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AI Systems Integrity And Data Security

AI systems integrity refers to artificial intelligence systems’ trustworthiness, reliability, and ethical behaviour. Ensuring the integrity of AI systems is crucial to maintaining their functionality, fairness, and safety in various applications. Giving it high consideration is essential to build users’ trust.

Understanding AI Systems Integrity

Exploring research by NIST, it’s obvious the issues regarding AI trustworthiness existed decades ago and still hold a significant concern in this present day despite multiple approaches geared towards ensuring the safety and security of AI technology while eliminating bias.

Bias and discrimination are the key factors affecting the trustworthiness of AI systems, especially when factors such as ethnicity, religion, etc. make it difficult to define standards of fairness. Combating these issues implies AI companies/organizations have to prioritize fairness in AI models, which means equality and equity, which are the core influencer of indiscrimination, has to be at the core of every AI model development.

How to mitigate systems integrity bias

Identifying the source of bias is a top priority to mitigate bias in AI systems. NIST categorized Human, Systemic, and Computational bias as top considerations to move forward faster and get a more lasting solution.

  • Human bias represents decision-making by people influenced by the use of information generated by AI systems.
  • Systemic bias covers the organizational practices, norms and all processes engaged during the AI lifecycle, its dataset and its use in solving situations requiring prompt decision-making. 
  • Computational bias: represents factors such as dataset and fairness of machine learning algorithms. With a better understanding of these factors, let’s dive into boosting AI’s integrity further by considering some factors.
  • Security and Resilience: The need for users to trust an AI system to withstand all adversarial attacks without posing any threat to them grows stronger daily. Security and resilience are two core factors in enhancing AI integrity. It must have high security at all times.
  • Interpretable: This is another emphasis on transparency regarding AI functionality. Users can only trust what they understand. It’s imperative to make the AI system’s output, decisions and predictions understandable.
  • Enhanced Privacy: An AI system with a porous privacy mechanism is operating with almost zero integrity, as users can’t boast of their data privacy. Anonymity, confidentiality and identity control should be a guideline influencing the choice of AI system’s development, design and deployment. NIST identified the possibility of privacy-related risks overlapping with transparency and security bias.

AI Systems Integrity And Data Security 

According to research by Microsoft, over 97% of existing organizations are embracing, implementing, and developing AI strategies. However, security challenges such as data security, privacy challenges and compliance coupled with lack of transparency and almost zero controls to protect data in AI are forcing some organizations to take drastic steps like banning AI usage, pausing, and slowing down its adoption.

How To Protect AI Data And Systems Integrity

  • Data Encryption: A fantastic thing about encryption is its ability to render stolen data useless, protect users’ information and prevent identity theft. It might be extra work/stress to have this embedded in your AI model development but it boosts the user’s confidence in the machine’s ability to safeguard data since attackers don’t have access to the data decryption key.
  • Access Control: Some people have no business accessing some information, so it doesn’t make sense if you grant them access to such. Ensuring only authorized individuals have access to data implies enforcing strict access control. It’s a good practice to implement Role-based access control (RBAC) to provide only people within a specific jurisdiction with access to data. For general asset security, Mandatory Access Control (MAC) is essential. Combining both adds some additional layer of protection and if cybercriminals manage to steal data for any reason, they will have to battle data encryption. 
  • Regular Auditing and Monitoring: This aspect of AI data security requires answers to three questions: “Who, When, and Why?” Despite putting some access control measures in place, implementing monitoring systems that keep track of users’ activities by answering those three essential questions helps identify in-house vulnerabilities and external intrusions. It also ensures complete compliance with security policies. 
  • Cybersecurity Measures: The cybersecurity world, to date, faces a challenge brought by people who assume cybersecurity isn’t essential until there is an incident. Engaging robust cybersecurity measures such as firewalls, intrusion detection systems, and anti-malware software to protect against cyber threats and unauthorized access is better than finding a solution after a hack.
  • Data Lifecycle Management: AI data security should start from the process of its collection and continue throughout its entire lifecycle, including its use, storage, and deletion. We have mentioned how bias is currently a problem in AI, which needs fast elimination. It implies the importance of every AI data source. If it’s not verified, then it’s not good enough. After ensuring the collection of bias-free data, the next thing to examine is compliance regulations and standards (GDPRHIPAA, etc.) to ensure data handling meets legal requirements and industry best practices. Secure storing and deletion should be prioritized as needed.
  • Employee Data Training: Data handling is an integral part of AI. Observations show most people think they understand how to handle data, but in actual sense, they are ignorant of it. To maximize data security, it is vital to make all personnel undergo compulsory training on data security best practices. The training must emphasize the importance of safeguarding sensitive data and recognizing potential security threats.

Conclusion

AI system integrity and data security depend on many factors which, when considered and implemented correctly, can revolutionize the AI industry by boosting users’ trust and enhancing AI models’ safe and secure performance.

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