The rapid evolution of AI has made it a critical aspect of business across all industries. We often hear about the benefits of AI in managing cybersecurity risks. The conversation hardly focuses on data security in AI systems. Threat actors are increasingly leveraging AI to orchestrate attacks. AI systems face a constant threat from these malicious actors. So, here are the current 2022 risks in artificial intelligence.
System Manipulation
The most prevalent attack on AI systems involves manipulating high-volume algorithms to make false predictions. Typically, this entails introducing malicious inputs into the system. Such attacks are designed to show AI systems a picture that doesn’t exist in the real world, compelling them to make decisions based on unproven data.
Since 2020, there has been a sharp rise in the online manipulation of AI systems. The Internet plays a significant role in the development of these systems. AI machines are connected to the Internet, giving threat actors a clear attack vertical. Attackers only need to provide false inputs to your AI system and retrain it gradually to give faulty outputs.
More AI systems are experiencing such attacks, and their effects can be far-reaching and lasting. For instance, if an AI system gets manipulated to provide sensitive customer information to hackers, the target company could face significant penalties. As a result, system manipulation is among the significant AI risks that IT teams should watch out for in 2022 and beyond. Streamlining and securing AI system operations can go a long way in preventing system manipulation.
Data Difficulties
Breaking down, linking, sorting, and using data properly is becoming harder, thanks to the vast volume of amorphous data from sources such as social media, sensors, the web, mobile devices, and the Internet of Things. As such, it’s pretty easy to fall prey to drawbacks such as accidentally revealing or using sensitive data hidden among anonymized data.
For instance, a patient’s name may be redacted from a medical record section used by an AI system. It could also be present in a medic’s notes section of the medical record. Considering such overlaps is essential in managing regulatory compliance with the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other regulations.
Misbehaving AI Models
In 2021, there was a record number of reported cases related to misbehaving AI models, a trend expected to continue in 2022. AI models can be problematic when they deliver prejudiced results. This often happens when a population sample is underrepresented in the data you use to train your AI model. Besides providing biased results, such AI models can become unstable or return conclusions with no actionable recourse for those impacted by the models’ decisions.
For instance, when the AI model of a bank’s loan system starts to misbehave, customers could be denied loans without knowing what can be done to reverse the decision. AI models also can potentially discriminate against protected classes and groups by intertwining income data and zip codes to create targeted offerings.
Interaction Issues
The interface between machines and people is another significant risk area. There has been a visible challenge in manufacturing, automated transportation, and infrastructure systems in recent years. Fatal accidents can happen if operators of heavy machinery, vehicles, and other equipment fail to recognize when the AI systems need to get overruled. The unexpected could also happen if the operators are slow to override the systems. An excellent example is accidents involving self-driving cars.
Human judgment can also be defective in overriding AI system results. In a data-analytics organization, for instance, scripting errors, miscalculations in model-training data, and lapses in data management can compromise compliance, security, and privacy.
All these are excellent examples of interaction issues between AI systems and users. These issues often lead to unintended consequences. However, implementing rigorous safeguards ensures the seamless interaction between your AI system and its users.
Data Privacy
AI systems collect, process, and transmit large volumes of datasets. Maintaining the confidentiality and privacy of the datasets is critical for IT teams. In particular, this is essential when the data is built into the AI system itself. Hackers can launch discrete data extraction attacks that place your entire AI system at risk in such scenarios.
Smaller sub-symbolic function extraction attacks can get launched with less effort and fewer resources. The only way to protect yourself is by implementing policies to prevent extraction attacks. Securing your AI systems against extraction attacks also helps to keep your data safe.
Data Corruption and Poisoning
Since AI systems rely on large datasets, your organization must guarantee their reliability and integrity. If your data gets poisoned or corrupted, your AI machines might provide malicious or false predictions. These attacks work by corrupting your data in a manner intended to manipulate the entire learning system.
As more organizations incorporate AI systems into their operation, the risk environment also grows. You can prevent data poisoning and corruption by implementing strict PAM policies to minimize the access threat actors have to your model-training data.
Confronting Current AI Risks
AI is a relatively new technology. As a result, the cybersecurity threats AI systems face are also new. With the risks of AI growing by the day, many believe the easiest way to temper or prevent these risks is by implementing some form of regulation.
Maintaining compliance with regulatory standards governing overall cybersecurity risks may not work for AI risks. Implementing a regulation that specifically targets AI systems will make it easy to ascertain that the systems are developed safely and for everyone’s benefit. Although the regulation of AI implementation is long overdue, the research itself shouldn’t get regulated. It would be akin to holding back progress in technology.
A regulatory framework for AI implementation will also be critical in preventing present and future attacks. The predicted emergence of offensive AI is an excellent example that the technology can be used to launch new attacks in the coming years. With attackers using AI to mimic human intelligence rather than actions, it’s easy to see why strong regulation is needed.
