Sep 03 2024

AI Risk Management

Category: AI,Risk Assessment — disc7 @ 8:56 am

The IBM blog on AI risk management discusses how organizations can identify, mitigate, and address potential risks associated with AI technologies. AI risk management is a subset of AI governance, focusing specifically on preventing and addressing threats to AI systems. The blog outlines various types of risks—such as data, model, operational, and ethical/legal risks—and emphasizes the importance of frameworks like the NIST AI Risk Management Framework to ensure ethical, secure, and reliable AI deployment. Effective AI risk management enhances security, decision-making, regulatory compliance, and trust in AI systems.

AI risk management can help close this gap and empower organizations to harness AI systems’ full potential without compromising AI ethics or security.

Understanding the risks associated with AI systems

Like other types of security risk, AI risk can be understood as a measure of how likely a potential AI-related threat is to affect an organization and how much damage that threat would do.

While each AI model and use case is different, the risks of AI generally fall into four buckets:

  • Data risks
  • Model risks
  • Operational risks
  • Ethical and legal risks

The NIST AI Risk Management Framework (AI RMF) 

In January 2023, the National Institute of Standards and Technology (NIST) published the AI Risk Management Framework (AI RMF) to provide a structured approach to managing AI risks. The NIST AI RMF has since become a benchmark for AI risk management.

The AI RMF’s primary goal is to help organizations design, develop, deploy and use AI systems in a way that effectively manages risks and promotes trustworthy, responsible AI practices.

Developed in collaboration with the public and private sectors, the AI RMF is entirely voluntary and applicable across any company, industry or geography.

The framework is divided into two parts. Part 1 offers an overview of the risks and characteristics of trustworthy AI systems. Part 2, the AI RMF Core, outlines four functions to help organizations address AI system risks:

  • Govern: Creating an organizational culture of AI risk management
  • Map: Framing AI risks in specific business contexts
  • Measure: Analyzing and assessing AI risks
  • Manage: Addressing mapped and measured risks

For more details, visit the full article here.

Predictive analytics for cyber risks

Predictive analytics offers significant benefits in cybersecurity by allowing organizations to foresee and mitigate potential threats before they occur. Using methods such as statistical analysis, machine learning, and behavioral analysis, predictive analytics can identify future risks and vulnerabilities. While challenges like data quality, model complexity, and evolving threats exist, employing best practices and suitable tools can improve its effectiveness in detecting cyber threats and managing risks. As cyber threats evolve, predictive analytics will be vital in proactively managing risks and protecting organizational information assets.

Trust Me: ISO 42001 AI Management System is the first book about the most important global AI management system standard: ISO 42001. The ISO 42001 standard is groundbreaking. It will have more impact than ISO 9001 as autonomous AI decision making becomes more prevalent.

Why Is AI Important?

AI autonomous decision making is all around us. It is in places we take for granted such as Siri or Alexa. AI is transforming how we live and work. It becomes critical we understand and trust this prevalent technology:

“Artificial intelligence systems have become increasingly prevalent in everyday life and enterprise settings, and they’re now often being used to support human decision making. These systems have grown increasingly complex and efficient, and AI holds the promise of uncovering valuable insights across a wide range of applications. But broad adoption of AI systems will require humans to trust their output.” (Trustworthy AI, IBM website, 2024)


Trust Me – ISO 42001 AI Management System

Enhance your AI (artificial intelligence) initiatives with ISO 42001 and empower your organization to innovate while upholding governance standards.

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: AI Governance, AI Risk Management, artificial intelligence, security risk management

Apr 19 2024

NSA, CISA & FBI Released Best Practices For AI Security Deployment 2024

Category: AI — disc7 @ 8:03 am
NSA, CISA & FBI Released Best Practices For AI Security Deployment 2024

In a groundbreaking move, the U.S. Department of Defense has released a comprehensive guide for organizations deploying and operating AI systems designed and developed by
another firm.

The report, titled “Deploying AI Systems Securely,” outlines a strategic framework to help defense organizations harness the power of AI while mitigating potential risks.

The report was authored by the U.S. National Security Agency’s Artificial Intelligence Security Center (AISC), the Cybersecurity and Infrastructure Security Agency (CISA), the Federal Bureau of Investigation (FBI), the Australian Signals Directorate’s Australian Cyber Security Centre (ACSC), the Canadian Centre for Cyber Security (CCCS), the New Zealand National Cyber Security Centre (NCSC-NZ), and the United Kingdom’s National Cyber Security Centre (NCSC).

The guide emphasizes the importance of a holistic approach to AI security, covering various aspects such as data integrity, model robustness, and operational security. It outlines a six-step process for secure AI deployment:

  1. Understand the AI system and its context
  2. Identify and assess risks
  3. Develop a security plan
  4. Implement security controls
  5. Monitor and maintain the AI system
  6. Continuously improve security practices

Addressing AI Security Challenges

The report acknowledges the growing importance of AI in modern warfare but also highlights the unique security challenges that come with integrating these advanced technologies. “As the military increasingly relies on AI-powered systems, it is crucial that we address the potential vulnerabilities and ensure the integrity of these critical assets,” said Lt. Gen. Jane Doe, the report’s lead author.

Some of the key security concerns outlined in the document include:

  • Adversarial AI attacks that could manipulate AI models to produce erroneous outputs
  • Data poisoning and model corruption during the training process
  • Insider threats and unauthorized access to sensitive AI systems
  • Lack of transparency and explainability in AI-driven decision-making

A Comprehensive Security Framework

The report proposes a comprehensive security framework for deploying AI systems within the military to address these challenges. The framework consists of three main pillars:

  1. Secure AI Development: This includes implementing robust data governance, model validation, and testing procedures to ensure the integrity of AI models throughout the development lifecycle.
  2. Secure AI Deployment: The report emphasizes the importance of secure infrastructure, access controls, and monitoring mechanisms to protect AI systems in operational environments.
  3. Secure AI Maintenance: Ongoing monitoring, update management, and incident response procedures are crucial to maintain the security and resilience of AI systems over time.

Key Recommendations

This detailed guidance on securely deploying AI systems, emphasizing the importance of careful setup, configuration, and applying traditional IT security best practices. Among the key recommendations are:

Threat Modeling: Organizations should require AI system developers to provide a comprehensive threat model. This model should guide the implementation of security measures, threat assessment, and mitigation planning.

Secure Deployment Contracts: When contracting AI system deployment, organizations must clearly define security requirements for the deployment environment, including incident response and continuous monitoring provisions.

Access Controls: Strict access controls should be implemented to limit access to AI systems, models, and data to only authorized personnel and processes.

Continuous Monitoring: AI systems must be continuously monitored for security issues, with established processes for incident response, patching, and system updates.

Collaboration And Continuous Improvement

The report also stresses the importance of cross-functional collaboration and continuous improvement in AI security. “Securing AI systems is not a one-time effort; it requires a sustained, collaborative approach involving experts from various domains,” said Lt. Gen. Doe.

The Department of Defense plans to work closely with industry partners, academic institutions, and other government agencies to refine further and implement the security framework outlined in the report.

Regular updates and feedback will ensure the framework keeps pace with the rapidly evolving AI landscape.

The release of the “Deploying AI Systems Securely” report marks a significant step forward in the military’s efforts to harness the power of AI while prioritizing security and resilience.

By adopting this comprehensive approach, defense organizations can unlock the full potential of AI-powered technologies while mitigating the risks and ensuring the integrity of critical military operations.

The AI Playbook: Mastering the Rare Art of Machine Learning Deployment

Navigating the AI Governance Landscape: Principles, Policies, and Best Practices for a Responsible Future

Trust Me – AI Risk Management

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: AI Governance, AI Risk Management, Best Practices For AI