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Speak to us today!
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More from Academy Xi
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- Digital Transformation Academy
- AI Futures Academy
- Academy Xi: Nano Labs
48.9% of global executives and security experts see AI as a powerful weapon against emerging threats. What makes them think that?
You will see the answer throughout this article as we explore how AI in cyber security supports businesses against hackers. We will also dive into its challenges to help you prepare to handle them. Then, we will discuss whether AI technologies can handle new hacking techniques.
By the end, you can decide whether AI is your answer to fight digital threats and bolster your cyber security defences.
5 Ways AI Strengthens Cyber Security Against Hackers
Identify which AI-driven solutions align with your team’s needs and highlight the action points that can help you implement them.
1. Activates Real-Time Threat Detection To Outpace Hackers’ Moves
With artificial intelligence systems, you have a 24/7 watchdog that reacts the moment a hacker tries something sneaky. It detects evolving threats before they cause damage, which helps security teams stay one step ahead.
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For example, consider your online store with AI algorithms monitoring thousands of transactions every day. AI can quickly detect unusual activity, like a sudden flood of login attempts from many locations or a spike in high-value purchases, and instantly flag these as possible signs of a cyber attack.
The system can then immediately alert you or your security team about the suspicious activities it uncovered. Depending on your settings, you can either review and block them manually or have your tool do it automatically to prevent unauthorised access or fraud.
How To Maximise AI-Powered Real-Time Threat Detection
Customise threat parameters. To do this, tailor your AI algorithms to recognise threats that are relevant to your industry or business to avoid unnecessary alerts.
For example, let’s say you run a financial services company. Adjust your system to flag unusual transaction patterns, like multiple large withdrawals in a short time.
Additionally, you need to pair AI systems with human intelligence to remove false positives and fine-tune detection settings for accuracy. Human oversight, guided by clear data governance policies, helps validate those alerts to maintain system reliability.
2. Leverages Data Encryption To Safeguard Sensitive Information
Unlike traditional security tools, AI analyses real-time data to encrypt sensitive information dynamically. So if a cyber attack happens, AI can instantly identify vulnerabilities and encrypt affected systems automatically to block access to crucial data.
So let’s say AI detects an unusual number of login attempts on your database. It can quickly encrypt sensitive customer data in real-time. This on-the-fly encryption means cyber criminals get nothing useful, even if they intercept the data.
How To Maximise AI-Powered Data Encryption
Regularly update encryption protocols. To do this, you or an IT team member should schedule routine checks for software updates and implement the latest encryption standards recommended by AI security tools.
You should also merge your existing security tools. So connect your AI encryption system with firewalls, intrusion detection, and monitoring tools. This creates a multi-layered defence to make it harder for hackers to penetrate your security operations.
Monitor for unusual access attempts too. Configure your AI system to flag unexpected login patterns or data requests.
For example, if an employee account suddenly tries to access large volumes of customer data at odd hours, set your AI system to flag this unusual behaviour. Then, set automated encryption protocols to activate immediately when potential security breaches are detected.
3. Engages Phishing Prevention To Filter Out Malicious Links
AI can learn from thousands of cyber attacks to identify suspicious patterns. How does it do that?
Unlike traditional filters, AI adapts to new threats in real-time using machine learning and threat intelligence to spot even the most cleverly disguised phishing attempts. It also analyses user behaviour and scans emails for red flags, like unusual sender addresses or links directing users to fake websites.
Then, the AI filters out these malicious links before they reach your team, reducing the risk of accidental clicks.
How To Maximise AI-Powered Phishing Prevention
Customise filters for industry-specific attacks. To do this, program your AI to identify keywords and patterns related to scams in your industry. For example, let’s say you are in the health sector selling nutritional supplements, where phishers exploit the industry’s reliance on certifications and frequent product recalls.
They might send fake emails posing as health authorities, using phrases like:
- Immediate product recall
- FDA compliance required
- Urgent certification update
Program your AI to flag these keywords, especially when paired with specific terms like your product names, “batch number,” or “safety alert.” These scams prey on your urgency to protect your brand’s reputation and customer trust, so filtering them out is crucial.
Continue to update your AI models. Schedule reviews of your AI’s threat intelligence database and incorporate the latest phishing trends. Make sure your security team adds new threat patterns to keep the system adaptive.
Also, make sure you or an IT team member create reports to highlight common phishing attempts in your company. Then, share these insights in employee training sessions to teach staff how to recognise and avoid potential cyber threats.
4. Implements Vulnerability Management To Fix Weak Points Fast
AI-powered cyber security tools can identify security gaps to give you a head start in outsmarting hackers. They use training data and network traffic analysis to flag the weak points in your system that your security analyst may overlook.
Plus, they can pinpoint potential risks as they emerge, which lets you react and fix them quickly. They can prioritise which vulnerabilities pose the highest risk too so you can focus on isolating affected systems and patching up critical issues first.
How To Maximise AI-Powered Vulnerability Management
Use AI-powered systems to consistently scan your network and identify patterns that indicate potential weak points. For example, Avast lets you automatically scan and rescan your networks to make sure all the devices connected to them are secured.
You should also set up your AI tool to automatically download and apply security patches to guarantee your software is always up to date against the latest threats. For this, Avast also has an answer to protect your business through automatic patch management.
5. Deploys Fraud Detection To Strengthen Trust With Customers
Fraud is expected to cost eCommerce companies $48 billion every year.
Fortunately, you reduce the chances of being part of that with AI. It can monitor transactions in real-time, analysing patterns to spot any anomalies, like multiple failed login attempts or large, unexpected purchases. With this, you can make sure your customers’ data stays safe.
What does that result in?
When customers see that you prioritise data protection and quickly address suspicious activity, it strengthens their trust in your brand.
How To Maximise AI-Powered Fraud Prevention
Choose payment gateways that offer AI-powered fraud detection. Look for gateways that monitor transactions in real-time, identify patterns of fraudulent behaviour, and automatically block suspicious payments to protect both your business and your customers.
For example, if you choose Stripe, here’s what to expect from its machine-learning system:
Additionally, Stripe Radar lets you set custom rules to flag or block suspicious transactions using years of data patterns. For example, if certain transaction amounts seem more prone to fraud, you can set a rule to apply 3D Secure verification to purchases over a specific threshold.
Another way to maximise what AI brings to fraud detection is to educate your customers. To do this, provide clear, simple guides on recognising phishing scams and suspicious activity. You should encourage them to report any unusual transactions, which reinforces a strong data protection partnership.
3 Challenges Of Using AI In Cyber Security
Consider how these limitations can impact your defences and jot down the tactics to address any potential threats that might slip through.
A. Demands Extensive Setup
AI systems need to be tailored to your business’s unique environment, which often requires specialised knowledge in both AI and cyber security. Without the right setup, these systems can miss critical threats or generate too many false positives, leaving your business vulnerable to cyber attackers.
So, it’s no surprise that this complexity makes it tough for companies without an experienced IT team to integrate AI into their cyber security strategy effectively.
Start small and scale. To apply this, begin with a simple AI security tool that integrates easily with your existing security systems. As your team grows more comfortable, you can gradually implement more complex features to expand your defence.
You can also use pre-built solutions since many AI-powered cyber security platforms come with pre-configured settings tailored for common cyber security scenarios. You or your security analysts can fine-tune these configurations to fit your specific needs without starting from scratch.
If this feels overwhelming, especially if you’re a solopreneur without an IT team or a chosen tool yet, let Academy Xi help you. We host a hands-on workshop on detecting cyber threats with AI. You can book a call with us to arrange a personalised workshop,and enquire about topics such as which AI security tool suits your business best or how to fully use its features.
Take this chance to boost your business’s edge. In fact, 72% of companies found that online learning helps them stay competitive.
B. Trigger False Positives
AI systems can misinterpret legitimate activities as cyber threats because they lack the human ability to understand context fully. For example, a spike in website traffic because of a successful marketing campaign could be flagged as a potential attack.
Without that contextual understanding, AI often produces incorrect assessments, overwhelming security professionals with unnecessary alerts. This can waste time and cause real threats to be missed because of the sheer volume of false positives.
Collaborate with security professionals to consistently adjust your AI’s parameters based on recent threat patterns. Continuing to fine-tune helps the system better understand legitimate activities versus suspicious behaviour to make sure minimal human intervention is needed.
Suppose you manage a bank. Work with experts to fine-tune your AI system if you notice an increase in suspicious wire transfers. They can adjust the AI’s parameters to flag transfers that deviate from typical customer behaviour, like transactions to suspicious destinations.
In addition, set risk-based alerts. To do this, prioritise alerts by risk level, so you or your security professionals can focus on high-priority threats first. This approach reduces alert fatigue and guarantees more accurate responses to real cyber threats.
For example, let’s say you have an eCommerce store. Set risk-based alerts to prioritise large international transactions or unusual shipping addresses. This way, your AI flags high-risk alerts first, but still keeps an eye on low-risk transactions for any signs of fraud.
If you have a team, make sure they are trained to identify false positives so they can efficiently differentiate between real threats and harmless activities. For example, you can provide them with previous false positive case studies and how they were resolved. We at Academy Xi can also provide group workshop sessions to help you with this.
C. Raises Questions About Data Privacy
AI solutions often require large amounts of sensitive information to detect potential threats and respond to security incidents. In other words, it collects and analyses personal customer data. But this can raise red flags if mishandled.
Customers can feel uneasy knowing their security data is constantly monitored. Plus, misuse or poor data management can damage trust and prompt legal or reputational issues.
Limit the amount of personal data the AI system collects to only what’s essential for detecting threats. To do this, you or an expert should set up filters that prevent the collection of irrelevant personal details, like social security numbers.
Perform audits to review what data the AI system is collecting. Then, adjust its parameters if you find it’s gathering more information than necessary. This reduces exposure while still allowing AI to function effectively in responding to security incidents.
Also, establish transparent policies outlining exactly how AI will use security data. Make sure customers understand what data is collected, why it’s necessary, and how it will be protected to maintain trust.
Here’s a great example from an online guitar course for beginners website:
Along with the key details about what, when, and how data is collected, the company also dives into other important info, like third-party behavioural tracking, Google Ads, and how they’re using cookies.
This transparency helps customers understand exactly how their data is being used. Also, being upfront about things like tracking and cookies helps the company make sure its users can make informed decisions about their privacy.
Can AI Adapt To Handle New Hacking Techniques?
Yes, AI can adapt to handle new hacking techniques. Cyber attackers constantly evolve their strategies, but so do AI-powered cyber security solutions.
Unlike traditional methods, which rely on predefined rules, AI learns from the patterns it detects over time. You can use this to improve AI’s ability to recognise and respond to various cyber threats like botnet.
But AI doesn’t work in isolation. It uses several advanced technologies to stay adaptable and effective. Let’s look at how AI handles evolving threats through some key technologies.
Machine Learning (ML)
Machine learning is a branch of AI that lets systems learn and get smarter over time, without needing constant reprogramming. It analyses data to spot patterns and make decisions based on what it learns.
What does this do for cyber security?
It means your AI can analyse past cyber incidents to predict and identify new patterns. Through ML, your AI can refine its response to different types of attacks and continuously improve its capability to recognise emerging threats.
Neural Networks
Neural networks are designed to work like the human brain to let machines make connections and uncover relationships in data. Just like our brains learn from experience, neural networks process vast amounts of information and identify relationships between different data points.
In cyber security, neural networks make AI powerful for detecting complex and subtle hacking techniques that don’t follow clear patterns.
For example, a hacker can use low-level, slow-paced attacks designed to avoid detection, like making tiny changes over time within a system. Traditional systems can miss this, but a neural network can detect these small irregularities and flag them as suspicious.
Natural Language Processing (NLP)
NLP lets AI understand and interpret human language. One of the key applications is detecting phishing emails and social engineering attacks, where hackers use deceptive messages to:
- Impersonate you to target your customers
- Trick employees into revealing sensitive information
For example, NLP can analyse an email’s content and flag phrases like “urgent action required” or unusual sender addresses that often signal phishing attempts. It can also identify subtle cues, like misspellings or tone inconsistencies, that can indicate a malicious message.
With NLP, your AI-powered cyber security solutions can block harmful communication attempts to keep sensitive information safe.
What To Consider When Adapting AI Into Your Cyber Security Strategy
Before diving into AI-powered cyber security, here are 5 key things to consider to make the most out of your AI investment:
I. Cost
AI solutions, whether in-house or from a third-party provider, can be expensive to implement and maintain. Make sure your budget aligns with both short-term setup costs and long-term maintenance.
II. Scalability
Get an AI model that can scale with your business as you grow, so it can handle increased data and more complex cyber threats.
III. Data Quality
AI is only as good as the data you feed it. Make sure your security data is clean, accurate, and diverse to improve threat detection.
IV. Compliance
Make sure your AI system complies with industry regulations like GDPR or HIPAA, especially if you handle sensitive customer data like medical information.
V. Vendor Support
If you don’t have your own AI model, make sure that your AI provider offers strong customer support and regular updates to keep the system running efficiently and up to date with the latest threats.
Conclusion
Take the next step now to integrate AI into your cyber security strategy. So identify your biggest threats to know where AI can offer the most value. Then, focus on immediate concerns—like automated threat detection or phishing prevention—based on your business’s needs.
But make sure you consistently update and review your AI tool. Otherwise, hackers will continue to have an easy time breaking into your system.
To keep you up to speed, Academy Xi offers exclusive 1-on-1 workshops you can join. If you have a team, we can re-skill your members with our courses. Use our platforms to build your knowledge of AI, cyber security, and data to make sure your system, business, and customers stay safe from hackers. Book a call now and let’s get your strategy up and running.
Credit: This article was written by Burkhard Berger, Founder of Novum™, on behalf of Academy Xi.
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