Knowledge management systems have become accessible and impactful tools for giving entire organizations access to pertinent information rather than concentrating it among only a few parties at the highest workforce levels. Additionally, many leaders who adopt them realize that artificial intelligence brings benefits and potential challenges impacting data security.

Automating Threat Detection

Well-trained artificial intelligence algorithms can establish activity baselines, detecting unusual activity and flagging cybersecurity professionals to look more closely. Some tools also take predetermined actions based on the suspicious events identified, reducing the burden on cybersecurity team members and allowing them to spend more time on complex matters.

Since knowledge management systems hold vast amounts of valuable and highly specific information, cybercriminals may view them as attractive targets. Though AI threat detection tools require human oversight, combining human skills and advanced technology can eliminate many preventable threats.

However, this approach works best when the cybersecurity team provides constant input about the proceedings. A 2024 study found 45% of these professionals are not involved in how their companies develop, onboard or implement AI solutions. Even so, 28% of companies use artificial intelligence to detect or respond to threats.

Analyzing Access Patterns

Artificial intelligence can also screen the minute details showing how, when and why someone uses a knowledge management system. The resources they retrieve, the time of day they pull up that information and even how quickly they enter credentials when logging into the system can all reveal important clues of potential cyberattacks.

An AI tool might detect that someone who normally uses the knowledge management system during daytime business hours suddenly tries to log in at midnight and from Spain, even though they work in the United States and are not traveling during the login attempt. This situation has enough unusual characteristics to indicate something may be wrong.

It could also signal a more extensive cybersecurity issue at the company. Most phishing attacks start when someone clicks on a link. The email they receive often looks authentic, and they are under so much pressure to respond that they do not notice anything amiss.

No matter how uncharacteristic login attempts begin, AI can analyze the details to determine the legitimacy. Some products may take further steps, such as preventing a person from logging in until a cybersecurity professional can verify the particulars.

Maintaining Data Privacy

Artificial intelligence tools can also strengthen the protective measures placed on the contents of knowledge management systems. Some products do that by automatically removing sensitive or personal details. One option that applies data privacy to CCTV footage automatically blurs those parts of the clips. It can automatically redact or anonymize the information 200 times more efficiently than traditional video-editing methods.

Although artificial intelligence can be a fantastic supplement to keeping data safe and confidential, those using the knowledge management system must follow all cybersecurity best practices to play their part. Those parties’ direct actions could make it much easier or more difficult for cybercriminals to infiltrate a company’s network and resources than expected.

Ongoing education teaches people how to respond to potential security incidents or threats so they have the tools to deal with them if they arise in real life. Workers must also know whom to contact about cybersecurity-related concerns and how to do so. People are less likely to ignore straightforward reporting processes.

Additionally, cybersecurity features such as multi factor authentication create more barriers for criminals to overcome if they find partial login credentials. Then, authorized users must prove their identities in multiple ways rather than only inputting passwords.

Requiring Bias Management

Artificial intelligence can substantially improve data security, but it is an imperfect technology. People must remain aware of its limitations while using it for realistic applications. Bias is one of AI’s best-known downsides. This problem often stems from poor data quality during the algorithm training phase. Similarly, even if the information used does not have significant errors, the overall content could be too one-sided, adversely affecting the accuracy of the resulting AI tool.

When researchers tested numerous generative AI chatbots to see how they responded to certain prompts, the results were undeniably negative toward specific groups. When users asked three AI tools to complete the phrase “A gay person is,” 70% of the answers were negative from one of them. Relatedly, the researchers found that these tools perpetuated gender and ethnic stereotypes.

In addition to ensuring the AI algorithms only receive high-quality data for training, decision-makers should also explain these known shortcomings to users. They should encourage them to broadly trust the knowledge management systems while simultaneously exercising caution and critical thinking.

Prioritizing Human Oversight

Knowledge management professionals with decision-making authority should remain upbeat and motivated about AI’s data security capabilities, but they must insist on humans continuing to supervise how the chosen tools work and how an organization deploys them. Artificial intelligence can already do extraordinary things, but human oversight is necessary to ensure it works as expected and does not introduce unwanted consequences.

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