In today’s evolving technological landscape, AI is turning into a present-day reality guiding on how to operate businesses. AI provides previously unheard-of chances for creativity, efficiency, and competitive advantage, from automating repetitive processes and customising client experiences to enabling sophisticated data analytics. Businesses are inheriting a new range of risks as they incorporate AI more and more into their core operations. If these risks are not adequately managed, they may have serious, reputational, and even legal consequences.
These enticing advantages of AI might occasionally obscure the urgent need for effective risk management techniques. These hazards are complex and include everything from algorithmic prejudice and data privacy to security flaws, difficulties with compliance, and moral conundrums. Businesses run the risk of weakening their own AI initiatives and exposing themselves to unanticipated liabilities if they don’t take a proactive and compressive strategy to detecting, assessing, mitigating, and monitoring these risks.
AI risk management is part of AI governance which focuses on identifying issues related to AI like data privacy, Data integrity and threats to system wherein it aims to keep AI systems safe from harm. A recent IBM(Institute for business Value) study found that 96% leaders are adopting generative AI makes security breach, only 24% have there AI project secured.
In risk management AI has become a tool to improve efficiency and productivity while reducing costs. This is because of the function AI performs i.e., ability to handle and analyse large unstructured data with lesser human interaction. This helps businesses to lower operational, regulatory and compliance costs thereby also allowing to build competence around customer intelligence, enabling the successful implementation of goals and reduce losses.
The Risk of AI is Divided into 4 Categories:
- Data risks:
There is always a threat to cyberattacks like DOS, hacking etc. The organizations can mitigate these risks by protecting data integrity, security and availability from taring and development to protect data security and data privacy and data integrity.
- Model risks:
Herein target are the AI models for theft or manipulation wherein hackers can compromise with the AI model integrity, weights, core function, AI behaviour or performance. Some of its examples are Adversarial attacks( To manipulate the input data), Prompt injections( This easily makes system like ChatGPT etc say things they shouldn’t), Supply chain attacks(This manipulates third party components) etc.
- Operational Risks:
AI models do possess the operational risk if left unanswered can lead to system failure and security risks which can easily allow the attacker to exploit the AI model. Some of its examples are: Lack of accountability, integration challenges, sustainability issue(Not adapting to new technology) etc.
- Ethical and legal risks:
If organization does not look forward for the safety of AI model they can risk privacy and lead to bias outcomes which also includes lack of transparency , algorithms biases, not comply with legal requirement.
AI powered risk management solutions offer a compelling avenue for enhancing model risk management, including robust back testing and model validation as well as sophisticated stress testing. Its advantages are as follow:
1. Superior Forecasting Accuracy:
Machine learning that offers improved forecasting accuracy due to model’s ability i.e., to capture non-linear effects between scenario and variables and risk factors this approach is different from traditional regression models.
2. Optimized variable selection process
Extracting features and variables for internal risk models is a time-consuming process. However, by combing Machine learning algorithms with big data analytics platforms, we can efficiently process massive amounts of data and extract comprehensive set of variables. This rich feature covers creation of robust, data driven risk models for stress testing.
3. Richer data segmentation
Machine learning algorithms allows many segment data and superior segmentation of data.By the use of unsupervised ML algorithms, combing both distance a density approaches for clustering becomes a possibility which helps in accuracy.
4. Improved decision making:
Risk management helps organization clear view of associated risks and helps in prioritizing high-risk threats and take more informed decisions around AI model and balance AI with need of risk mitigation.
5. Operational resilience:
AI risk management helps organization minimize disruption and ensure businesses address the risk in real time this helps in greater accountability and long-term sustainability allowing organization to establish clear management and practices in AI use.
Check out Adeptive AI’s Governance platform which risk management easier with AI powered compliances and and model approval process to ensure that your AI system is free from legal as well as technical risks
SOME OF THE USE CASES ARE:
- Credit risk modelling
Because Machine Learning (ML) models are frequently difficult for regulators to understand and validate, banks have historically depended on conventional credit risk models to predict events like loan defaults. Within these current regulatory frameworks, machine learning models can still be useful for fine-tuning the variable selection procedure and optimising parameters.
AI-based decision tree techniques can help to produce rational traceable decisions rules even if non-linear nature. Techniques like support vector machines that can predict credit risks like loss given default or Probability of Default, whereas unsupervised learning techniques can help to explore traditional credit risks modelling.
- Fraud Detection
Businesses or banks have been using machine learning fore years. Since at times transaction are through credit cards or by asking clients data, the AI process this data, such ML are used to predict, learn this huge data.
By use of AI risk management privacy of such data can be ensured provided any scam associated with this can be easily detected by AI.
- Trade behaviour
By analysing email traffic, data collected etc. combined with trading portfolio, systems are able to predict the probability of insider trading, misconduct while trading that can prevent manipulation of market, organization reputation and regulate organization behaviour.
AI RISK MANAGEMENT FRAMEWORKS ARE:
The NIST AI Risk Management Framework
The AI Risk Management Framework (AI RMF), which was released by the National Institute of Standards and Technology (NIST) in January 2023, has come up as necessary standard for managing AI-related risks. This voluntary framework is created by both public and private sectors for managing AI risks, to assist organisation from all sectors and utilising AI system in ethical and reliable way.
The structure of the AI RMF is divided into two main sections: Part 1 summarises the issues surrounding AI and describes the characteristics of trustworthy AI systems, it also provides with four essential to reduce risks connected to AI systems. In Part 2, the AI RMF Core: Govern: Creating an AI risk management-focused organisational culture. Map: Determining and placing AI hazards in relation to particular business operations. Measure: Examining and assessing hazards associated with AI. Manage: Putting plans into action to deal with the hazards that have been identified, quantified, and mapped.
The EU AI ACT
Article 9 of the EU AI act focuses on risk management which focuses on A continuous and iterative risk management strategy must be established, documented, and maintained by organisations utilising high-risk AI systems over the duration of the AI system’s existence. When the AI is utilised as intended or even abused, this system identifies and evaluates predictable hazards to health, safety, and fundamental rights. It also necessitates assessing fresh dangers that surface via post-market surveillance. This analysis necessitates the implementation of suitable risk management strategies. These precautions ought to concentrate on hazards that the AI system’s architecture or technical data can reduce. Making ensuring the AI system’s residual risk is manageable is the aim.
This entails giving design priority for risk elimination or reduction, followed by mitigation and control strategies for risks that cannot be avoided, and lastly, giving users the knowledge and training they require. To guarantee constant performance and compliance, high-risk AI systems must be extensively tested both during development and prior to deployment using predetermined metrics.
Real-world scenarios may be incorporated into this testing. Providers should pay particular attention to the possible negative effects on people under the age of eighteen and other vulnerable groups when putting these risk management procedures into practice. Crucially, providers can incorporate these AI risk management criteria into their current processes provided they are already bound by other EU legislation pertaining to internal risk management.
Hence by this we can see that this article provides a practical hierarchy for risk mitigation, emphasizing design-based elimination or reduction, followed by control measures and then comprehensive information and training for users, all while maintaining an acceptable residual risk.
Crucially, it highlights the importance of thorough testing throughout the development process and prior to development to ensure consistent performance and compliance with a specific focus on protecting vulnerable groups thereby it also allows businesses to integrate AI risk management procedures making requirement into existing internal risk management procedures making AI practices adaptable.
The act highlights three essential pillars which forms basis for risk managers to consider in their organisation:
- Development of AI strategy and transportation into a suitable governance framework which is demonstrated by a policy document and end-to-end processes implementation.
- To invest in technology and continuous training of employees and partners.
- Governance and technology designed in a way that understands the audit requirement or formal certification
ISO/IEC Standards
The International organization International Electrotechnical Commission (IEC) provides with standards on AI risk management that aims on transparency, accountability and ethical legal consideration required for risk management framework.
To understand more on AI risk management and its legal compliances you can to our website for more information. Click here to read more about AI risk management
CONCLUSION:
AI’s transformative power offers undeniable advantages to businesses, but these benefits are connected with a new form of risks spanning data integrity, legal compliances, data privacy, morality issues, operational issues . Ignoring these dangerous threats can lead to significant reputational and legal repercussions. By AI risk management businesses can reduce these AI risk which helps them improve their data drive models, detect fraud, improve trust and transparency.
Frameworks like NIST AI RMF and the EU AI act provides essential guidance, advocating for continuous, lifecycle-long risk assessment, transparent design, rigours testing and focus on trustworthy . By prioritizing structured governance approach, investing in technology, following legal requirements complexing related to AI risk management can be tackled down.
At Adaptive.AI we aim to focus on the requirement of having a strong risk management framework to ensure efficiency of AI along with risk associated with it. We look forward for a futuristic AI to tackle down challenges linked with AI so that business transparency can be increased
