Artificial intelligence is becoming an important part of business operations. Organizations use AI to automate customer service, analyze financial data, detect cybersecurity threats, support hiring decisions, and improve productivity.

However, every AI system introduces risks that organizations must understand and manage.

An AI application may generate inaccurate results, expose sensitive information, make unfair recommendations, or behave differently when its data or operating environment changes. These problems can affect customers, employees, business operations, and regulatory compliance.

Identifying AI risks is an important first step, but identifying risks alone does not make an organization safer.

Organizations must determine which risks require immediate attention, select appropriate safeguards, assign responsibilities, and verify that their risk treatment activities are effective.

ISO/IEC 23894:2023 provides international guidance on managing risks associated with artificial intelligence. It helps organizations integrate AI risk management into their existing governance and business processes.

In this guide, we explain how ISO 23894 risk treatment works, how to identify and prioritize AI risks, which treatment options organizations can consider, and how to develop a practical AI risk treatment plan.

What is ISO 23894 risk treatment?

ISO 23894 risk treatment is the process of selecting and implementing appropriate responses to AI-related risks after those risks have been identified, analyzed, and evaluated.

The objective is to help organizations make informed decisions about how to manage risks that could affect the safety, reliability, security, fairness, and performance of AI systems.

ISO/IEC 23894:2023, officially titled Information technology — Artificial intelligence — Guidance on risk management, was published in February 2023 by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC).

The standard provides guidance for organizations that develop, produce, deploy, or use AI-based products, systems, and services.

It also helps organizations integrate AI risk management into existing activities rather than creating separate processes for every AI application.

What does risk treatment mean in practice?

Consider an organization using an AI-powered recruitment system to evaluate job applications.

During a risk assessment, the organization identifies a possibility that the system may unfairly disadvantage certain groups of applicants.

Identifying this risk does not resolve the problem.

The organization must evaluate its significance, investigate potential causes, and determine appropriate treatment measures.

These measures might include reviewing training data, testing outcomes across relevant groups, introducing human oversight, or restricting the system’s use until identified concerns are addressed.

The organization must also assign responsibility for implementing these measures and evaluate whether they reduce the risk to an acceptable level.

This process illustrates how AI risk treatment turns assessment findings into practical actions.

Why is AI risk treatment important?

AI systems can create risks that are difficult to identify through traditional technology risk assessments alone.

Unlike conventional software, some AI systems produce results based on patterns learned from data. Their behavior may change when they encounter unfamiliar information, new operating conditions, or different user interactions.

This creates challenges for organizations that depend on AI-generated outputs to support important decisions.

AI risks can change throughout the system lifecycle

An AI system may perform well during initial testing but produce less reliable results after deployment.

Changes in input data, model updates, user behavior, or external dependencies can affect its performance.

For example, an AI system trained to detect fraudulent transactions may become less effective as fraud patterns evolve.

Organizations must therefore review existing safeguards and adjust risk treatment measures when new risks emerge.

Unmanaged AI risks can affect business operations

AI failures can create financial losses, service disruptions, incorrect decisions, and reputational damage.

A customer service chatbot that provides inaccurate financial information may mislead customers and create additional compliance concerns.

Similarly, an AI system connected to internal business applications may expose sensitive information if access permissions and security controls are not properly managed.

Risk treatment helps organizations reduce these exposures through appropriate technical, operational, and governance measures.

Organizations need accountability for AI decisions

AI risk management becomes difficult when no individual or department is responsible for addressing identified risks.

Technology teams may develop an AI application while business teams use its outputs and compliance teams monitor applicable requirements.

Without clear ownership, important risk treatment activities may be delayed or overlooked.

A structured approach helps organizations assign responsibilities, document decisions, and establish appropriate oversight.

How ISO 23894 supports the AI risk management process

ISO 23894 provides guidance for managing AI-related risks within an organization’s broader risk management activities.

The approach is consistent with established risk management principles, including those reflected in ISO 31000.

Although risk treatment is a central activity, it should not be performed independently of risk identification, analysis, evaluation, communication, and monitoring.

Understanding the relationship between assessment and treatment

AI risk management involves several connected activities.

Risk management activity	Main purpose	Practical output
Establishing context	Understand the AI system, its purpose, and operating environment	AI system profile and risk criteria
Risk identification	Determine what could go wrong and who may be affected	AI risk register
Risk analysis	Examine potential causes, likelihood, and consequences	Documented risk analysis
Risk evaluation	Compare risks against established criteria	Prioritized risk list
Risk treatment	Select and implement appropriate responses	AI risk treatment plan
Monitoring and review	Check whether risks and controls have changed	Monitoring results and updated assessments
Communication and consultation	Involve relevant stakeholders throughout the process	Decisions, approvals, and stakeholder records

These activities should be adapted to the organization’s size, industry, risk exposure, and the characteristics of its AI systems.

A low-impact internal AI tool may require a simpler assessment than an AI application supporting healthcare decisions or access to financial services.

How to identify AI risks before treatment

Effective ISO 23894 risk treatment begins with understanding the risks associated with each AI system.

Organizations should avoid relying on generic risk lists without considering how the technology is actually used.

Step 1: Create an inventory of AI systems

Start by identifying AI applications used throughout the organization.

This inventory should include internally developed models, third-party AI platforms, generative AI tools, automated decision systems, and AI capabilities embedded within existing software.

For each system, document its purpose, business owner, provider, data sources, users, and relevant business processes.

An accurate inventory helps organizations understand where AI-related risks may exist and which systems require additional attention.

Step 2: Understand the purpose and operating environment

Before identifying risks, determine how the AI system works within the business.

Consider the decisions it supports, the information it processes, the people who rely on its outputs, and the consequences of incorrect or unexpected behavior.

For example, an AI tool used to summarize internal documents presents different risks from an AI system used to recommend medical treatment.

The operating environment should influence the depth and scope of the assessment.

Step 3: Identify potential risk scenarios

Risk identification should focus on realistic situations that could create harmful or unwanted outcomes.

Instead of recording a broad risk such as “AI bias,” describe how that risk could affect a particular system.

For example, a more useful risk statement might explain that an AI recruitment system could produce unfair candidate rankings because its training data does not adequately represent relevant applicant groups.

Specific risk scenarios make it easier to evaluate consequences and determine appropriate treatment measures.

Step 4: Involve relevant stakeholders

AI risk identification should not be limited to technology teams.

Business owners, cybersecurity professionals, data scientists, compliance officers, legal advisers, and other relevant stakeholders may identify different risks.

For systems that affect individuals, organizations should also consider the perspectives and interests of people who may experience the consequences of AI decisions.

This helps create a more complete understanding of potential impacts.

Common AI risks that organizations should evaluate

AI risk treatment depends on understanding the types of risks that may affect a system.

The following categories provide a practical starting point for assessments. They are illustrative risk categories rather than a complete classification from ISO 23894.

1. Data privacy and confidentiality risks

AI systems may process personal information, customer records, employee data, and confidential business documents.

Risks can arise when sensitive information is shared with unauthorized services, retained longer than necessary, or accessed without appropriate permissions.

Potential treatments include data minimization, access restrictions, approved AI usage policies, vendor assessments, and stronger data handling controls.

2. Bias and fairness risks

AI systems may produce unfair outcomes when training data, model design, or operating conditions introduce inappropriate differences in results.

These risks are especially important when AI influences decisions involving employment, financial services, education, or healthcare.

Organizations may respond through data quality reviews, fairness testing, appropriate human oversight, and restrictions on automated decision-making.

3. Cybersecurity risks

AI systems may introduce security weaknesses through insecure APIs, unauthorized access, prompt injection, malicious inputs, and compromised third-party components.

For example, an AI assistant connected to internal documents may expose confidential information if access controls are poorly designed.

Risk treatment may involve security testing, access controls, system isolation, monitoring, and incident response procedures.

4. Accuracy and reliability risks

AI applications can produce incorrect, misleading, or inconsistent outputs.

These risks become more significant when employees rely on AI-generated information to make important business decisions.

Organizations may introduce output validation, performance testing, human review, and monitoring procedures to reduce the likelihood or consequences of errors.

5. Transparency and accountability risks

Some AI systems produce outputs that are difficult for users or decision-makers to understand.

This can create challenges when organizations need to explain how decisions were made or demonstrate appropriate oversight.

Potential treatments include maintaining system documentation, defining decision responsibilities, improving explanations where feasible, and establishing review procedures.

6. Third-party AI risks

Organizations frequently depend on external AI providers, cloud platforms, and software vendors.

These relationships can introduce risks involving service availability, data handling, model updates, security, and contractual responsibilities.

Third-party risk treatment may include vendor due diligence, contractual safeguards, ongoing assessments, change notifications, and contingency planning.

How to prioritize AI risks under ISO 23894

Once risks have been identified, organizations must determine which ones require immediate attention.

Not every risk has the same likelihood or consequences, and organizations rarely have unlimited resources to address every issue simultaneously.

A consistent prioritization process helps teams focus on risks that could cause the greatest harm.

Evaluate the potential consequences

Start by considering what could happen if a risk occurs.

Consequences may include financial losses, regulatory exposure, privacy violations, operational disruption, security incidents, or harm to individuals.

For example, an AI system that incorrectly summarizes noncritical internal notes may have limited consequences.

An AI system that produces incorrect recommendations affecting patient care may create much more serious concerns.

Organizations should consider the severity, scale, duration, and potential reversibility of harmful outcomes.

Assess the likelihood of the risk

Likelihood describes how plausible it is that a particular risk scenario will occur.

Organizations may consider historical incidents, testing results, system complexity, known vulnerabilities, data quality, and the effectiveness of existing controls.

However, AI risks can involve uncertainty, especially when systems are new or reliable performance data is limited.

Teams should document these uncertainties rather than presenting unsupported probability estimates as precise facts.

Consider existing controls and exposure

A risk may be significant before safeguards are considered but less severe after effective controls are implemented.

For example, an AI application that processes sensitive information may present substantial confidentiality risks.

Appropriate access controls, data restrictions, monitoring, and contractual protections may reduce some of those risks.

Organizations should distinguish between inherent risk, which is assessed before considering relevant controls, and residual risk, which remains after controls are taken into account.

Use a consistent AI risk scoring method

A risk matrix can help organizations compare risks using defined criteria.

One common approach evaluates likelihood and impact on a scale from one to five.

The resulting score can support initial prioritization, although it should not replace professional judgment or detailed analysis.

Example AI risk prioritization matrix

Risk scenario	Likelihood (1–5)	Impact (1–5)	Illustrative score	Priority
Sensitive data exposed through an AI assistant	4	5	20	Critical
Unfair recommendations in an AI hiring system	3	5	15	High
Incorrect AI-generated financial summaries	3	4	12	High
Temporary interruption of a low-impact AI tool	3	2	6	Moderate
Minor formatting errors in AI-generated content	4	1	4	Low

These values are illustrative only. The scoring scale, thresholds, and priorities are examples, not scoring requirements prescribed by ISO 23894.

A numerical score should not automatically determine whether a risk is acceptable.

For example, a low-likelihood event involving severe harm, legal restrictions, or safety concerns may require immediate treatment regardless of its position in a simple risk matrix.

Organizations should therefore combine scoring with risk tolerance, stakeholder impacts, applicable requirements, and informed management judgment.

ISO 23894 risk treatment options: How to mitigate AI risks

After evaluating and prioritizing risks, organizations must select appropriate treatment responses.

Risk treatment is broader than risk mitigation. While mitigation generally focuses on reducing risk, treatment can also involve avoiding, sharing, or retaining risks through informed decisions.

The appropriate response depends on the nature of the AI system, the potential consequences, and the organization’s risk criteria.

1. Avoid the AI risk

Risk avoidance involves deciding not to begin or continue an activity that creates unacceptable exposure.

For example, an organization may decide not to deploy an AI system that makes high-impact decisions without adequate testing or oversight.

Avoidance may also involve discontinuing an AI feature or restricting a use case until identified risks can be addressed.

This option is particularly important when potential consequences are severe or suitable safeguards are unavailable.

2. Reduce the likelihood of AI risks

Organizations can introduce controls that make undesirable outcomes less likely.

For example, an AI application that processes confidential information may require stronger access controls, security testing, input validation, and restrictions on data sharing.

Similarly, a recruitment system may require better training data, fairness testing, and appropriate model validation.

The objective is to address factors that contribute to the identified risk.

3. Reduce the consequences of AI failures

Some risks cannot be eliminated completely.

Organizations can still reduce their potential consequences through safeguards that limit the impact of failures.

For example, a financial institution may require human approval before an AI system can execute certain high-value transactions.

Other measures may include transaction limits, incident response procedures, fallback processes, and mechanisms for correcting harmful outcomes.

4. Share or transfer aspects of the risk

Organizations may share certain financial or operational consequences through insurance, contractual arrangements, or external service agreements.

For example, a business may establish contractual responsibilities with an AI service provider regarding security incidents, service availability, and data protection.

However, transferring contractual responsibilities does not necessarily remove the organization’s legal obligations or accountability to affected stakeholders.

Organizations should evaluate which responsibilities remain under their control.

5. Retain the risk through an informed decision

Some risks may remain within the organization’s approved risk tolerance after assessment.

In these cases, authorized decision-makers may choose to retain the risk.

The decision should be documented, supported by appropriate reasoning, and reviewed when circumstances change.

Risk acceptance should not be used to bypass legal requirements or justify risks that the organization has no authority to accept.

6. Remove or change the source of risk

Organizations may also modify the AI system or its operating environment to address the underlying source of risk.

For example, a company might remove unnecessary access to sensitive databases, replace an unsuitable training dataset, or disable a feature that produces unreliable outputs.

Addressing the source can sometimes provide a more effective response than adding monitoring controls around an unchanged problem.

How to create an ISO 23894 AI risk treatment plan

An AI risk treatment plan converts assessment results into documented responsibilities, actions, and monitoring activities.

Without a clear plan, organizations may identify important risks but fail to address them consistently.

The following process provides a practical approach to developing an AI risk treatment plan aligned with established risk management practices.

Step 1: Document the risks requiring treatment

Start with the prioritized AI risk register.

For each risk, record the affected AI system, the risk scenario, potential consequences, existing safeguards, assessment results, and reasons for treatment.

This information helps decision-makers understand why action is necessary.

Step 2: Select appropriate treatment measures

Evaluate available treatment options based on their effectiveness, feasibility, cost, and potential consequences.

For example, a generative AI application that may expose confidential information could require a combination of data restrictions, access controls, vendor safeguards, and employee training.

Organizations should also consider whether a proposed control introduces new risks or negatively affects the system’s intended purpose.

Step 3: Assign clear risk owners

Every treatment activity should have an accountable owner.

Depending on the risk, responsibility may belong to a business manager, security team, AI product owner, data governance team, or compliance function.

Risk owners should understand the actions required, expected outcomes, and escalation procedures.

Step 4: Establish deadlines and implementation milestones

Treatment plans should include realistic deadlines based on risk severity and operational requirements.

Critical risks may require immediate restrictions or temporary suspension of affected activities.

Lower-priority risks may be addressed through planned improvements, provided the remaining exposure is acceptable.

Documenting milestones helps teams track progress and identify overdue actions.

Step 5: Define how treatment effectiveness will be measured

Implementing a control does not automatically mean the risk has been reduced sufficiently.

Organizations should determine how they will evaluate whether treatment measures work as intended.

For example, a control designed to reduce inaccurate AI recommendations might be evaluated through testing results, error rates, incident trends, and review findings.

The measurement approach should reflect the nature of the risk and the system’s operating environment.

Step 6: Evaluate and approve residual risk

After treatment measures have been implemented and tested, reassess the remaining risk.

Determine whether the residual risk falls within approved tolerance levels and whether any additional action is required.

Significant residual risks should be reviewed by the appropriate decision-makers.

Documenting these decisions helps maintain accountability and provides evidence for future reviews.

Example AI risk treatment plan

Risk	Treatment action	Responsible owner	Evidence of effectiveness
Sensitive data exposure	Apply access controls and restrict data sharing	Information security manager	Access reviews and security testing
Unfair hiring recommendations	Conduct fairness testing and introduce human review	AI product owner and HR leader	Testing results and review records
Inaccurate financial outputs	Require validation before important decisions	Finance process owner	Validation logs and error monitoring
Third-party AI service disruption	Establish contingency procedures and review vendor arrangements	Vendor risk manager	Vendor reviews and recovery tests
Unauthorized AI tool usage	Establish approved tool policies and monitoring	IT governance manager	Tool inventory and policy compliance reviews

This example can be adapted to different industries and AI use cases.

Organizations should ensure that treatment measures reflect actual risk assessments rather than applying the same controls to every AI system.

Practical example: ISO 23894 risk treatment for a generative AI assistant

Consider a healthcare organization introducing a generative AI assistant to help administrative employees summarize patient-related documents.

The organization wants to reduce manual work and improve operational efficiency.

However, the system processes sensitive information and may generate inaccurate summaries.

Before deployment, the organization conducts an AI risk assessment and identifies several concerns.

Identifying the risks

The assessment identifies three important risk scenarios.

First, sensitive patient information could be exposed if the AI service processes or shares data without appropriate safeguards.

Second, the assistant could generate inaccurate summaries that employees mistakenly treat as reliable.

Third, a third-party service disruption could prevent employees from accessing the application when needed.

Each scenario is documented with its potential consequences and existing controls.

Prioritizing the risks

The organization evaluates the risks based on their potential impact and likelihood.

Confidentiality risks receive high priority because unauthorized disclosure could affect patients and create legal consequences.

Inaccurate summaries also receive significant attention because employees may rely on the information when performing administrative tasks.

Service availability is evaluated based on the organization’s ability to continue operations without the AI assistant.

Selecting treatment measures

For confidentiality risks, the organization restricts the information that can be submitted to the AI service and applies appropriate access controls.

It also reviews the provider’s data handling arrangements and relevant contractual protections.

For inaccurate summaries, the organization requires employees to verify important information against original records before using it.

The organization also tests the system’s performance and monitors recurring errors.

For service availability, the organization establishes an alternative manual process that employees can use during outages.

Monitoring residual risks

After implementing the safeguards, the organization evaluates whether they are operating effectively.

Security teams review access controls, business owners monitor output quality, and vendor management teams review service performance.

The organization reassesses risks when the AI provider changes its model, the system receives new capabilities, or its intended use changes.

This example demonstrates how ISO 23894 risk treatment can support AI adoption while maintaining appropriate oversight.

How to measure the effectiveness of AI risk treatment

Risk treatment should be evaluated using evidence rather than assumptions.

Organizations need to understand whether their safeguards are reducing risks and whether additional action is necessary.

Monitor AI system performance

Relevant performance measures depend on the AI system’s purpose.

They may include error rates, false-positive rates, service availability, security incidents, or the frequency of human corrections.

For systems involving fairness concerns, organizations may also evaluate relevant outcome differences using suitable testing methods.

These measures should be interpreted in context rather than treated as universal indicators of acceptable AI risk.

Track risk treatment completion

Organizations should monitor whether approved treatment activities are completed on time.

Overdue actions, unresolved findings, and repeated control failures can indicate weaknesses in the risk management process.

Tracking these issues helps management identify where additional resources or oversight may be required.

Review control effectiveness

Control effectiveness reviews help determine whether safeguards operate as intended.

For example, an organization may test whether access restrictions prevent unauthorized users from retrieving sensitive information through an AI application.

Evidence from testing, monitoring, and internal reviews can support reassessment of residual risk.

Reassess risks after significant changes

AI risk treatment should be reviewed when important changes occur.

These changes may include new model versions, updated datasets, additional integrations, expanded use cases, security incidents, or changes in applicable regulations.

A reassessment helps determine whether previous risk treatment decisions remain appropriate.

Common mistakes to avoid during ISO 23894 risk treatment

Organizations can weaken their AI risk management efforts when treatment decisions are inconsistent or poorly documented.

Understanding common mistakes can help teams establish more effective processes.

Treating all AI systems as equally risky

Not every AI application requires the same level of oversight.

A low-impact internal productivity tool may not require the same controls as an AI system used to support medical or financial decisions.

Organizations should evaluate risks according to the system’s purpose, potential consequences, and operating environment.

Relying only on numerical risk scores

Risk matrices can support prioritization, but they cannot capture every important consideration.

A low-probability event involving severe consequences may require greater attention than its numerical score suggests.

Organizations should consider uncertainty, stakeholder impacts, legal requirements, and professional judgment when making treatment decisions.

Assuming vendor controls eliminate organizational responsibility

Third-party AI providers may implement security and governance safeguards, but organizations still need to understand their own responsibilities.

Vendor assessments, contractual arrangements, and ongoing oversight remain important.

Failing to document risk acceptance

Risk acceptance decisions should be supported by appropriate analysis and authorization.

Without documentation, organizations may struggle to explain why certain risks were retained or whether those decisions remain appropriate.

Treating risk treatment as a one-time activity

AI systems can change after deployment, and previously effective safeguards may become less reliable.

Regular monitoring, reassessment, and control reviews help organizations respond to changing risks.

ISO 23894 risk treatment vs ISO 42001 risk treatment

ISO 23894 and ISO 42001 both support AI risk management, but they serve different purposes.

ISO 23894 provides guidance on how organizations can manage AI-related risks.

ISO 42001 establishes requirements for an Artificial Intelligence Management System, including processes for AI risk assessment and treatment.

Comparison	ISO 23894	ISO 42001
Main purpose	Guidance on AI risk management	Requirements for an AI management system
Risk treatment role	Helps organizations select and apply suitable responses to AI risks	Requires AI risk treatment processes within the management system
Organizational focus	AI-related risks and their management	Broader AI governance, accountability, and continual improvement
Certification	Not a standalone certifiable management-system standard	Supports AI management-system certification
How they work together	Informs risk assessment and treatment methods	Provides the governance structure for managing and reviewing those activities

Organizations implementing ISO 42001 can use ISO 23894 as supporting guidance when developing their AI risk management methodology.

However, using ISO 23894 does not automatically establish conformity with ISO 42001 or compliance with AI-related regulations.

How CyberArrow GRC supports ISO 23894 risk treatment

Implementing ISO 23894 risk treatment requires organizations to coordinate risk assessments, treatment decisions, control activities, documentation, and ongoing monitoring.

When these activities are managed through disconnected spreadsheets, emails, and separate applications, teams may struggle to maintain accurate records and consistent accountability.

At CyberArrow, we help organizations simplify governance, risk management, and compliance through our integrated CyberArrow GRC platform.

Our platform supports the operational activities that organizations need when building and maintaining structured AI risk management programs.

Centralize AI risk assessments and risk registers

CyberArrow GRC helps organizations conduct structured risk assessments and maintain centralized records of identified risks.

Teams can document AI-related risk scenarios, evaluate their significance, assign ownership, and track treatment activities alongside other enterprise risks.

This helps organizations maintain a more consistent approach to risk management across departments.

Manage risk treatment activities and remediation

Once risks have been assessed, organizations need to ensure that treatment decisions lead to action.

CyberArrow GRC supports risk management workflows, ownership assignments, and remediation tracking.

These capabilities help teams monitor treatment progress, identify outstanding actions, and maintain accountability for addressing identified risks.

Connect risks with policies and controls

Effective AI risk treatment often requires organizations to implement technical, operational, and governance controls.

CyberArrow GRC helps organizations centralize policies, manage controls, and coordinate related compliance activities.

Connecting risk management activities with relevant policies and controls can improve visibility into how treatment decisions are implemented.

Automate evidence collection and compliance workflows

Maintaining documentation is important for demonstrating that risk management activities have been performed.

CyberArrow GRC helps organizations automate compliance workflows and streamline evidence collection.

This reduces repetitive administrative work and supports teams responsible for internal reviews, management reporting, and audit preparation.

Improve ongoing monitoring and reporting

AI risks can change as systems are updated, new data is introduced, and business requirements evolve.

CyberArrow GRC supports continuous internal control monitoring, compliance tracking, and centralized reporting.

These capabilities can help organizations maintain oversight of risk treatment activities and identify issues that require further attention.

CyberArrow GRC can support the management of AI-related risks, but organizations remain responsible for determining appropriate risk criteria, selecting effective safeguards, and meeting applicable legal or regulatory obligations.

Conclusion: Strengthen AI risk treatment with CyberArrow GRC

Artificial intelligence creates opportunities for organizations to improve productivity, automate operations, and make better decisions. However, these benefits must be supported by effective risk management.

ISO 23894 risk treatment helps organizations move beyond identifying AI risks and take practical action to address them.

By establishing consistent processes for risk identification, prioritization, treatment, and monitoring, organizations can improve accountability and make more informed decisions about their AI systems.

However, managing these activities through manual processes and disconnected tools can make risk management more complex and time-consuming.

At CyberArrow, we help organizations simplify these challenges through CyberArrow GRC, our integrated governance, risk, and compliance platform.

Our platform helps organizations centralize risk assessments, manage policies and controls, automate compliance workflows, collect evidence, track remediation activities, and maintain continuous visibility into their risk and compliance processes.

With structured workflows and centralized risk information, organizations can improve coordination between business, security, compliance, and risk management teams.

CyberArrow is trusted by the world’s biggest brands across the US, Europe, Africa, Asia and the Middle East. We help organizations strengthen governance, simplify compliance management, and build more efficient risk management programs.

Whether your organization is implementing ISO 23894, strengthening AI governance, or preparing for ISO 42001 certification, we can help you manage the supporting risk and compliance activities more effectively.

Ready to strengthen AI risk management and simplify GRC? Book a free CyberArrow GRC demo and discover how we can help your organization build a more effective risk management program.

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