How secured is your AI system?

Published by FirstAlign

The use of Artificial Intelligence (AI) across industries is growing as we speak. It is widely implemented in banking, retail, medical, and manufacturing, to name a few. One of the powerful functions of AI is the ability to detect fraudulent patterns. That’s the primary reason it is widely augmented as security for data and other systems. That brings us to the big question, how secure are your AI algorithms?

Like any technology, AI is also prone to security threats and more so in unique ways. Deloitte survey states that despite active enthusiasm among companies to adopt AI, AI-related risks acts as a prime hindrance. Cybersecurity is the most worrisome AI risk, followed by AI failures, misuse of data, and regulatory uncertainty.

Complexity of AI

The complexity of AI is that it involves enormous volumes of data. It means a cloud platform is handling the data, which needs a separate layer of protection. AI has three types of data – training data, testing data, and real-time transactional data. Real-time transactional data is the data provided after the system is in production, but one cannot rule out the vulnerability of training and testing data. It can give great insights.

This data is at risk of being manipulated. It is called data poising. Hackers corrupt the data in such a way that it can bring down the entire AI system. Such attacks have not yet been reported, but it is a possibility in the future as attackers are leveraging publicly available AI algorithms.

System bias and model drift are other risk factors. When a system is under-trained or trained on outdated data, it leaves the entire system exposed. Adapting to changing environments will keep the system reliable. Companies are already dealing with system bias. System bias creates ethical problems. For example, news website ProPublica reports that a criminal justice algorithm used in Broward Country, Florida, mislabeled African-American defendants as “high risk” at nearly twice the rate it mislabeled white defendants.

Mitigating risks

Here are a few steps that can help reduce AI risk

  1. Have well-established testing and auditing procedures. It can either be done internally or by an independent auditing team. Make sure your policies are aligned with social, government, and business values. Build trust by having these measures in place.
  2. As governments are keen on how businesses use data, new regulatory policies are always in development. Hence monitor if your regulatory efforts are in sync with changing government policies.
  3. One crucial step in mitigating AI-related risks is to keep a formal inventory of the organization’s AI. It helps keep track of all the uses of AI and sees if every area of its implementation is secured enough.
  4. Address ethical issues, given the high-level concern on AI biasing. Companies should create good ethical policies and see that their systems are aligned with the same principles.
  5. Security should be in design. Companies should use well-known data protection methods such as encryption and tokenization to prevent the misuse of data. Since the AI system will interact with other systems, we need to see that every part of it is secured from end to end.

With more and more private data used by AI systems, privacy and security are of great concern.  Governments are developing new policies to determine accountability and liability regime for AI systems in case of mishap. Leading AI service providers are paving the way in research and development of improved methods of data protection. Steps like having established testing and auditing procedures, maintaining AI inventory, and addressing any drift or bias in the system will go a long way in building secured AI systems that customers can trust.

References

[Featured image by Jan Alexander from Pixabay]

Click here to connect with us

Spread the word

Related posts