This week we have a guest post from Katie Pierce looking at how companies can use AI to de-risk their exposure to cyber risks

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Image source: Mati Mango

How AI Is Transforming Data Classification to Protect Companies from Cyber Risks

Artificial intelligence (AI) significantly transforms data classification and enhances cybersecurity measures to protect companies from increasing cyber risks.

It also plays a vital role in digital asset management, ensuring that valuable digital resources are securely organised, accessed, and protected. Thanks to these technologies, it’s easier for companies to safeguard sensitive information and manage digital assets effectively.

There are many ways companies can integrate AI into their workflow for cyber protection and digital asset management. Let’s take a closer look at how AI is transforming data classification to protect companies from cyber risks.

Tools & Innovations in AI-Driven Data Classification and Cybersecurity

Machine Learning

Machine learning algorithms are at the core of AI’s ability to transform data classification and cybersecurity:

Supervised learning: This type of machine learning trains and teaches models to recognise patterns in labeled data. This means the AI learns from examples, like identifying malicious or benign activities in cybersecurity.
Unsupervised learning: Identifies new patterns or unusual activities without needing pre-labeled data. This is helpful for detecting unknown threats or subtle signs of attacks.
Deep learning: Uses neural networks to analyse complex data structures, such as large volumes of network traffic or intricate behavioural patterns. This allows AI to detect advanced cyber threats, including sophisticated attacks like advanced persistent threats (APTs), which often evade conventional detection systems.

Together, these machine learning approaches enable a more proactive, intelligent defence against cyber risks.

National Language Processing (NLP)

In data classification, NLP tools automatically scan and interpret unstructured text data. It can detect personal information, financial records, or confidential data in large datasets. This makes it easier to enforce proper security protocols and compliance regulations.

In cybersecurity, NLP-powered content inspection tools can analyse the language and context of emails, documents, and social media interactions to detect potential threats. Since NLP can flag suspicious emails or messages before they reach employees, it helps prevent attackers from crafting realistic, targeted messages.

Additionally, NLP is essential for text mining, which extracts and analyses relevant information from unstructured data. This capability helps identify patterns of suspicious behaviour, such as unauthorised data sharing, potentially leading to a data breach.

Behavioural Analytics

Behavioural analytics is an AI tool that watches how users and systems normally act to find possible threats. User Behaviour Analytics (UBA) uses AI to track what employees or systems usually do. These AI tools follow things like login times, file access, and network activity to see what’s normal.

Suppose there are big changes—like logging in from a strange place, accessing secret data, or sending a lot of sensitive information—the system flags this as suspicious. Finding these early signs can help companies stop data breaches and strengthen their cybersecurity.

Insider threat detection is another important part of behavioural analytics. It finds unusual access patterns or suspicious data handling by people inside the company. Insider threats are dangerous because these people often have access to important data and systems.

Encryption and Tokenisation

Encryption and tokenisation are important ways to protect data. AI tools are making these methods better for cybersecurity.

Encryption turns sensitive data into code only authorised users can access, while tokenisation replaces it with a unique identifier. AI tools can automatically find sensitive data and decide if it needs encryption or tokenisation.

They quickly scan data and recognise sensitive information like financial records or health data. This helps distinguish between public, confidential, and restricted information to protect the most important.

AI automates this process, reducing work for IT teams and ensuring accuracy. AI systems also adapt encryption protocols as data moves between systems, adding security, especially in cloud environments. These tools provide smarter ways to protect data and reduce the risk of breaches or non-compliance.

Best Practices for Enhancing Cybersecurity with AI

Since AI is becoming an invaluable tool for cybersecurity defences, following best practices can help organisations be more protected against emerging threats.

Here are some best practices to consider:

Clean and accurate data: Ensure that the data used for AI training is clean, accurate, and representative of real-world scenarios.
Data labelling: Properly label datasets to train AI models effectively. Consistent and accurate labelling is crucial for accurate threat detection.
Combine human and AI: While AI can automate many security tasks, human oversight is essential to make informed decisions and address complex threats.
Ethical considerations: Ensure AI systems are used ethically and responsibly, considering factors like bias, privacy, and accountability.
Penetration testing: Conduct regular penetration testing to assess the effectiveness of your AI-powered security measures and identify vulnerabilities.
Performance monitoring: Continuously monitor the performance of AI models to ensure they are detecting threats accurately and efficiently.

Final Words

AI is revolutionising cybersecurity by offering innovative solutions to address evolving threats. From advanced threat detection to automated incident response and data protection, AI-powered tools are empowering organisations to enhance their security posture and protect their valuable assets. Companies embracing AI and implementing best practices can effectively stay ahead of the curve and build a more resilient cybersecurity infrastructure.

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