Jan 29 2025

Basic Principle to Enterprise AI Security

Category: AIdisc7 @ 12:24 pm

Securing AI in the Enterprise: A Step-by-Step Guide

  1. Establish AI Security Ownership
    Organizations must define clear ownership and accountability for AI security. Leadership should decide whether AI governance falls under a cross-functional committee, IT/security teams, or individual business units. Establishing policies, defining decision-making authority, and ensuring alignment across departments are key steps in successfully managing AI security from the start.
  2. Identify and Mitigate AI Risks
    AI introduces unique risks, including regulatory compliance challenges, data privacy vulnerabilities, and algorithmic biases. Organizations must evaluate legal obligations (such as GDPR, HIPAA, and the EU AI Act), implement strong data protection measures, and address AI transparency concerns. Risk mitigation strategies should include continuous monitoring, security testing, clear governance policies, and incident response plans.
  3. Adopt AI Security Best Practices
    Businesses should follow security best practices, such as starting with small AI implementations, maintaining human oversight, establishing technical guardrails, and deploying continuous monitoring. Strong cybersecurity measures—such as encryption, access controls, and regular security audits—are essential. Additionally, comprehensive employee training programs help ensure responsible AI usage.
  4. Assess AI Needs and Set Measurable Goals
    AI implementation should align with business objectives, with clear milestones set for six months, one year, and beyond. Organizations should define success using key performance indicators (KPIs) such as revenue impact, efficiency improvements, and compliance adherence. Both quantitative and qualitative metrics should guide AI investments and decision-making.
  5. Evaluate AI Tools and Security Measures
    When selecting AI tools, organizations must assess security, accuracy, scalability, usability, and compliance. AI solutions should have strong data protection mechanisms, clear ROI, and effective customization options. Evaluating AI tools using a structured approach ensures they meet security and business requirements.
  6. Purchase and Implement AI Securely
    Before deploying AI solutions, businesses must ask key questions about effectiveness, performance, security, scalability, and compliance. Reviewing trial options, pricing models, and regulatory alignment (such as GDPR or CCPA compliance) is critical to selecting the right AI tool. AI security policies should be integrated into the organization’s broader cybersecurity framework.
  7. Launch an AI Pilot Program with Security in Mind
    Organizations should begin with a controlled AI pilot to assess risks, validate performance, and ensure compliance before full deployment. This includes securing high-quality training data, implementing robust authentication controls, continuously monitoring performance, and gathering user feedback. Clear documentation and risk management strategies will help refine AI adoption in a secure and scalable manner.

By following these steps, enterprises can securely integrate AI, protect sensitive data, and ensure regulatory compliance while maximizing AI’s potential.

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Tags: AI Governance, AI privacy, AI Risk Management, AI security


Oct 04 2024

4 ways AI is transforming audit, risk and compliance

Category: AI,Risk Assessment,Security Compliancedisc7 @ 9:11 am

AI is revolutionizing audit, risk, and compliance by streamlining processes through automation. Tasks like data collection, control testing, and risk assessments, which were once time-consuming, are now being done faster and with more precision. This allows teams to focus on more critical strategic decisions.

In auditing, AI identifies anomalies and uncovers patterns in real-time, enhancing both the depth and accuracy of audits. AI’s ability to process large datasets also helps maintain compliance with evolving regulations like the EU’s AI Act, while mitigating human error.

Beyond audits, AI supports risk management by providing dynamic insights that adapt to changing threat landscapes. This enables continuous risk monitoring rather than periodic reviews, making organizations more responsive to emerging risks, including cybersecurity threats.

AI also plays a crucial role in bridging the gap between cybersecurity, compliance, and ESG (Environmental, Social, Governance) goals. It integrates these areas into a single strategy, allowing businesses to track and manage risks while aligning with sustainability initiatives and regulatory requirements.

For more details, visit here

Credit: Adobe Stock Images

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Oct 01 2024

Could APIs be the undoing of AI?

Category: AI,API securitydisc7 @ 11:32 am

The article discusses security challenges associated with large language models (LLMs) and APIs, focusing on issues like prompt injection, data leakage, and model theft. It highlights vulnerabilities identified by OWASP, including insecure output handling and denial-of-service attacks. API flaws can expose sensitive data or allow unauthorized access. To mitigate these risks, it recommends implementing robust access controls, API rate limits, and runtime monitoring, while noting the need for better protections against AI-based attacks.

The post discusses defense strategies against attacks targeting large language models (LLMs). Providers are red-teaming systems to identify vulnerabilities, but this alone isn’t enough. It emphasizes the importance of monitoring API activity to prevent data exposure and defend against business logic abuse. Model theft (LLMjacking) is highlighted as a growing concern, where attackers exploit cloud-hosted LLMs for profit. Organizations must act swiftly to secure LLMs and avoid relying solely on third-party tools for protection.

For more details, visit Help Net Security.

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Sep 03 2024

AI Risk Management

Category: AI,Risk Assessmentdisc7 @ 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.

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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)


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Apr 19 2024

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

Category: AIdisc7 @ 8:03 am

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.

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