Explanation
Picture this: It's Saturday morning and you made breakfast for your family. The pancakes were golden brown and looked delicious, but everyone, including you, felt sick immediately after eating them. Unbeknownst to me, the milk I used to make the dough had expired several weeks ago. The quality of the ingredients affected the meal, but everything looked fine from the outside.
The same philosophy can be applied to artificial intelligence (AI). Regardless of its purpose, the output of AI is directly related to the quality of its input. As AI grows in popularity, security concerns regarding the data fed to it are being questioned.
The majority of organizations today have integrated AI into their business operations in some capacity, and threat actors are taking notice. In recent years, a tactic known as AI poisoning has become increasingly prevalent. This new malicious activity involves injecting deceptive or harmful data into the AI training set. The tricky thing about AI poisoning is that even if the input is compromised, the output can initially continue as normal. Deviations from the norm become apparent until threat actors have a solid grasp of the data and launch a full-scale attack. The effects can range from a slight inconvenience to damaging your brand reputation.
This is a risk that affects organizations of all sizes, including today's most prominent technology vendors. For example, over the past few years, adversaries have launched several large-scale attacks to take down Google's Gmail spam filter and even made Microsoft's Twitter chatbot hostile.
Defense against AI data poisoning
Fortunately, organizations can take the following steps to protect their AI technology from potential contamination.
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Build a comprehensive data catalog. First, organizations need to create a live data catalog that serves as a central repository for the information that feeds into their AI systems. Whenever new data is added to the AI system, it must be tracked in this index. Additionally, to ensure transparency and accountability, the catalog should be able to categorize the who, what, when, where, why, and how of the data flowing into the AI system.
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Create a normal baseline of users and devices that interact with AI data. Once security and IT teams have a solid understanding of all the data in their AI systems and who has access to that data, it's important to create a baseline of normal user and device behavior.
Compromising credentials is one of the easiest ways for cybercriminals to break into your network. All a threat actor has to do is play a guessing game or purchase one of the following: 24 billion combinations of usernames and passwords Available on the Cybercrime Marketplace. Once they gain access, the attacker can easily access her AI training dataset.
By establishing baseline user and device behavior, security teams can easily detect anomalies that could indicate an attack. This often helps stop attackers before an incident develops into a full-blown data breach. For example, let's say you have an IT executive who typically works out of a New York office and oversees an AI data training set. One day we find out that he is operating in another country and adding a large amount of data to the AI. If your security team already has a baseline of user behavior, they can quickly determine that this is an anomaly. A security officer will then speak with the executive to confirm whether they took that action or, if not, temporarily disable the account until the alert is thoroughly investigated to prevent further damage. You can.
Responsible for AI training set
Just as you need to check the quality of your ingredients before making a meal, it's important to ensure the integrity of your AI training data. AI intelligence is intricately related to the quality of the data being processed. Implementing guidelines, policies, monitoring systems, and improved algorithms will play a vital role in ensuring the safety and effectiveness of AI. These measures protect against potential threats and enable organizations to take advantage of the transformative potential of AI. It's a delicate balance that organizations must learn to leverage the capabilities of AI while remaining vigilant in the face of an ever-evolving threat landscape.
