Artificial intelligence (AI) risk management is the process of finding, checking, and reducing risks with AI systems.
Table of Contents
Understanding the need of AI risk management
AI risk management is different from regular IT risk management because of its unique challenges, such as poor training data, stolen models, biased algorithms, and unexpected behaviors. With the evolution of AI never ending, according to Forrester, “Continuous risk management must take place with the goal of producing continuous assurance”1.
AI continues to change how businesses work, including how they are dealing with the new and ever-changing security risks it poses. Attackers can damage AI models by messing with training data, steal valuable algorithms, or trick AI decisions to create unfair results. These problems need special oversight and technical protection that is made for AI in order to properly mitigate and manage the potential risks.
Poor oversight of artificial intelligence (AI) can lead to more than technical failures; companies may face regulatory fines, reputational damage, financial losses, and lawsuits when AI systems malfunction.
Research shows security and compliance worries are the top challenge for 37%2 of organizations evaluating their AI systems. Among IT leaders, this figure jumps to 44%, highlighting a big gap between adopting AI and effectively managing its risks.
Finding AI security threats
AI systems face different security risks that regular security tools can't catch or stop. Knowing these threats helps with good risk management.
Bad training data
Criminals add harmful data to training sets to break AI models. This forces models to classify things incorrectly or show unfair decisions that can aid attackers.
Stolen models
Smart attackers can copy valuable AI models by studying their results and stealing important business advantages.
Adversarial examples
Inputs intentionally crafted to fool AI systems into making incorrect predictions. For example, small tweaks can cause self-driving cars to misread traffic signs or face recognition systems to identify the wrong person.
Training data extraction
Attackers use the model’s outputs to infer or reconstruct sensitive attributes or even specific examples from the training data, revealing private information about individuals.
Behavior analysis
AI systems show predictable patterns during normal operation. Watching for changes from these patterns can signal security problems or system issues.
Performance tracking
Sudden changes in an AI model’s accuracy or performance can show attacks or other security issues. Automated monitoring can track performance and alert security teams to problems.
Activity logging
Complete logging of AI system activities shows system behavior and helps investigate security incidents. This includes tracking model requests, data access, and administrative actions.
Threat intelligence
Staying updated on new AI security threats helps organizations protect their systems early. Threat intelligence gives information about new attack methods and weak spots.
Key components of an AI risk assessment
Any good risk assessment needs a clear method that covers both technical weak spots and business effects. Here are the key components you will want to address in conducting your AI risk assessment:
Finding assets
You organization must track your entire AI stack, from the models, datasets to the development tools and systems. You can take advantage of automated tools that can find AI-related cloud resources and rank them by risk and business importance.
AI threat analysis
AI threat analysis goes beyond regular software security to include various AI attack methods including machine learning. This finds potential attack paths against AI models, training data, and systems.
Impact review
Organizations must judge how AI system failures or breaches could affect people, groups, and society. This includes checking for bias, privacy violations, and safety problems.
Risk measurement
Measuring risks helps organizations focus security spending and make smart decisions about acceptable risk levels. This includes calculating potential money losses from AI security problems and compliance violations.
How to generate strong AI governance
Like any other governance standard, strong AI governance needs teamwork across different company areas and technical fields, as well as clear and consistent rules, controls, and monitoring.
Create rules
Organizations need complete policies covering AI development, use, and operation. These policies should match business goals while meeting regulatory needs and what stakeholders expect.
Set clear jobs
Clear responsibility makes sure AI risks get managed properly throughout the system lifecycle. This means naming AI risk owners, creating oversight committees, and setting up escalation procedures.
Add technical controls
AI-specific security controls handle unique risks that traditional cybersecurity can't address. These include AI model scanning, runtime protection, and special monitoring.
Constant monitoring
AI systems need constant watching to catch performance changes, security problems, and compliance violations. Automated monitoring can track model behavior and alert security teams to issues.
Important security controls for AI systems
Security is a crucial component of any good risk management, especially in the world of AI. Protecting AI systems needs multiple security layers that address risks throughout the AI lifecycle.
Development security
Secure development ensures AI systems include security from the onset. This covers code scanning, vulnerability checks, and secure coding for AI applications.
Data protection
AI systems handle a lot of sensitive data that requires special protections. This includes data encryption, access controls, and privacy techniques.
Model security
AI models need protection from theft, tampering, and attacks. Model encryption, access controls, and checking help protect valuable AI assets.
Runtime protection
AI applications need real-time protection against attacks during operation. This includes input validation, output filtering, and behavior monitoring to spot unusual activity.
Rules and regulations for AI risk management
Following regulations becomes more important as governments create AI-specific rules. According to Forrester, “Agentic AI introduces autonomous decision-making that must comply with evolving regulations while maintaining regulatory alignment across multiple jurisdictions”3. New regulations, like the EU AI Act, require specific criteria for AI system development and use. Organizations must understand and follow applicable regulations in their areas. Industry standards, like ISO 42001, give frameworks for AI management systems that help organizations show responsible AI practices. Following these standards can reduce regulatory risk and improve stakeholder confidence.
AI systems often process personal data, making privacy regulations like GDPR directly relevant. Organizations must make sure their AI systems follow data protection requirements, keeping detailed documentation of AI system development, testing, and use to show compliance during audits.
Building an AI security team
In order to build a strong AI risk management strategy, you need in-depth AI knowledge combined with a proactive cybersecurity solution.
Required skills
AI security professionals need strong cybersecurity skills and basic fluency in how machine learning models are built, deployed, and monitored. Defending AI systems requires understanding both traditional security risks and how model behavior, data pipelines, and deployment choices create new vulnerabilities. This mix is uncommon, so hire and upskill for it, and use cross‑functional teams rather than expecting one person to know everything.
Training programs
AI security training programs teach security teams AI-specific threats, secure machine learning lifecycle practices, red teaming and incident response, compliance and privacy, and include hands-on labs. It is best to offer role-based paths for engineers, analysts, and leaders, with ongoing refreshers to keep pace with evolving risks.
Outside support
Many organizations partner with specialized AI security providers to complement their internal capabilities. These partnerships give access to expertise and tools that would be expensive to develop internally.
Continuous learning
The AI security field changes fast, requiring continuous education and skill development. Organizations must invest in ongoing learning programs to keep their teams current with new threats and technologies.
Business benefits of implementing AI risk management
Investing in AI risk management gives significant business value beyond reducing risks, including:
Competitive edge. Organizations with strong AI governance are empowered to use AI systems more confidently and quickly—enabling faster innovation and market advantage over the competitors without proper risk management in place.
Trust building. Complete AI risk management builds trust with customers, partners, and regulators, creating more spaces for new business opportunities and partnerships that need proven AI governance capabilities.
Cost prevention. Preventing AI security incidents avoids significant costs from data breaches, regulatory fines, and reputation damage. The average cost of a data breach is $4.45 million, with AI-related incidents potentially costing more.
Better efficiency. Automated AI security controls reduce manual oversight needs, while giving better protection. This allows your organization to scale AI use without increasing security overhead proportionally.
Getting started with AI risk management
Building complete AI risk management needs a structured approach that develops capabilities over time. The question isn't whether to implement complete AI risk management, but how quickly your organization can achieve an effective governance and competitive advantage through strategic investment in AI security capabilities.
- Assessment and planning
Start with a complete assessment of your current AI landscape and security position. Find capability gaps and develop a plan for addressing them. - Quick wins
Focus on basic AI security controls that provide immediate value. This includes AI asset discovery, basic monitoring, and policy development. - Step-by-step setup
Build AI risk management capabilities gradually, starting with highest-risk systems and expanding coverage over time. This approach helps learning and improvement while giving immediate protection. - Constant improvement
AI risk management is an ongoing process needing continuous refinement and improvement. Regular assessments and updates ensure capabilities remain effective and resilient against changing threats.
Where can I get help with AI risk management?
With the constant change of AI, you need a solution that evolves just as quickly to stay instep. Trend Vision One™ AI Security solution provides a multi-layered approach to protect the entire AI Stack and uses AI in the platform to improve operational efficiencies of your security teams. Learn more about AI cybersecurity at https://www.trendmicro.com/en_us/business/ai/security-ai-stacks.html
Sources:
Source 1: Pollard, J., Scott, C., Mellen, A., Cser, A., Cairns, G., Shey, H., Worthington, J., Plouffe, J., Olufon, T., & Valente, A. (2025). Introducing Forrester’s AEGIS Framework: Agentic AI Enterprise Guardrails for Information Security. Forrester Research, Inc.
Source 2: Leone, M., & Marsh, E. (2025 January). Navigating Build-versus buy Dynamics for Enterprise-ready AI. Enterprise Strategy Group.
Source 3: Pollard, J., Scott, C., Mellen, A., Cser, A., Cairns, G., Shey, H., Worthington, J., Plouffe, J., Olufon, T., & Valente, A. (2025). Introducing Forrester’s AEGIS Framework: Agentic AI Enterprise Guardrails for Information Security. Forrester Research, Inc.
Fernando Cardoso is the Vice President of Product Management at Trend Micro, focusing on the ever-evolving world of AI and cloud. His career began as a Network and Sales Engineer, where he honed his skills in datacenters, cloud, DevOps, and cybersecurity—areas that continue to fuel his passion.