Predictive AI is widely used in applications like network surveillance, fraud detection and supply chain management. Here\u2019s how it works.<\/p>\n\n\n\n
Machine learning tools, a subsection of AI, rapidly \u201cread\u201d and analyze reams of historical data to find patterns. Historical data can mean anything from network traffic patterns to consumer behavior. Since machine learning tools can analyze vast datasets, they find subtle patterns that might not be evident to a human analyst working their way slowly through the same data. This kind of predictive analysis helps organizations identify risks before they escalate.<\/p>\n\n\n\n
Once ML identifies the patterns, it can use them to make highly specific and accurate predictions. That can mean, for example, predicting website traffic and preventing unexpected outages due to increased usage. It can also mean spotting the warning signs of new computer viruses or identifying phishing emails.<\/p>\n\n\n\n
<\/a>Generative AI<\/strong><\/h3>\n\n\n\nGenerative AI (GenAI) is often discussed in terms of its content creation capabilities, but the technology also has enormous potential for risk management.<\/p>\n\n\n\n
GenAI can rapidly synthesize data from a wide range of inputs and use it to create a coherent analysis. For example, GenAI can make predictions about supply chain disruptions, based on weather patterns, geopolitical issues and market demand. Many generative systems use natural language processing to interpret context, summarize information and support more accurate decisions.<\/p>\n\n\n\n
GenAI can also come up with solutions to the problems it identifies. The technology excels at breaking down silos and drawing connections between different sources of information. For example, the technology can suggest alternative shipping routes or suppliers in the event of a supply chain disruption.<\/p>\n\n\n\n
It’s worth noting that, like any other AI tool, generative AI does best with human oversight. GenAI analysis should never be accepted at face value. Rather, employees can use it as an inspiration or a jumping-off point for further planning. Human expertise should always play a key role in the planning process, since GenAI isn’t always accurate.<\/p>\n\n\n\n
<\/a>Adaptive Risk Modeling<\/strong><\/h3>\n\n\n\nAI tools are capable of continuous learning and real-time analysis. Those capabilities lay the groundwork for adaptive risk modeling.<\/p>\n\n\n\n
Adaptive risk modeling allows for a dynamic understanding of risk factors, instead of the traditional static approach. The old way of calculating risk relied on identifying patterns in historical data and using a linear model with a simple cause-and-effect analysis.<\/p>\n\n\n\n
In contrast, adaptive risk modeling uses machine learning and deep learning to continually scan data sets for changes or new patterns. Instead of a static, linear model, AI risk modeling can build a dynamic model and continually update it.<\/p>\n\n\n\n
Use Cases for AI Risk Management Tools<\/h2>\n\n\n\n![\"\"](\"https:\/\/www.carahsoft.com\/wordpress\/wp-content\/uploads\/2025\/12\/300x300.png\")
<\/figure><\/div>\n\n\nAI is widely used in the Public and Private Sectors to predict and manage risk, even with <\/a>third parties<\/a> involved. Here are some of the common use cases.<\/p>\n\n\n\n<\/a>Federal Government Use Cases<\/strong><\/h3>\n\n\n\nA growing number of Federal agencies use AI tools to increase efficiency in their work. Some are beginning to pilot AI-powered agents to automate routine tasks and provide real-time recommendations for employees.<\/p>\n\n\n\n
\n- The Department of Labor leverages AI chatbots to answer inquiries about procurement and contracts.<\/li>\n\n\n\n
- The Patent and Trademark Office uses AI to rapidly surface important documents.<\/li>\n\n\n\n
- The Centers for Disease Control uses AI tools to track the spread of foodborne illnesses.<\/li>\n<\/ul>\n\n\n\n
<\/a>Financial Sector<\/strong><\/h3>\n\n\n\nLenders increasingly use AI tools to assess the risk of issuing loans. Because AI can collect and analyze large data sets, the technology provides a comprehensive way to assess creditworthiness.<\/p>\n\n\n\n
Financial institutions also use AI for fraud detection. AI tools can spot patterns in typical customer behavior and identify anomalies that could indicate fraud.<\/p>\n\n\n\n
<\/a>Insurance Industry<\/strong><\/h3>\n\n\n\nInsurance companies frequently use AI for underwriting, including risk assessment and risk mitigation. AI is also a useful tool for processing claims and searching for fraud.<\/p>\n\n\n\n
Generative AI is also often used to provide frontline services to customers. For example, chatbots answer straightforward questions, provide triage and refer more complex questions to human operators.<\/p>\n\n\n\n
Risks Associated with AI Technologies<\/h2>\n\n\n\n
AI is a valuable tool in mitigating risk, but it\u2019s important to <\/a>be aware of the risks<\/a> the tools themselves present.<\/p>\n\n\n\nChief among those risks is the problem of algorithmic bias. AI and ML excel at identifying patterns and codifying them. However, this means that AI is only as good as the data that feeds it. If AI\/ML tools are trained on biased data, the tools will codify the biases embedded in that data. AI\/ML takes the unspoken prejudices in datasets and turns them into hard and fast rules, which inform every decision going forward.<\/p>\n\n\n\n
Agencies must also consider data privacy implications when AI tools process sensitive or regulated data. If human operators do not question the algorithm’s output, there’s a real risk that bias will become deeply ingrained, causing lasting harm to individuals and organizations and even creating regulatory compliance issues.<\/p>\n\n\n\n
Addressing AI Bias<\/h2>\n\n\n\n
Federal agencies and contractors must understand exactly how AI tools are being deployed. Operators should frequently look \u201cunder the hood\u201d of the AI algorithms, asking questions about how the outputs are generated. Opening the \u201cblack box\u201d allows organizations to check for bias and prevent it from being codified. Strong data ethics practices ensure that AI systems are trained on fair, transparent and accountable data sources.<\/p>\n\n\n\n
It\u2019s best practice to implement a cross-functional AI governance council or team to oversee artificial intelligence. It\u2019s also important to work closely with a trusted partner who has experience <\/a>integrating AI into a GRC platform<\/a>. The best AI tools help humans manage a Federal agency with efficiency. The question is, how to make the most of the available technology while mitigating the associated risk.<\/p>\n<\/head>","protected":false},"excerpt":{"rendered":"Artificial intelligence (AI) is a valuable risk management tool, but it also poses a degree of risk. As AI becomes more prevalent, it opens new possibilities while simultaneously raising new concerns. Federal agencies and contractors have a responsibility to closely … Continue reading
AI tools are capable of continuous learning and real-time analysis. Those capabilities lay the groundwork for adaptive risk modeling.<\/p>\n\n\n\n
Adaptive risk modeling allows for a dynamic understanding of risk factors, instead of the traditional static approach. The old way of calculating risk relied on identifying patterns in historical data and using a linear model with a simple cause-and-effect analysis.<\/p>\n\n\n\n
In contrast, adaptive risk modeling uses machine learning and deep learning to continually scan data sets for changes or new patterns. Instead of a static, linear model, AI risk modeling can build a dynamic model and continually update it.<\/p>\n\n\n\n
Use Cases for AI Risk Management Tools<\/h2>\n\n\n\n<\/figure><\/div>\n\n\nAI is widely used in the Public and Private Sectors to predict and manage risk, even with <\/a>third parties<\/a> involved. Here are some of the common use cases.<\/p>\n\n\n\n<\/a>Federal Government Use Cases<\/strong><\/h3>\n\n\n\nA growing number of Federal agencies use AI tools to increase efficiency in their work. Some are beginning to pilot AI-powered agents to automate routine tasks and provide real-time recommendations for employees.<\/p>\n\n\n\n
\n- The Department of Labor leverages AI chatbots to answer inquiries about procurement and contracts.<\/li>\n\n\n\n
- The Patent and Trademark Office uses AI to rapidly surface important documents.<\/li>\n\n\n\n
- The Centers for Disease Control uses AI tools to track the spread of foodborne illnesses.<\/li>\n<\/ul>\n\n\n\n
<\/a>Financial Sector<\/strong><\/h3>\n\n\n\nLenders increasingly use AI tools to assess the risk of issuing loans. Because AI can collect and analyze large data sets, the technology provides a comprehensive way to assess creditworthiness.<\/p>\n\n\n\n
Financial institutions also use AI for fraud detection. AI tools can spot patterns in typical customer behavior and identify anomalies that could indicate fraud.<\/p>\n\n\n\n
<\/a>Insurance Industry<\/strong><\/h3>\n\n\n\nInsurance companies frequently use AI for underwriting, including risk assessment and risk mitigation. AI is also a useful tool for processing claims and searching for fraud.<\/p>\n\n\n\n
Generative AI is also often used to provide frontline services to customers. For example, chatbots answer straightforward questions, provide triage and refer more complex questions to human operators.<\/p>\n\n\n\n
Risks Associated with AI Technologies<\/h2>\n\n\n\n
AI is a valuable tool in mitigating risk, but it\u2019s important to <\/a>be aware of the risks<\/a> the tools themselves present.<\/p>\n\n\n\nChief among those risks is the problem of algorithmic bias. AI and ML excel at identifying patterns and codifying them. However, this means that AI is only as good as the data that feeds it. If AI\/ML tools are trained on biased data, the tools will codify the biases embedded in that data. AI\/ML takes the unspoken prejudices in datasets and turns them into hard and fast rules, which inform every decision going forward.<\/p>\n\n\n\n
Agencies must also consider data privacy implications when AI tools process sensitive or regulated data. If human operators do not question the algorithm’s output, there’s a real risk that bias will become deeply ingrained, causing lasting harm to individuals and organizations and even creating regulatory compliance issues.<\/p>\n\n\n\n
Addressing AI Bias<\/h2>\n\n\n\n
Federal agencies and contractors must understand exactly how AI tools are being deployed. Operators should frequently look \u201cunder the hood\u201d of the AI algorithms, asking questions about how the outputs are generated. Opening the \u201cblack box\u201d allows organizations to check for bias and prevent it from being codified. Strong data ethics practices ensure that AI systems are trained on fair, transparent and accountable data sources.<\/p>\n\n\n\n
It\u2019s best practice to implement a cross-functional AI governance council or team to oversee artificial intelligence. It\u2019s also important to work closely with a trusted partner who has experience <\/a>integrating AI into a GRC platform<\/a>. The best AI tools help humans manage a Federal agency with efficiency. The question is, how to make the most of the available technology while mitigating the associated risk.<\/p>\n<\/head>","protected":false},"excerpt":{"rendered":"Artificial intelligence (AI) is a valuable risk management tool, but it also poses a degree of risk. As AI becomes more prevalent, it opens new possibilities while simultaneously raising new concerns. Federal agencies and contractors have a responsibility to closely … Continue reading
<\/figure><\/div>\n\n\nAI is widely used in the Public and Private Sectors to predict and manage risk, even with <\/a>third parties<\/a> involved. Here are some of the common use cases.<\/p>\n\n\n\n A growing number of Federal agencies use AI tools to increase efficiency in their work. Some are beginning to pilot AI-powered agents to automate routine tasks and provide real-time recommendations for employees.<\/p>\n\n\n\n Lenders increasingly use AI tools to assess the risk of issuing loans. Because AI can collect and analyze large data sets, the technology provides a comprehensive way to assess creditworthiness.<\/p>\n\n\n\n Financial institutions also use AI for fraud detection. AI tools can spot patterns in typical customer behavior and identify anomalies that could indicate fraud.<\/p>\n\n\n\n Insurance companies frequently use AI for underwriting, including risk assessment and risk mitigation. AI is also a useful tool for processing claims and searching for fraud.<\/p>\n\n\n\n Generative AI is also often used to provide frontline services to customers. For example, chatbots answer straightforward questions, provide triage and refer more complex questions to human operators.<\/p>\n\n\n\n AI is a valuable tool in mitigating risk, but it\u2019s important to <\/a>be aware of the risks<\/a> the tools themselves present.<\/p>\n\n\n\n Chief among those risks is the problem of algorithmic bias. AI and ML excel at identifying patterns and codifying them. However, this means that AI is only as good as the data that feeds it. If AI\/ML tools are trained on biased data, the tools will codify the biases embedded in that data. AI\/ML takes the unspoken prejudices in datasets and turns them into hard and fast rules, which inform every decision going forward.<\/p>\n\n\n\n Agencies must also consider data privacy implications when AI tools process sensitive or regulated data. If human operators do not question the algorithm’s output, there’s a real risk that bias will become deeply ingrained, causing lasting harm to individuals and organizations and even creating regulatory compliance issues.<\/p>\n\n\n\n Federal agencies and contractors must understand exactly how AI tools are being deployed. Operators should frequently look \u201cunder the hood\u201d of the AI algorithms, asking questions about how the outputs are generated. Opening the \u201cblack box\u201d allows organizations to check for bias and prevent it from being codified. Strong data ethics practices ensure that AI systems are trained on fair, transparent and accountable data sources.<\/p>\n\n\n\n It\u2019s best practice to implement a cross-functional AI governance council or team to oversee artificial intelligence. It\u2019s also important to work closely with a trusted partner who has experience <\/a>integrating AI into a GRC platform<\/a>. The best AI tools help humans manage a Federal agency with efficiency. The question is, how to make the most of the available technology while mitigating the associated risk.<\/p>\n<\/head>","protected":false},"excerpt":{"rendered":" Artificial intelligence (AI) is a valuable risk management tool, but it also poses a degree of risk. As AI becomes more prevalent, it opens new possibilities while simultaneously raising new concerns. Federal agencies and contractors have a responsibility to closely … Continue reading <\/a>Federal Government Use Cases<\/strong><\/h3>\n\n\n\n
\n
<\/a>Financial Sector<\/strong><\/h3>\n\n\n\n
<\/a>Insurance Industry<\/strong><\/h3>\n\n\n\n
Risks Associated with AI Technologies<\/h2>\n\n\n\n
Addressing AI Bias<\/h2>\n\n\n\n