AI Model Risk Management involves detecting, mitigating, and addressing AI threats. It emphasizes formal AI risk management frameworks and includes tools, techniques, and principles.
Artificial intelligence risk management aims to minimize its drawbacks and maximize its benefits.
AI risks management
Governance of AI includes risk management. AI governance safeguards AI tools and systems from harm.
Artificial intelligence risk management is part of AI governance. AI model risk management targets vulnerabilities and threats to protect AI systems. To ensure safety, fairness, and human rights, AI governance sets guidelines, regulations, and standards for research, development, and application. IBM Consulting can integrate appropriate AI governance into your business.
The Importance Of AI Risk Management
AI adoption has increased across industries in recent years. According to McKinsey, 72% of organizations employ AI, up 17% from 2023. Organizations pursue AI’s benefits innovation, efficiency, and productivity but don’t always address its risks privacy, security, and ethical and legal challenges.
Leaders know this problem. According to an IBM Institute for Business Value (IBM IBV) survey, 96% of CEOs think generative AI increases security risks. Meanwhile, the IBM IBV showed that 24% of generative AI projects are secure.
AI model risk management can help organizations maximize AI systems’ potential without compromising ethics or security.
Artificial Intelligence Risk Management
Like other security risks, AI risk measures how likely and damaging an AI related attack is to effect an organisation. Each AI model and use case has various dangers, but four categories exist:
- Data threats
- Simulate dangers
- The operational hazards
- Ethics and law dangers
- AI systems and organisations can suffer financial losses, reputational damage, regulatory penalties, public confidence erosion, and data breaches if these risks are not addressed properly.
Data threats
Data sets used by AI systems may be tampered with, breached, biased, or attacked. From AI creation to training and deployment, organizations may reduce these risks by ensuring data integrity, security, and availability.
Common data threats
- Security: AI systems’ biggest and most important challenge is data security. Threat actors can corrupt AI data sets, causing unauthorized access, data loss, and confidentiality issues for organizations.
- AI systems manage sensitive personal data, which can lead to privacy breaches and regulatory and legal difficulties for organisation.
- Training data determines AI model risk management reliability. Data distortion can cause false positives, inaccurate outputs, and poor decision making.
Simulate dangers
Threat actors may steal, reverse engineer, or manipulate AI models. AI models’ architecture, weights, and parameters the key components that determine their behavior and performance can be tampered with by attackers.
AI Model Risk Management
Some frequent model risks
- Input data manipulation: Adversarial attacks trick AI systems into producing inaccurate predictions or classifications. For instance, attackers may give AI algorithms hostile instances to distort or interfere with decision making.
- PROMPT injections attack huge language models. Hackers trick generative AI systems into disclosing sensitive data, spreading falsehoods, or worse. Even simple cue injections can cause AI chatbots like ChatGPT break system rules and utter stuff.
- Analyzing complex AI models can be difficult, making it hard for users to understand their decisions. Lack of transparency hinders bias identification and accountability and erodes AI system and provider trust.
- Threat actors target AI systems during development, deployment, and maintenance via supply chain attacks. Attackers could exploit weaknesses in third party AI development components to breach data or get unauthorized access.
AI And Machine Learning In Risk Management
AI model risk management are not magic, but rather complex code and machine learning algorithms. Like other technology, they have operational hazards. If ignored, these risks can cause system failures and security vulnerabilities that threat actors can exploit.
Some frequent operational risks
- Model drift, where data or data point associations alter, can affect AI model performance.
- Over time, a fraud detection model may grow inaccurate and miss fraudulent transactions.
- Sustainability concerns: AI systems are difficult and need scale and assistance. Sustainability issues can make maintaining and updating these systems difficult, resulting in inconsistent performance, higher operating costs, and energy use.
- AI system integration with IT infrastructure is difficult and resource intensive. Data silos, system interoperability, and compatibility plague organizations. By increasing cyberthreats’ attack surface, AI systems can develop new vulnerabilities.
- Absence of accountability: AI systems are new, thus many companies lack suitable corporate governance mechanisms. AI systems are generally unsupervised. Only 18% of organizations have a council or board that can make responsible AI governance decisions, according to McKinsey.
AI Risk Management Certification
Ethics and law dangers
When creating and implementing AI systems, organizations risk privacy violations and biased results if safety and ethics are not prioritized. Biased recruiting data may promote gender or racial stereotypes and develop AI models that favors particular demographic groupings.
Typical ethical and legal risks
Lack of transparency: Organizations that refuse to disclose their AI systems risk losing public trust.
Not complying with government regulations: The GDPR and sector specific standards can result in costly fines and punitive consequences.
AI biases: Training data can prejudice AI systems, resulting in biased hiring decisions and unequal financial services access.
Privacy, autonomy, and human rights issues might arise from AI judgements. When handled poorly, these issues can damage an organization’s reputation and public trust.
Without explainability, AI systems’ decisions are hard to understand and defend.
Unexplainability can destroy trust, reputation, and legal issues. A CEO not understanding where their LLM receives training data might lead to poor headlines or regulatory problems.
Artificial Intelligence Risk Management Framework
Many organizations use AI Model Risk Management frameworks to handle risks across the AI lifecycle.
These guidelines are playbooks that explain an organization’s AI rules, procedures, roles, and duties. Organisations can build, deploy, and operate AI systems using AI risk management frameworks to minimise risks, uphold ethics, and comply with regulations.
AI risk management frameworks
- A NIST AI Risk Management Framework
- EU AI ACT
- ISO/IEC norms
- AI executive directive from the US
- AI RMF by NIST
- AI risk was organized by NIST’s AI Risk Management Framework (AI RMF) in January 2023. NIST AI RMF has created AI Model Risk Management standards since then.
AI Risk Management Software
AI RMF helps organizations design, develop, implement, and employ AI systems to control risks and encourage trustworthy, responsible AI practices. The voluntary AI RMF, developed with the public and private sectors, applies to any company, industry, or area.
Two sections make up the framework. Part 1 covers trustworthy AI system threats and traits. AI RMF Core Part 2 lists four functions to help organizations manage AI system risks:
- Create an AI Model Risk Management culture in your organisation.
- Map: Business-specific AI threats
- Evaluate AI risks
- Handle mapped and assessed risks
- The EU AI Act
- AI development and use in the EU are regulated by the EU AI Act.
- The act regulates AI systems based on their risks to human health, safety, and rights.
- The act also regulates building, training, and deploying general-purpose AI models like ChatGPT and Google Gemini.
AI Risk Management ISO/IEC Norms
AI Model Risk Management standards are available from ISO and IEC.
In AI risk management, ISO/IEC standards emphasize openness, accountability, and ethics. They also provide actionable AI model risk management principles from design and development to deployment and operation.
AI executive directive from the US
At the end of 2023, the Biden administration issued an executive order on AI security. This comprehensive directive establishes new AI technology risk management criteria, however it is not a risk management framework.
Trustworthy, transparent, explainable, and accountable AI is one of the executive order’s main concerns. The executive order created a precedent for private sector AI risk management.
How AI risk management aids businesses
Despite varying from organisation to organisation, AI risk management can give certain common basic benefits when implemented correctly.
Better security
Organizations may improve cybersecurity and AI security using AI risk management. Enterprises can discover AI lifecycle risks and weaknesses by undertaking frequent risk assessments and audits. Their risk mitigation techniques can be implemented after these assessments.
This method may incorporate data security and model robustness improvements. Institutional changes like ethical rules and access controls may be needed. Organisation can reduce data breaches and cyberattacks by taking a proactive strategy to threat detection and response.
Better judgement
AI risk management can also improve group decision making. Organizations can assess their risks using qualitative and quantitative studies, statistical approaches, and expert opinions. This holistic view helps organizations prioritize high-risk threats and make better AI adoption decisions, balancing innovation and risk mitigation.
Regulation compliance
The GDPR, CCPA, and EU AI Act protect sensitive data worldwide.
Those who break these laws face severe fines and punishments. AI model risk management can help organizations comply and stay in good standing as AI rules grow almost as quickly as the technology.
Resilience operations
Artificial intelligence risk management helps companies minimize disruption and maintain business continuity by addressing AI system issues in real time. By helping organizations build defined AI management practices and processes, AI Model Risk Management may improve accountability and sustainability.
Trust and openness increased
AI Model Risk Management prioritizes trust and transparency to promote AI ethics.
Executives, AI developers, data scientists, users, policymakers, and ethicists are involved in most AI model risk management processes. AI systems are designed and deployed ethically with all stakeholders in mind with this inclusive approach.
Constant testing, verification, and monitoring
Testing and monitoring an AI system’s effectiveness helps organizations spot new dangers faster. Monitoring helps organizations comply with regulations and mitigate AI hazards earlier, lowering threats.
AI Risk Management
AI technologies can improve labour efficiency, but they can pose risks. Nearly every enterprise IT can be misused.
Organizations can use generative AI. It should be treated like any other technical tool. That involves knowing the risks and taking precautions to prevent an assault.
IBM watsonx.governance lets organizations administer, manage, and track AI initiatives in one place. Watsonx.governance can control any vendor’s generative AI models, assess model health and accuracy, and automate compliance operations.
