SDLCs & DevOps pipelines: Supports automated testing, CI/CD integration, version control (Git), and staged deployments so you can manage changes.
• Incredibly simple observability: Receive metrics, traces, and logs from all your internal tools directly in Datadog, New Relic, Splunk, or any other observability platform.
• Real-time streaming support: Stream data to front-end components and connect to any streaming platform, such as Kafka, Confluent, or Kinesis, to build real-time user interfaces.

If you’d like to see these features in practice, check out our Quickstart Guide

Frequently asked questions

How does the NIST AI RMF work?

The NIST AI Risk Management Framework works by guiding teams through four repeatable actions. You start by identifying how the AI system is used and where risks may appear (Map). Then, you evaluate those risks using defined metrics and assessments (Measure). 

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Based on what you find, you apply controls to reduce or mitigate those risks (Manage). Throughout, you establish oversight structures to ensure accountability and policy enforcement (Govern).

What tools help with AI governance?

Organizations use policy engines, monitoring platforms, and documentation workflows to enforce AI governance.

How can dev teams reduce risk from LLMs?

Dev teams can reduce risk from LLMs by combining technical safeguards with policy and oversight. Start by enforcing input filters to block sensitive data, use prompt validation to prevent injection attacks, and limit output scope to avoid hallucinations. 

Is AI compliance required by law in 2025?

AI compliance laws in 2025 vary by region. In the European Union, the AI Act legally enforces mandatory requirements for high-risk systems. Compliance deadlines started in Feb 2025.  In the US, regulations are at the state level.

What’s the difference between model governance and AI governance?

The difference between model governance and AI governance is scope. Model governance manages the lifecycle and performance of individual AI models. AI governance oversees how AI is used across the organization, including policy, compliance, ethics, and risk. Model governance is one piece of the broader AI governance strategy

Can Superblocks help enforce AI policies across citizen developers?

Yes. Superblocks gives IT teams control over how AI is used across the org, even by non-technical users. You can define permissioned access to data sources, restrict which AI actions are allowed, and monitor usage through audit logs. Clark AI also runs within your security and governance settings.

What are the biggest risks of using AI in regulated industries?

The biggest risks of using AI in regulated industries include bias, lack of explainability, privacy violations, and noncompliance with sector-specific regulations.

How do I create an AI audit trail?

To build an AI audit trail, log all AI system actions, data sources, model versions, and interactions. Use frameworks like Model Cards and Datasheets for Datasets for structured documentation. Centralized audit tools help track and reconstruct AI activities for compliance

What’s the best way to govern GenAI in enterprise apps?

The best way to govern GenAI in enterprise apps is to establish clear policies for usage data access and security. Then use governance tools to enforce these policies and continuously monitor usage.