AI has permeated various aspects of our personal and professional lives, from the voice assistants in our homes to the platforms collating and delivering business data. AI adoption in business has doubled since 2017, with 50% of companies leveraging AI for at least one business function.
In many ways, AI has made our lives easier. At the same time, AI has also made it easier for cyber criminals to exploit vulnerabilities, with some AI platforms becoming targets.
Understanding AI Attacks
Artificial intelligence attacks are deliberate interventions to trigger a malfunction in AI systems. These attacks capitalise on the vulnerabilities in AI by manipulating algorithms or datasets to distort their functionality. The core intention is to cause adverse effects, from simple service disruption to more severe outcomes like generating false information or providing unauthorised access to sensitive data.
Identifying AI Attacks
Early detection is key to protecting your organisation from AI attacks. One indication of a possible attack is unusual or unexpected results from your AI systems. If an AI model suddenly starts producing outcomes that deviate from expected or trained behaviour, this could signal a potential issue. For example, you might notice inconsistent prediction patterns or an unexplained rise in false positives or negatives.
Abnormal input patterns are another red flag to watch for. If your system starts processing inputs that differ greatly from the usual patterns, it might be subject to an attack. Similarly, a drastic drop in performance or accuracy, without clear reasons or changes in the system or data, should also raise concerns. Such sudden changes might indicate that an attacker has tampered with the model or data.
Finally, any signs of unauthorised system access or unusual activity related to the AI system or its components should immediately prompt an investigation. It’s essential to stay vigilant and adopt a proactive approach to protect AI platforms.
Preventing AI Attacks
To protect your business from the consequences of AI attacks, you can begin by using trusted data sources to train your AI. In addition, any inputs given to the AI platform should be validated to reduce the chances of someone feeding malicious information into the system.
Robust authentication processes are also a must for securing AI. By implementing strong authentication and authorisation protocols, your business can restrict access to AI systems and data. It’s a good idea to educate your staff on using authentication so they can do their part in protecting your systems.
Anomaly detection is another effective preventative measure. This method identifies abnormal behaviour or patterns within the input data, model outputs, or system activity. Anomaly detection acts as an early warning system, flagging deviations from expected behaviour and enabling your team to quickly investigate and respond to potential AI attacks. Educating your team on recognising any issues also goes a long way in preventing AI attacks.
Conclusion
As artificial intelligence becomes increasingly integrated into various sectors, AI systems’ potential for intentional manipulation and exploitation will increase. With the potential for widespread disruption, financial loss, and reputational damage, addressing AI attacks must be a top priority for your business to safeguard assets, maintain trust, and ensure the integrity of operations.
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