In most organizations, artificial intelligence models are “black boxes,” where only data scientists understand what exactly AI does. That can create significant risk for organizations.
Large, sensitive datasets are often used to train AI models, creating privacy and data breach risks. The use of AI increases an organization’s threat vectors and broadens its attack surface. AI further creates new opportunities for benign mistakes that adversely affect model and business outcomes.
Risks that are not understood cannot be mitigated. A recent Gartner survey of chief information security officers reveals that most organizations have not considered the new security and business risks posed by AI or the new controls they must institute to mitigate those risks. AI demands new types of risk and security management measures and a framework for mitigation.
Here are the top five priorities that security and risk leaders should focus on to effectively manage AI risk and security within their organizations:
1. Capture the extent of AI exposure
Machine learning models are opaque to most users, and unlike normal software systems, their inner workings are often opaque to even the most skilled experts. Data scientists and model developers generally understand what their machine learning models are trying to do, but they cannot always decipher the internal structure or the algorithmic means by which the models process data.
This lack of understandability severely limits an organization’s ability to manage AI risk. The first step in AI risk management is to inventory all AI models used in the organization, whether they are a component of third-party software, developed in-house or accessed via software-as-a-service applications. This should include identifying interdependencies among various models. Then rank the models based on operational impact, with the idea that risk management controls can be applied over time based on the priorities identified.
Continue reading: https://siliconangle.com/2022/06/08/manage-artificial-intelligence-risk-security-focus-five-priorities/
Large, sensitive datasets are often used to train AI models, creating privacy and data breach risks. The use of AI increases an organization’s threat vectors and broadens its attack surface. AI further creates new opportunities for benign mistakes that adversely affect model and business outcomes.
Risks that are not understood cannot be mitigated. A recent Gartner survey of chief information security officers reveals that most organizations have not considered the new security and business risks posed by AI or the new controls they must institute to mitigate those risks. AI demands new types of risk and security management measures and a framework for mitigation.
Here are the top five priorities that security and risk leaders should focus on to effectively manage AI risk and security within their organizations:
1. Capture the extent of AI exposure
Machine learning models are opaque to most users, and unlike normal software systems, their inner workings are often opaque to even the most skilled experts. Data scientists and model developers generally understand what their machine learning models are trying to do, but they cannot always decipher the internal structure or the algorithmic means by which the models process data.
This lack of understandability severely limits an organization’s ability to manage AI risk. The first step in AI risk management is to inventory all AI models used in the organization, whether they are a component of third-party software, developed in-house or accessed via software-as-a-service applications. This should include identifying interdependencies among various models. Then rank the models based on operational impact, with the idea that risk management controls can be applied over time based on the priorities identified.
Continue reading: https://siliconangle.com/2022/06/08/manage-artificial-intelligence-risk-security-focus-five-priorities/
