Jun 25 2025

AI Governance Is a Boardroom Imperative—The SEC Just Raised the Stakes on AI Hype

Category: AI,IT Governance — disc7 @ 7:18 am

The SEC has charged a major tech company for deceiving investors by exaggerating its use of AI—highlighting that the falsehood was about AI itself, not just product features. This signals a shift: AI governance has now become a boardroom-level issue, and many organizations are unprepared.

Advice for CISOs and execs:

  1. Be audit-ready—any AI claims must be verifiable.
  2. Involve GRC early—AI governance is about managing risk, enforcing controls, and ensuring transparency.
  3. Educate your board—they don’t need to understand algorithms, but they must grasp the associated risks and mitigation plans.

If your current AI strategy is nothing more than a slide deck and hope, it’s time to build something real.

AI Washing

The Securities and Exchange Commission (SEC) has been actively pursuing actions against companies for misleading statements about their use of Artificial Intelligence (AI), a practice often referred to as “AI washing”. 

Here are some examples of recent SEC actions in this area:

  • Presto Automation: The SEC charged Presto Automation for making misleading statements about its AI-powered voice technology used for drive-thru order taking. Presto allegedly failed to disclose that it was using a third party’s AI technology, not its own, and also misrepresented the extent of human involvement required for the product to function.
  • Delphia and Global Predictions: These two investment advisers were charged with making false and misleading statements about their use of AI in their investment processes. The SEC found that they either didn’t have the AI capabilities they claimed or didn’t use them to the extent they advertised.
  • Nate, Inc.: The founder of Nate, Inc. was charged by both the SEC and the DOJ for allegedly misleading investors about the company’s AI-powered app, claiming it automated online purchases when they were primarily processed manually by human contractors. 

Key takeaways from these cases and SEC guidance:

  • Transparency and Accuracy: Companies need to ensure their AI-related disclosures are accurate and avoid making vague or exaggerated claims.
  • Distinguish Capabilities: It’s important to clearly distinguish between current AI capabilities and future aspirations.
  • Substantiation: Companies should have a reasonable basis and supporting evidence for their AI-related claims.
  • Disclosure Controls: Companies should establish and maintain disclosure controls to ensure the accuracy of their AI-related statements in SEC filings and other communications. 

The SEC has made it clear that “AI washing” is a top enforcement priority, and companies should be prepared for heightened scrutiny of their AI-related disclosures. 

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Jun 02 2025

Summary of CISO 3.0: Leading AI Governance and Security in the Boardroom

Category: AI,CISO,Information Security,vCISO — disc7 @ 5:12 pm

  1. Aaron McCray, Field CISO at CDW, discusses the evolving role of the Chief Information Security Officer (CISO) in the age of artificial intelligence (AI). He emphasizes that CISOs are transitioning from traditional cybersecurity roles to strategic advisors who guide enterprise-wide AI governance and risk management. This shift, termed “CISO 3.0,” involves aligning AI initiatives with business objectives and compliance requirements.
  2. McCray highlights the challenges of integrating AI-driven security tools, particularly regarding visibility, explainability, and false positives. He notes that while AI can enhance security operations, it also introduces complexities, such as the need for transparency in AI decision-making processes and the risk of overwhelming security teams with irrelevant alerts. Ensuring that AI tools integrate seamlessly with existing infrastructure is also a significant concern.
  3. The article underscores the necessity for CISOs and their teams to develop new skill sets, including proficiency in data science and machine learning. McCray points out that understanding how AI models are trained and the data they rely on is crucial for managing associated risks. Adaptive learning platforms that simulate real-world scenarios are mentioned as effective tools for closing the skills gap.
  4. When evaluating third-party AI tools, McCray advises CISOs to prioritize accountability and transparency. He warns against tools that lack clear documentation or fail to provide insights into their decision-making processes. Red flags include opaque algorithms and vendors unwilling to disclose their AI models’ inner workings.
  5. In conclusion, McCray emphasizes that as AI becomes increasingly embedded across business functions, CISOs must lead the charge in establishing robust governance frameworks. This involves not only implementing effective security measures but also fostering a culture of continuous learning and adaptability within their organizations.

Feedback

  1. The article effectively captures the transformative impact of AI on the CISO role, highlighting the shift from technical oversight to strategic leadership. This perspective aligns with the broader industry trend of integrating cybersecurity considerations into overall business strategy.
  2. By addressing the practical challenges of AI integration, such as explainability and infrastructure compatibility, the article provides valuable insights for organizations navigating the complexities of modern cybersecurity landscapes. These considerations are critical for maintaining trust in AI systems and ensuring their effective deployment.
  3. The emphasis on developing new skill sets underscores the dynamic nature of cybersecurity roles in the AI era. Encouraging continuous learning and adaptability is essential for organizations to stay ahead of evolving threats and technological advancements.
  4. The cautionary advice regarding third-party AI tools serves as a timely reminder of the importance of due diligence in vendor selection. Transparency and accountability are paramount in building secure and trustworthy AI systems.
  5. The article could further benefit from exploring specific case studies or examples of organizations successfully implementing AI governance frameworks. Such insights would provide practical guidance and illustrate the real-world application of the concepts discussed.
  6. Overall, the article offers a comprehensive overview of the evolving responsibilities of CISOs in the context of AI integration. It serves as a valuable resource for cybersecurity professionals seeking to navigate the challenges and opportunities presented by AI technologies.

For further details, access the article here

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Jun 01 2025

AI in the Workplace: Replacing Tasks, Not People

Category: AI — disc7 @ 3:48 pm

  1. Establishing an AI Strategy and Guardrails:
    To effectively integrate AI into an organization, leadership must clearly articulate the company’s AI strategy to all employees. This includes defining acceptable and unacceptable uses of AI, legal boundaries, and potential risks. Setting clear guardrails fosters a culture of responsibility and mitigates misuse or misunderstandings.
  2. Transparency and Job Impact Communication:
    Transparency is essential, especially since many employees may worry that AI initiatives threaten their roles. Leaders should communicate that those who adapt to AI will outperform those who resist it. It’s also important to outline how AI will alter jobs by automating routine tasks, thereby allowing employees to focus on higher-value work.
  3. Redefining Roles Through AI Integration:
    For instance, HR professionals may shift from administrative tasks—like managing transfers or answering policy questions—to more strategic work such as improving onboarding processes. This demonstrates how AI can enhance job roles rather than eliminate them.
  4. Addressing Employee Sentiments and Fears:
    Leaders must pay attention to how employees feel and what they discuss informally. Creating spaces for feedback and development helps surface concerns early. Ignoring this can erode culture, while addressing it fosters trust and connection. Open conversations and vulnerability from leadership are key to dispelling fear.
  5. Using AI to Facilitate Dialogue and Action:
    AI tools can aid in gathering and classifying employee feedback, sparking relevant discussions, and supporting ongoing engagement. Digital check-ins powered by AI-generated prompts offer structured ways to begin conversations and address concerns constructively.
  6. Equitable Participation and Support Mechanisms:
    Organizations must ensure all employees are given equal opportunity to engage with AI tools and upskilling programs. While individuals will respond differently, support systems like centralized feedback platforms and manager check-ins can help everyone feel included and heard.

Feedback and Organizational Tone Setting:
This approach sets a progressive and empathetic tone for AI adoption. It balances innovation with inclusion by emphasizing transparency, emotional intelligence, and support. Leaders must model curiosity and vulnerability, signaling that learning is a shared journey. Most importantly, the strategy recognizes that successful AI integration is as much about culture and communication as it is about technology. When done well, it transforms AI from a job threat into a tool for empowerment and growth.

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intelligence”

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May 20 2025

Why Legal Teams Should Lead AI Governance: Ivanti’s Cross-Functional Approach

Category: AI — disc7 @ 8:25 am

In a recent interview with Help Net Security, Brooke Johnson, Chief Legal Counsel and SVP of HR and Security at Ivanti, emphasized the critical role of legal departments in leading AI governance within organizations. She highlighted that unmanaged use of generative AI (GenAI) tools can introduce significant risks, including data privacy violations, algorithmic bias, and ethical concerns, particularly in sensitive areas like recruitment where flawed training data can lead to discriminatory outcomes.

Johnson advocates for a cross-functional approach to AI governance, involving collaboration among legal, HR, IT, and security teams. This strategy aims to create clear, enforceable policies that enable responsible innovation without stifling progress. At Ivanti, such collaboration has led to the establishment of an AI Governance Council (AIGC), which oversees the safe and ethical use of AI tools by reviewing applications and providing guidance on acceptable use cases.

Recognizing that a significant number of employees use GenAI tools without informing management, Johnson suggests that organizations should proactively assume AI is already in use. Legal teams should lead in defining safe usage parameters and provide practical training to employees, explaining the security implications and reasons behind certain restrictions.

To ensure AI policies are effectively operationalized, Johnson recommends conducting assessments to identify current AI tool usage, developing clear and pragmatic policies, and offering vetted, secure platforms to reduce reliance on unsanctioned alternatives. She stresses that AI governance should be treated as a dynamic process, with policies evolving alongside technological advancements and emerging threats, maintained through ongoing cross-functional collaboration across departments and geographies.

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Jan 29 2025

Basic Principle to Enterprise AI Security

Category: AI — disc7 @ 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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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.

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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.

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