Cybersecurity in the Age of Artificial Intelligence
The Evolving Landscape of Cyberattacks
Cyberattacks deployed with AI are becoming increasingly sophisticated and evasive, with hackers using advanced technologies to mimic the CEO’s voice, generate fake video streams, and trick employees into transferring funds to fraudulent accounts. In 2019, a cyberattack on a UK-based energy firm used AI to mimic the CEO’s voice and trick a staffer into transferring $243,000 to a fake account. In 2021, a cyber espionage campaign targeted international telecom companies with AI-generated phishing emails. And last year, hackers using AI injected fake video streams into the biometric verification process of crypto exchange Bitfinex, ultimately earning themselves $150 million worth of digital assets.
The Power of AI in Cybersecurity
The good news is that the power of AI cuts both ways, and an increasing number of enterprises are exploring opportunities to deploy AI (and its subfield, machine learning) in their own cyber defenses – fighting fire with fire, as it were. Today, more than two-thirds (69%) of enterprises believe AI is necessary for cybersecurity because threats are rising to levels beyond the capacity of cyber analysts, according to Deloitte.
The Future of Cybersecurity
While the prevalence of AI in cybersecurity programs is still in its relative infancy, the potential benefits are clear: AI has the ability to process vast amounts of data, recognize patterns quickly, and make informed decisions, helping organizations identify vulnerabilities and threats, minimize or eliminate threats, and respond more quickly.
How AI Cybersecurity Tools Will Improve Vulnerability Testing
Software engineers strive to write secure code, but sometimes mistakes happen. They might inadvertently introduce vulnerabilities by using improper error handling or not validating user inputs; complex systems might make it challenging for them to anticipate all potential security vulnerabilities; or software engineers might face tight deadlines to deliver new features quickly, leading to shortcuts or compromises in code quality and security.
With the use of AI, organizations could improve the speed and efficiency with which they can detect and remediate potential vulnerabilities in code, creating a more secure environment. AI-powered tools could scan through codebases to identify potential vulnerabilities by analyzing patterns to detect common risks such as SQL injections and cross-site scripting. AIs could also be trained on large datasets of known vulnerabilities to identify similar patterns in new code, thereby revealing previously unknown vulnerabilities or zero-day exploits.
How AI Cybersecurity Tools Will Empower Threat Detection
Identifying potential security threats at an early stage helps prevent data loss and unauthorized access to intellectual property and protect valuable assets that make up an organization’s "crown jewels." This helps organizations avoid costly data breaches, financial losses, and reputational damage.
At many organizations, security analysts are responsible for manually monitoring system logs, network traffic logs, and application logs for suspicious activity that may indicate a security breach. This process can be time-consuming and straining on individuals. AI, however, can analyze vast amounts of data and establish a baseline of normal behavior for systems, networks, and users. By detecting deviations or anomalies, AI can help identify potential security threats, such as unauthorized access attempts, unusual network traffic, or abnormal user behavior.
How AI Cybersecurity Tools Will Accelerate Threat Containment and Response
When a threat has been detected or a security incident has occurred, moving quickly to rectify the situation is crucial. "It’s all about speed when it comes to threats, compromises, breaches, and ransomware attacks," says Adam Levin, author of Swiped: How to Protect Yourself in a World Filled With Scammers, Phishers and Identity Thieves, and co-host of the What the Hack podcast. "You need to be in a position to move as quickly as possible to plug the hole and stop the problem so you can begin working on the solution. The faster you can contain the threat, the faster you can defend against it."
Traditional methods of threat containment and response rely heavily on manual intervention. When a security incident occurs, for example, analysts must manually identify the affected systems, isolate compromised assets, and implement containment measures. Security analysts will manually review security alerts, logs, and forensic data to understand the scope of the incident, then work to patch systems or reset compromised credentials. These processes take time and introduce opportunities for human error, which may further delay resolutions.
With AI, algorithms can automatically assess the severity and impact of the threat, identify which assets are impacted, and even orchestrate response actions, Schwenger says. This includes a number of autonomous endpoint management tasks that support better endpoint security, including isolating infected endpoints, blocking malicious traffic, or turning off compromised services.
Conclusion
The use of AI in cybersecurity is becoming increasingly crucial in the fight against sophisticated cyberattacks. As the threat landscape continues to evolve, it is essential for organizations to stay ahead of the curve by leveraging AI-powered tools to improve vulnerability testing, empower threat detection, and accelerate threat containment and response.
FAQs
Q: What is the current state of AI in cybersecurity?
A: The prevalence of AI in cybersecurity programs is still in its relative infancy, but the potential benefits are clear: AI has the ability to process vast amounts of data, recognize patterns quickly, and make informed decisions, helping organizations identify vulnerabilities and threats, minimize or eliminate threats, and respond more quickly.
Q: How can AI improve vulnerability testing?
A: AI-powered tools can scan through codebases to identify potential vulnerabilities by analyzing patterns to detect common risks such as SQL injections and cross-site scripting. AIs could also be trained on large datasets of known vulnerabilities to identify similar patterns in new code, thereby revealing previously unknown vulnerabilities or zero-day exploits.
Q: How can AI empower threat detection?
A: AI can analyze vast amounts of data and establish a baseline of normal behavior for systems, networks, and users. By detecting deviations or anomalies, AI can help identify potential security threats, such as unauthorized access attempts, unusual network traffic, or abnormal user behavior.
Q: How can AI accelerate threat containment and response?
A: AI algorithms can automatically assess the severity and impact of the threat, identify which assets are impacted, and even orchestrate response actions, including isolating infected endpoints, blocking malicious traffic, or turning off compromised services.
Q: Is AI a replacement for human expertise in cybersecurity?
A: No, AI is not a replacement for human expertise in cybersecurity. AI is only as good as the data it’s based on, trained on, and analyzing. Nothing can replace the human expertise and oversight that is always needed.
