03 June 2024

Don't get stung!🐝 A Peek Into Emerging Risks In AI Technologies and Organizational Strategies For Mitigation.

A snapshot into various risks posed by modern AI technologies

Daniel Wallace
Daniel Wallace Senior Security Architect LinkedIn

In recent years, artificial intelligence (AI) has been at the forefront of technological innovation, providing solutions that enhance operational efficiency across multiple sectors. However, as these technologies evolve and become more integral to business operations and societal functions, they introduce a complex array of risks. This article quickly gives a snapshot into various risks posed by modern AI technologies, examines instances of these risks, and provides detailed guidance on how organizations can effectively identify, assess, and mitigate these challenges.

Part I: Examination of AI-Related Risks

  1. Data Privacy and Security
  2. Overview: AI systems require extensive datasets to operate, which often contain sensitive information. The improper handling of such data can lead to significant privacy breaches and security risks.

    Notable Incidents: Various incidents have highlighted these risks, such as exposing millions of people's data through unprotected AI databases. Data collected by the U.S. government is no stranger to being targeted by adversaries. Federal systems have been besieged by external attackers for decades, but in the post-AI world, mismanagement of AI data sets poses a serious risk. On March 23, 2024, Vertitone Inc., a prominent provider of AI technology for the government, left approximately 550GB of internal and client data vulnerable on two unprotected Elasticsearch servers. Elasticsearch is loaded with some of the latest advancements in machine learning and natural language processing. It’s often used to build AI search applications that integrate with generative AI and power semantic and image search, personalization, and question-answering to improve search experiences. The exposed information included employee data and credentials, internal system logs, AI training data, and sensitive client data belonging to various U.S. government organizations, including the Department of Homeland Security and Veterans Affairs.

    An effort from the White House was launched in October 2023 to help curtail this type of fallout, but implementation efforts take time and resources to put into place. And while this executive order will indeed help in the near future, things are still inchoate and very dynamic at present. So, what are a few immediate actions that you can take to help mitigate data privacy and security risks?

    Mitigation Strategies:

    • Implement state-of-the-art encryption and secure data storage solutions.
    • Securing an AI database, especially one that handles sensitive or proprietary data, requires robust encryption strategies. One example of a state-of-the-art encryption technique used in this context is homomorphic encryption (HE). This advanced cryptographic method allows for computations to be performed on encrypted data, returning an encrypted result that when decrypted, matches the result of operations performed on the plaintext.

      Some benefits of using homomorphic encryption:
      Privacy Preservation. HE ensures that sensitive data is never exposed in plaintext during storage or processing, preserving the privacy of individuals.
      Security Compliance. This approach helps organizations comply with stringent data protection regulations such as GDPR or HIPAA by ensuring that personal data is encrypted at all stages.
      Flexibility in Data Usage. Even though the data is encrypted, it can still be utilized for complex computations, making it highly suitable for AI applications where data utility and security are both critical.

    • Adhere to privacy-by-design principles throughout the development lifecycle of AI systems.

    • Privacy-by-design principles advocate for privacy and data protection to be considered throughout the entire development and operational lifecycle of AI systems. Implementing these principles effectively means incorporating privacy into the initial design and architecture, rather than treating it as an afterthought.

      Here’s an example of how Privacy-by-Design can be integrated into the development lifecycle of an AI system:

      1. Conceptualization and Planning Phase. Establish that the system will collect only data essential for its function and ensure that data is handled with the highest privacy standards. Next, engage stakeholders: consult with privacy experts, legal teams, and potential users to understand privacy concerns and expectations.
      3. Design Phase. Create algorithms that use aggregated data instead of individual data and design a secure data architecture with encryption and multi-party computation techniques, where no participant or observer learns more than their own input and the computed result.
      4. Development Phase. Implement privacy enhancing technologies (PETs) to ensure that the AI model’s outputs cannot be used to infer details about individual users. Develop user interfaces that clearly communicate what data is being collected and for what purpose, ensuring that consent is informed and can be easily withdrawn.
      5. Testing and Validation Phase. Regularly conduct privacy impact assessments (PIAs) to evaluate how well the AI system protects user privacy and adheres to regulatory requirements. Moreover, identify and mitigate vulnerabilities that expose user data.
      6. Deployment Phase. Protect your data by default. Ensure that the strictest privacy settings are automatically applied without requiring user inter vention. Provide clear and accessible information to users about how their data is used and protected in the system.
      7. Maintenance and Update Phase. Monitor compliance with privacy policies and the effectiveness of privacy protections. Ensure that your privacy practices are updated in response to new laws, regulations, and technological advancements.
      8. Decommissioning Phase.When the system is decommissioned, ensure that all personal data is security deleted or anonymized.

      There are immediate benefits to incorporating these principles from the outset. Users are more likely to trust and engage with AI systems that they know respect and protect their privacy.

    • Conduct regular security audits and vulnerability assessments.

    Conducting a security audit and vulnerability assessment on AI systems is a critical process to ensure that the system is secure from potential threats and vulnerabilities. Both at the software and data levels. Here is an 8-step structured example of how this process might be carried out:
    1. Planning. Define the audit scope, assemble a team of specialists, and identify key assets.
    2. Review. Assess data management policies and system architecture, and review previous audits.
    3. Threat Modeling. Identify and assess risks using frameworks like STRIDE.
    4. Vulnerability Assessment. Conduct status and dynamic analysis, check dependencies and evaluate data security.
    5. Penetration Testing. Perform controlled attacks to test defenses and document the system’s responses.
    6. Reporting. Provide a detailed report of vulnerabilities and recommendations for mitigating them.
    7. Remediation. Develop and implement a remediation plan and schedule follow-up audits.
    8. Continuous Monitoring. Implement ongoing security monitoring and regularly update security practices.

  3. Bias and Discrimination
  4. Overview: Data bias refers to systematic errors or inaccuracies in data that result in misrepresentations, typically in favor of or against a particular group or concept. Data bias can significantly affect the performance and fairness of algorithms, especially in AI applications where decision-making is heavily reliant on the underlying data. AI systems can perpetuate and amplify existing prejudices, leading to unfair outcomes and discrimination. Notwithstanding, bias and discrimination ultimately lead to injury to an organization’s economic base. Here, I’ll show you how:

    Notable Incident: An example includes a secret AI recruitment tool that developed a bias against female applicants, reflecting historical hiring biases present in the training data. This was due to models being trained on a 10-year pool of applicants, mostly men — a reflection of male dominance across the tech industry. But this leaves a question to be asked: if males are dominating tech production, are males only dominating tech consumption? The answer is a big fat no! This translates to financial risks and missed financial opportunities.

    Diversity in the workforce is linked to increased creativity, better problem-solving, and greater overall innovation. A lack of diversity due to biased hiring practices can stifle new ideas and reduce an organization’s competitiveness in the market. Moreover, consider employee turnover. Organizations that fail to address bias in their AI systems may see higher turnover rates, especially among employees from groups that feel underrepresented or unfairly treated. High turnover can lead to increased hiring and training costs, impacting the organization’s bottom line. Lastly, consider market share and consumer trust. Organizations known for biased practices may lose consumer trust and market share, particularly among demographics that value corporate responsibility and equity. This can translate into direct economic losses and reduced growth potential.

    Mitigation Strategies:
    • Ensure the diversity and representativeness of training datasets. Thoroughly review and analyze features and correlations in datasets. Be well-informed about your data.
    • Establish ongoing monitoring and auditing for AI-driven decisions to detect and correct bias.
    • Develop ethical guidelines and compliance checks specifically tailored to mitigate bias in AI applications.
  5. Transparency and Explainability
  6. Overview: The complex nature of some AI algorithms, particularly those based on deep learning, can make it difficult to understand or predict their behavior, leading to a lack of accountability. Many AI technologies often operate as “black boxes” with decision-making processes that are not transparent. This lack of explainability can lead to trust issues and accountability challenges.

    Mitigation Strategies:
    • Invest in research and development of explainable AI (XAI) technologies.
    • Create comprehensive documentation for AI models, detailing their decision processes and limitations.
    • Engage interdisciplinary teams in the development process to ensure decisions made by AI are understandable and explainable across different expertise levels.
  7. Automation Bias and Overreliance

Overview: Excessive dependence on AI can lead to automation bias, where human operators overly trust AI decision-making, potentially overlooking errors or misjudgments.

Notable Incidents: This risk was starkly highlighted in several accidents involving semi-autonomous vehicles, where overreliance on automation contributed to critical oversights.

Mitigation Strategies:

  • Design systems that require periodic human oversight and verification.
  • Train users on the potential flaws and limits of AI systems, promoting a balanced perspective on their capabilities.
  • Regularly update training protocols to align with advancements and discovered limitations in AI technologies.

Part II: Proactive Detection and Mitigation

Proactive detection and mitigation are essential for AI systems primarily to prevent security breaches. Since AI systems often process sensitive or personal data, they are prime targets for cyberattacks. Implementing proactive security measures enables the early detection and resolution of vulnerabilities, preventing potential data breaches and maintaining the integrity of the system. This is crucial as it helps maintain trust and system reliability.

Additionally, proactive measures are cost-effective, reducing the need for extensive resources post-incident and helping avoid substantial fines for non-compliance with data protection laws. Continuous monitoring and updating of AI systems not only enhance their performance and stability but also adapt them to evolving cyber threats. By staying ahead of potential security issues, AI systems can operate efficiently, remain compliant, and continue to deliver accurate and reliable outputs in a dynamic threat landscape. Here are a few strategies to help stay ahead of security issues and continue to deliver toward the desired goals of an organization.

Strategies for Organizations

  • Development of AI Governance Frameworks: Crafting comprehensive governance frameworks that address ethical, legal, and operational risks. On April 29, 2024, NIST released a draft publication based on the AI Risk Management Framework (AI RMF) to help manage the risk of Generative AI. The draft AI RMF Generative AI Profile can help organizations identify unique risks posed by generative AI and propose actions for generative AI risk management that best aligns with their goals and priorities.
  • Dynamic Monitoring Systems: Implementing systems that continually assess the performance and ethical implications of AI applications. A few examples/methods would include (but are not limited to):
    • Continuous Monitoring and Evaluation.
      • Performance metrics: Regularly measure and evaluate the performance of AI systems using relevant metrics, such as accuracy, speed, reliability, and scalability. Monitoring should also include stress testing under various conditions to ensure robustness.
      • Ethical Audits: Conduct periodic audits to assess the ethical implications of AI systems. This includes reviewing the algorithms for bias, fairness, transparency, and accountability.
    • Feedback Mechanisms:
      • User Feedback: Implement systems to gather feedback from users to understand their experiences and any issues they face with the AI applications.
      • Stakeholder Engagement: Regular consultations with stakeholders, including ethicists, users, community representatives, and industry experts, to gain diverse perspectives on the ethical aspects of AI.
    • Compliance and Regulatory Frameworks:
      • Ethical Guidelines and Standards: Adhere to established AI ethics guidelines and standards, such as those from professional organizations and international bodies.
      • Legal Compliance: Ensure that AI applications comply with all relevant laws and regulations, including data protection laws and anti-discrimination laws.
    • Transparency and Reporting:
      • Documentation: Maintain thorough documentation of AI system development processes, including data sources, algorithmic decisions, and changes over time.
      • Reporting Mechanisms: Develop and implement reporting mechanisms to communicate both performance metrics and ethical assessments to relevant stakeholders.
    • Education and Training:
      • Training Programs: Regular training for AI developers and users on ethical AI use and the implications of AI technology.
      • Ethics in Design: Incorporate ethical considerations into the design and development phase of AI systems, including training on ethical decision-making.
    • Technology Solutions:
      • Bias Detection Tools: Use tools and methodologies specifically designed to detect and mitigate bias in AI algorithms.
      • Explainability Tools: Employ technologies that increase the transparency of AI decisions, making them understandable to experts and laypeople alike.
    • Dynamic Adjustments:
      • Iterative Improvements: Regularly update AI systems to incorporate new ethical standards and performance enhancements.
      • Responsive Design: Design AI systems that can be quickly adjusted or reconfigured in response to ethical concerns or performance issues.
  • Broad Stakeholder Engagement: I’ve mentioned it before, and I’ll mention it again. Be sure that you’re Including a wide range of stakeholders in discussions about AI development to ensure diverse perspectives and ethical considerations are integrated.
  • Adherence to Evolving Legal Standards: Keeping abreast of and complying with international standards and regulations related to AI.

  • Conclusion

    The proliferation of AI technologies brings with it transformative potential, but also introduces a spectrum of risks that must be managed with informed and strategic actions. By understanding these risks, learning from past incidents, and implementing a robust framework for AI risk management, organizations can leverage the benefits of AI while ensuring ethical, secure, and effective operations.

If you have any questions, or would like to discuss this topic in more detail, feel free to contact us and we would be happy to schedule some time to chat about how Aquia can help you and your organization.


Artificial Intelligence Risk Management Compliance Security