Quick guide: How to identify, assess, and manage AI risks throughout the AI lifecycle

Better understanding of AI risks and limitations enables the creation of a safe space for AI innovations. Performing robust risk management processes is crucial for preventing and reducing possible negative impacts of AI on individuals, organisations, and society at large, and enhancing trust in your AI systems.

This ensures that AI technologies are used responsibly and safely, in compliance with relevant regulatory instruments. Effective AI risk management also enables organisations to foster trust among users and stakeholders by showing a commitment to addressing potential issues related to AI.

In this article, we will walk you through:

1. How to define overall AI system-level risk and business impact.

2. The basics of 6-step AI risk management process: identifying, assessing, managing and monitoring AI risks throughout the entire AI system lifecycle, ensuring reliability, safety and effectiveness of your AI system.

3. How Saidot makes AI risk management and AI governance faster

‍A quick disclaimer: There are many stages and a lot of work that goes into AI risk management, but with the right tools and processes it can be efficient. Just read on and learn.

“With the emergence of AI regulation in different parts of the world, effective AI risk management is increasingly important for ensuring compliance and maintaining public trust.”

‍–Edla Aittokallio, AI Governance Specialist at Saidot.

How to define overall AI system level risk and business impact

Understanding and balancing the overall risk and benefits of an AI system is crucial for its successful development and deployment by an organisation. This involves thorough AI system risk classification and assessment of business impact.

AI system risk classification assesses an AI system's overall risk based on its specific use context, intended purpose, and related regulatory and business risks. Systems can be categorised as low-risk, medium-risk, high-risk, or prohibited. The organisation’s own criteria and the risk management process they conduct on the system can guide the classification process.

The business impact measures the potential benefits of an AI system, such as increased efficiency, new services, and overall productivity. This impact is categorised as high, medium, or low. Evaluating business impact alongside the overall risk level allows for comparing the AI system's potential benefits and associated risks. This provides essential information for managing the AI governance portfolio.

What does the AI risk management process look like in practice?

Effective AI risk management involves 6 steps:

1. Identification
2. Documentation
3. Evaluation
4. Treatment strategies
5. Assessing residual risks
6. Risk monitoring

Saidot's AI risk management process

1. Identify risks

First, you need to identify relevant risks for the registered AI systems of your organisation. During the identification process, you should consider factors such as the business contexts of your systems and system components. Plus, It is important to identify context-specific risks that your AI systems may pose.

2. Document risks

After the identification stage, your organisation should document the risks identified. Documenting the identified risks facilitates an organised and accessible way to track and evaluate them. It also makes it possible to monitor changes in specific risks and identify the tasks relevant for managing each risk.

This process involves recording:

1. The risk name and its description
2. Risk owner (the person managing the risk)
3. Risk type (the category of potential negative outcomes or threats of using your AI system)
4. Risk source

3. Evaluate risks

Risk evaluation includes assessing the inherent likelihood and impact of each risk. On top of that, evaluating the marginal risk (how the introduction of AI technology changes the risk) enables your organisations to differentiate AI-specific risks from those that already exist.

This comprehensive assessment helps your organisation effectively prioritise specific risks as well as their risk management resources, ensuring your mitigation efforts are targeted where they are most needed.

Risk criteria ‍

Risk criteria define the benchmarks your organisation uses to assess and measure the significance of risks. Standardising your interpretation of risks is important to maintain consistency in risk evaluation across your AI inventory.

AI risk criteria should support your organisation in distinguishing acceptable from non-acceptable risks, performing AI risk assessments, conducting AI risk treatments, and assessing AI risk impacts. Risk criteria should include the risk acceptance criteria and the criteria for performing risk assessments.

“It is important to clearly define and communicate the organisation’s risk appetite and tolerance. This helps making informed decisions about the necessary types of risk treatment and determine when risks can be accepted.”

–Edla Aittokallio, AI Governance Specialist at Saidot.

How to evaluate inherent risk

‍Inherent risk score provides a baseline understanding of the impact and likelihood of possible adverse events, which means you need to assess both.

Risk likelihood

Risk likelihood refers to assessing the probability of a risk scenario to occur. When assessing the risk likelihood, you should consider several factors influencing the possible risk scenario, such as types and number of risk sources as well as frequency and severity of threats.

Risk impact

Risk impact refers to assessing the outcome of the possible risk scenario. Business impact analysis should assess the extent to which the organisation is affected by considering elements such as severity of the impact on the organisation.

Impact analysis for individuals should assess the extent to which the individuals are affected by the development or use of AI by the organisation, or both by considering elements such as types of data used about the individuals as well as potential bias, fairness and safety impacts on individuals.

Impact analysis for society should assess the extent to which the individuals are affected by the development or use of AI by the organisation, or both by considering, for instance, scope of the impact on society.

“Organisations can tailor the risk scales to fit their specific context to ensure their risk evaluation processes are systematic and effective.”

‍–Edla Aittokallio, AI Governance Specialist at Saidot.

Marginal risk

Marginal risk refers to the change in risk resulting from introducing AI technology. Evaluating the marginal risk of AI systems enables your organisation to differentiate the risks associated with introducing AI technology from those already existing in your organisation.

This helps organisations understand the source of risks so they can manage them effectively.

4. Treat risks

Risk treatment refers to selecting and implementing options for addressing specific risks. The input of a risk treatment strategy is based on the risk evaluation outcomes in the form of a prioritised set of risks to be treated, based on the inherent risk (risk likelihood and impact).

The output of this process is a set of necessary actions that you should deploy or enhance in accordance with the chosen risk treatment strategy. By doing this, you’ll modify the risk's impact and likelihood to reduce the residual risk level as much as possible so that it meets your organisation’s criteria for acceptance.

Risk treatment strategies

Here are treatment strategies for your AI risks:

* Avoid: Eliminate the risk by either abstaining from the activities that introduce it or by removing its source.
* Transfer: Allocate the risk to a third party, for example through contractual agreements or insurance policies.
* Mitigate: Reduce the risk effects by altering its probability or impact.
* Accept: Decide to accept the risk by making an informed decision.

Not every treatment option is necessarily suitable for all situations. Based on the evaluated inherent risk level, we propose default treatment strategy options (underlined):

AI risk management evaluation scales

5. Assess residual risk

Whereas inherent risk refers to the amount of risk that exists before it is addressed, residual risk refers to the remaining risk level after risk treatments. In other words, the risk should be smaller after the treatment strategy has been carried out.

Assessing residual risk level is crucial for robust risk management: it indicates the effectiveness of the treatment strategy and its impact on the inherent risk level, and it informs decision-making on the potential next steps referring to each individual risk.

6. Monitor risks

Regular monitoring and review of the risks, their treatments and the risk management process should be planned as part of your organisation’s risk management strategy.

The monitoring process should be ongoing and include all aspects of the risk management process to ensure its effectiveness, gather information to improve the process and identify emerging risks.

How Saidot enables efficient and robust AI risk management

Guess what? Saidot’s platform helps your organisation with the steps of the AI risk management process, which means everything you just read can be done on our platform.

The risk management methodology by Saidot is based on industry standards and aligned with the most common risk management best practices.

Our step-by-step approach ensures systematic and effective workflows and is assisted with an extensive Risk library on the Saidot platform. Risk database contains information about the potential risks and mitigations that can be connected to specific AI systems, models and their context.

The recommended risks and suggested mitigations can be used as a baseline when managing risks of specific AI system to make the process more effective.

Here’s how you can do efficient and robust AI risk management on Saidot’s platform:

1. Adopt risk management practices that are aligned with key AI risk management standards.
2. Implement a systematic risk management process to identify, document and evaluate risks, create risk treatment strategies, identify mitigations and monitor risks.
3. Make use of an expert-curated risk register to support the identification and monitoring of AI risks efficiently.
4. Prioritise risks based on the systematic evaluation, identify the most relevant risks for your organisation, and avoid over-mitigating them by selecting an appropriate treatment strategy for each identified risk.

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Quick guide: How to identify, assess, and manage AI risks throughout the AI lifecycle

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