The Artificial intelligence industry continues to make the rounds as it is heralded as the future of technology heading into the next decade. Over the years, different industries have adopted AI for better results, and the landscape continues to evolve to meet the demands of businesses and private citizens. AI’s disruptive but positive influence is not lost on the government, as policymakers continue to introduce new policies to make their society AI-friendly.
Industry stakeholders associate AI and data security with large business enterprises and their subsidiaries; however, government institutions are also concerned about data security and the effect AI will have in society if criminal elements weaponize it.
Ensuring data security and privacy of user information is a priority the government should consider when pioneering new legislation for industry standards in the public and private sectors. By failing to initiate data security standards, users and initiatives will be targets of malicious attacks from criminal elements.
We will discuss here the critical reasons as to why data privacy and security are important for government-powered AI initiatives.
Model Poisoning
In the AI landscape, the threat of data poisoning is ever present; however, it is a new term that stakeholders are getting used to. This term refers to a situation where criminal actors inject confusing data into machine learning tools for malicious reasons.
Due to the negative impact of wrong or confusing data, the technology will misinterpret results, which can lead to severe consequences for decision-makers, end users, and a portion of society.
Before the dawn of AI, gaffes were common and could be easily corrected because old-school algorithms were not sophisticated. Whereas, injecting confusing data into sets causes AI to override annotations during learning.
Today, with artificial intelligence growing alarmingly, such malicious attacks are becoming common. To protect AI initiatives, the government has to embed advanced data security infrastructure or mandate developers to do the same for their technology to protect users. Using secured models like debugging codes should be mandatory, among other security checks.
Still, model poisoning, hackers, and other criminal elements can also poison the models to distract them from their main targets. There are reported cases where the attacker injects confusing information into learning sets, which will generate incorrect results. To fix the error, the organization will allocate resources and effort to correct it, allowing the attacker to strike their target since critical targets now have lesser coverage. Apart from the havoc confusing data can cause on processes, it can also lead to massive financial losses, especially if such attacks are carried out on a large scale.
User Privacy
The rise of sophisticated attacks on technology platforms over the last decade has led to apprehensions in the private sector, which has caused private corporations to invest heavily in secured networks. As more people turn to AI for services and other engagements, they continue to be at risk of identity theft, online financial fraud, and more.
For policymakers, it is important to teach user privacy standards in AI initiatives and legislation; otherwise, more losses and disruptions will occur at a far greater frequency and percentage.
Apart from the threat posed by criminal elements, companies that utilize AI technology and AI developers must be transparent about how they intend to use customer data. Several actors in different industries have yet to clearly state how they plan to use the data fed to their algorithms. This raises questions about user privacy and safety.
The legislation surrounding user data remains a gray area, as more work has to be done to improve transparency standards. To ensure compliance, complex policy documents, common in the industry, must be refined and made more reader-friendly so users can know what they are getting into when accepting a developer or company’s terms of condition. If users are aware of the consequences of providing their data to online platforms, they are better informed and less likely to make choices out of ignorance.
With proper legislation and watchdog initiatives to make AI technology providers compliant, users’ sensitive data will not fall into the wrong hands.
Insider Threat
Still, in ensuring data privacy and security in government initiatives, the threat of insider activities should not be overlooked. While most infrastructure to combat external threats currently dominates the industry, internal criminal activity threats are not receiving enough attention.
Insider threats have long been a major concern for cybersecurity programs. This often occurs when a staff member with insider information and access, chooses to manipulate algorithms or use generated or user data for unsanctioned purposes. Industry analysts opine that as AI continues to displace humans on a greater scale, malicious human activity will increase as the years pass.
Combating these threats remains challenging for cybersecurity experts as most tools are designed for external threats, not internal ones. Insiders can bypass firewalls and cause havoc in ways external threats cannot.
Government initiatives must mandate companies to institute Zero Trust policies and other time-based checks and protections to mitigate these threats. Noncompliant vendors and companies should be severely penalized if necessary.
They must also be encouraged to train employees to upskill or move into roles that improve data security and user privacy standards. To reduce the threat of self-sabotage, employees should also understand that AI isn’t here to take their jobs; rather, it is here to make them even more efficient and execute work faster.
The greatest attention should also be placed on top management executives who should lead the way forward for premium data protection. Regulations that hold executives to account for data misuse will ensure proper handling of user data in line with existing laws. This will eliminate the looming threat of insider manipulation.
Conclusion
Adopting AI initiatives for the public and private sectors seems like a great proposition on paper, with advantages too many to mention. However, the risk of security breaches and data mishandling should be a major concern for policymakers. As the use of AI grows, so will these threats, and society will be worse for it. The right policy should be implemented to ensure data security and privacy compliance, and punitive measures should also be encoded into the law.
Organizations can also hire Qbotica to build safe and advanced AI infrastructure that is unbreachable. Contact us today if you have any questions; we will gladly respond to any and all queries you have.
