Mai 17, 2024

7 Examples of How AI is Improving Data Security

Bryan Arnott

The mobile phone reinvented – well, pretty much everything we do.

 

And though we’re still in its early days, it’s looking as if Artificial Intelligence (AI) will have a similar widespread impact.

Get Visibility and Control Over Your Data

The ability of AI to analyze vast amounts of data and identify patterns makes it an invaluable tool for enterprises that have more data than they even know exists. AI can detect subtle anomalies that could indicate a security breach and these systems can learn and adapt over time, improving their effectiveness in thwarting attacks and reducing false positives.

These characteristics give it all the makings of changing the face of cybersecurity. But we don’t have to guess about the potential of AI; here are some examples of AI being used in data security today:

  1. Data Discovery and Classification
  2. Threat Detection
  3. Identity and Access Management
  4. Phishing Detection and Prevention
  5. Security Information and Event Management
  6. Vulnerability Scanning and Patching
  7. Cybersecurity Training

 

Data Discovery and Classification

With petabytes of data flowing through on-premises and cloud-based storage and being accessed by users across the world, on-going data discovery and classification is a critical element of any data security strategy.

AI can significantly improve the accuracy of data classification. For example, Forcepoint Data Security Posture Management (DSPM) uses a 50-dimensional model with machine learning to continuously train as it classifies, enabling it to get more precise over time. This ultimately reduces false positives and enhances overall security posture.

 

Threat Detection

One of the most popular examples of AI in data security is the development of sophisticated threat detection systems. These systems leverage machine learning to analyze network traffic patterns and identify unusual activities that could signal a cyberattack.

AI-powered tools can monitor network traffic in real time, swiftly identifying and alerting security teams to any suspicious behavior. As cyber threats evolve, AI systems continuously learn and adapt, ensuring they can detect new and emerging threats and stop threat actors before they compromise data.

 

Identity and Access Management

The anomaly detection that AI excels at can also be used to monitor access to applications. If there’s an unwelcome visitor, AI can help security teams spot it quickly.

Logging and IAM tools can use AI to analyze user behavior to detect anomalies, such as unusual login times or data access patterns, which could suggest a compromised account. Upon detecting an anomaly, AI can automatically take predefined actions, such as blocking access or alerting security personnel, to mitigate potential damage.

 

Phishing Detection and Prevention

Phishing attacks are an ever-pervasive threat, and AI is instrumental in combating them through Natural Language Processing (NLP).

AI systems such as Abnormal Security can scrutinize the language and metadata of emails to detect signs of phishing, such as subtle language anomalies or spoofed email addresses. As phishing techniques evolve, AI tools can learn from new examples, improving their ability to detect even the most sophisticated phishing emails.

 

Security Information and Event Management

A natural enhancement to a solution that runs on data, Security Information and Event Management (SIEM) solutions are a great example of AI enabling efficient analysis of security data and event logs.

AI can correlate data from various sources, providing a holistic view of security events and aiding in the identification of complex attack patterns or insider threat behavior. It also helps prioritize threats by assessing their severity, ensuring that security teams can address the most critical risks to data first.

 

Vulnerability Scanning and Patching

Patch management is a challenge for all companies, large and small. Keeping up with the ever increasing amount of vulnerabilities without siphoning time and energy from IT teams is difficult to balance.

AI can help in various parts of the process, from identifying which patches are most critical based on severity to addressing them through automation. This always-on solution can keep the enterprise safe from emerging threats that may otherwise put data at risk.

 

AI-Enabled Cybersecurity Training

Lastly, AI is transforming cybersecurity training by providing interactive and personalized learning experiences.

AI can assess an individual's knowledge and skills, tailoring the training content to address their specific learning needs. AI-powered simulations create realistic scenarios for cybersecurity professionals to practice their skills, enhancing their ability to respond to real-world threats.

 

Embracing AI for Data Security

At RSA 2024, several customers expressed interest in using DSPM to categorize their own data before sending it to be ingested by an LLM so they can use AI insights safely and more effectively. Hear more from Todd Bursch, a Sr. Consulting Sales Engineer:

 

Businesses that embrace these AI innovations will be better equipped to safeguard their data against the myriad of cyber threats that loom in the digital age. As the technology matures, examples how AI is being used in data security will only become more common.

Learn more about how you can incorporate AI into your data security technologies and strategies by talking to an expert today.

Bryan Arnott

Bryan Arnott is a Senior Content Marketer and Digital Strategist at Forcepoint.

Read more articles by Bryan Arnott

Über Forcepoint

Forcepoint ist einer der weltweit führenden Anbieter von Cyber-Sicherheit im Bereich Anwender- und Datensicherheit und hat es sich zur Aufgabe gemacht, Organisationen zu schützen und gleichzeitig die digitale Transformation und das Wachstum voranzutreiben. Unsere Lösungen passen sich in Echtzeit an das Nutzerverhalten an und ermöglichen Mitarbeitern einen sicheren Datenzugriff bei voller Produktivität.