Updated October 02, 2024
For nearly 42% of bank executives and 48% of credit union executives, cybersecurity is a top concern.
And there’s good reason for that concern: data breaches are at an all-time high, with the average cost of a data breach resulting in losses reaching $4.88 million, up 10% from $4.45 million in 2023.
So, what can kind of cybersecurity strategy should financial institution leaders implement to best protect their accountholders – and their institution’s critical data and systems – from cyberattacks?
A defense-in-depth approach is important in protecting against cyberthreats, as it provides multiple layers of security that can help prevent an attack. If one layer is breached, others are still in place to protect the system. This approach reduces the risk of a single point of failure and provides redundancy.
Even if a hacker manages to get past a firewall, they still need to bypass additional security measures like intrusion detection systems, encryption, and multi-factor authentication. This makes it significantly more difficult for an attacker to gain unauthorized access to sensitive data or systems. Moreover, defense-in-depth also includes policies, procedures, and awareness training for employees – who are crucial in preventing social engineering attacks and other user-targeted threats.
When it comes to Artificial Intelligence (AI) and cybersecurity, AI can be applied to systems or machines that mimic human intelligence to perform tasks. They can iteratively improve themselves based on the information they collect. AI can also identify patterns and trends out of large volumes of data, making this a powerful tool in detecting potential threats or malicious activities that a human might miss.
Machine Learning (ML), a subset of AI, involves the practice of using algorithms to parse data, learn from it, and then make a determination or prediction. In cybersecurity, ML algorithms are often used to predict the nature of a threat or to identify unusual behavior within a network that may signify a threat.
When combined together, AI and ML can provide proactive threat intelligence, automate repetitive tasks for quicker threat response, and enhance the ability to predict, prevent, and mitigate cyberattacks.
These technologies can be used at various layers of defense to enhance threat detection and response. For instance, ML algorithms can analyze network traffic to identify unusual patterns that may indicate a cyber-attack. AI can further automate responses to detected threats, reducing the time it takes to react and potentially limiting damage. Financial institutions that utilize AI in cybersecurity – along with ML – can also gain valuable benefits and capabilities like:
- Anomaly Detection: AI can be trained to learn what normal behavior looks like within a network and then flag any deviations from the norm. This could be anything from unusual data transfers to suspicious login attempts.
- Predictive Analytics: ML algorithms can analyze patterns and trends from past cyberattacks to predict and prevent future ones.
- Phishing Detection: AI can analyze emails to detect phishing attempts, which are often the starting point for many cyberattacks. It can look for suspicious links, language, and other signs that an email might be a phishing attempt.
- Automated Response: AI can not only detect potential threats but also respond to them. For example, if a system is under attack, the AI can automatically isolate the affected system to prevent the spread of the attack.
- Vulnerability Management: AI can be used to scan and monitor systems for known vulnerabilities that hackers might exploit.
- Threat Intelligence: AI can help in gathering and analyzing information about potential threats and threat actors, helping you stay ahead of cybercriminals.
- User Behavior Analytics: AI can analyze user behavior to detect suspicious activities. For example, if a user who typically logs in during regular business hours suddenly logs in at 2 a.m., the AI can flag this as suspicious.
- AI-Powered Antivirus: Traditional antivirus software relies on virus definitions to detect threats, but AI-powered antivirus software can proactively detect and block unknown threats by analyzing the behavior of files.
Remember, while AI and ML can significantly enhance cybersecurity, they are not a silver bullet and should be used as part of a comprehensive cybersecurity strategy. No single layer of defense is perfect. But by using a combination of these strategies, you can significantly reduce the risk of a cyberattack. The best defense against cyberthreats is a combination of robust cybersecurity software and good digital habits, like regularly updating software, backing up data, and being cautious of suspicious emails and links.
Learn more about protecting your critical systems with powerful technology and cybersecurity capabilities from Jack Henry™.
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