How Companies Are Using AI To Fight Cyber Threats

Company networks today are more like ecosystems than static systems. Data is constantly moving between devices and servers, and that nonstop activity makes it tough to keep track of everything at once. The only way to stay ahead is with tools that can watch your network in real time and react the moment something changes.

Enter Artificial Intelligence. AI can interpret all of these interactions simultaneously. It recognizes meaningful patterns and subtle irregularities, which could indicate emerging vulnerabilities.

Cybercrime in the modern era

In the past, cybercrime only involved rudimentary viruses and worms. Most of which manifested at the turn of the century.

In stark contrast, today, advanced attackers deploy sophisticated AI-powered techniques in their process. These can: 

• mimic human behavior
• generate deepfake identities
• execute credential theft with precision

Cybercriminals now expertly exploit vulnerabilities across cloud platforms and IoT devices. As well as financial networks and enterprise databases.

They are so cunning that they move at top speed and with a cloak of anonymity. You need to beware of these digital thieves. It’s time to stop these system intrusions and protect your assets.

This is the reason why AI uses cutting-edge detection systems. It acts like your digital guard dog. Always monitoring your network activity. Ready to respond rapidly to any anomalies.

AI-enhanced global threat databases

Most organizations are being confronted with an unprecedented volume of cyber threat intelligence. Dispersed across a myriad sources, which range from malware signature repositories to dark web forums.

To fight these threats, you need to employ a system capable of synthesizing vast, heterogeneous datasets. 

You could also consider using AI-driven threat databases. They ingest terabytes of structured and unstructured data. Which will include: 

• malware hashes
• exploit reports
• adversary communications 

Advanced machine learning algorithms correlate these inputs to organize them. This establishes probabilistic linkages that highlight emerging threats or malicious campaigns. Making it obvious which is the correct course of action. 

Intelligent Anti-Money Laundering systems

Financial networks are like the arteries of the global economy. This is why they always attract digital pirates to tap into their supply. However, Anti-money Laundering (AML) systems are here to step in and save the day.

​​Money laundering is rarely a single transaction. It is a sequence of carefully designed movements, performed with precision. Where illicit capital is layered and restructured. Then it is reintroduced into regular circulation.

AML systems are designed to identify and prevent this criminal financial activity. They can protect your business from being exploited in the cyber universe.

AI in Anti-Money Laundering

AI technology is now the most secure method for detecting potential money laundering. AI provides the computational architecture to track these dispersed fragments as a unified whole. The AI establishes multidimensional profiles of all the financial activity on your network. 

Firstly, it establishes a baseline pattern of normal transactions. Then the system flags all deviations that may indicate: 

• fraud
• embezzlement
• organized crime

Research in an article about AI for anti-money laundering, by SEON, shows how predictive algorithms detect subtle money laundering schemes. These include layered transfers or unusual cross-border activity. 

Once the suspicious behavior is identified, your AI can immediately quarantine the transaction. 

Detect payment fraud in digital networks

Payment fraud has become increasingly complex in the modern era. Mostly because fraudsters are taking advantage of technical digital systems. Rule-based monitoring cannot keep pace with the speed and volume of these modern transactions. 

Instead, intricate AI-powered systems can effortlessly analyze the transactional data across all your networks. This helps you to immediately identify suspicious patterns: 

• rapid-fire small purchases
• repeated failed authorizations
• anomalies in account behavior

These models use historical data to recognize emerging fraud schemes. It allows them to differentiate between legitimate users and potential criminals.

If the system suspects something is wrong, it can indicate high-risk transactions for review. These alerts are prioritized automatically. 

Adaptive access controls

Modern systems all require user authentication as a method of privacy.

We suggest you implement an adaptive access control system. These dynamically adjust the authentication requirements based on multiple contextual factors, which include: 

• device fingerprinting
• geolocation
• network characteristics
• time of access
• behavioral patterns

As per usual, machine learning models can work their magic for you. For instance, access attempts from unusual locations or devices usually indicate account compromise. This groundbreaking type of authentication balances security with user convenience.

To do this effectively, advanced AI algorithms assign risk scores to each login attempt. Then they will automatically trigger additional verification steps if suspicious behavior is detected. This reduces false positives.

Behavioral analytics for instant intrusion detection

Corporate environments have grown into intricate ecosystems. With facilities where core applications link to: 

• third-party APIs
• cloud services
• distributed user endpoints

This level of interconnectedness will dramatically improve productivity at your company. However, any unsecured integration becomes a viable target.

Using enhanced behaviour analytics, these innovative algorithms can detect: 

• weakly protected endpoints
• misconfigured servers
• outdated libraries 

Once they gather this data, they can protect these vulnerabilities in your system. Long before any attackers try to probe them. 

Company vulnerabilities will be logged and actively contextualized within the broader attack surface. In essence, they help with the rapid mitigation of further attacks.

Detect anomalous behavior in your network

The volume of data traveling across enterprise networks makes manual oversight nearly impossible. Terabytes of packets flow through: 

• routers
• VPN gateways
• internal switches 

They do this several times per hour. They each carry subtle indicators of either daily use or hidden intrusion attempts.

AI-driven traffic analysis brings order to this flood of information. Accomplished by learning the expected rhythm of communication between applications and external hosts. Tools like TrafficLogix make it look easy.

The future of digital security is unfolding before your eyes. As you know, innovation moves with relentless momentum. It is always shaping possibilities that once existed only in imagination.