With the rapid advancement of generative artificial intelligence (GenAI), new opportunities and threats to cybersecurity have emerged in a relatively short period, and its capabilities can only expand.
In this article, we will examine the dual role of generative AI in cybersecurity. On one hand, AI can be used by attackers to target organizations and individuals. On the other hand, cybersecurity experts can harness AI to protect against these threats and develop strategies to prevent future attacks.
The Offensive: How Cyberattackers Are Using GenAI
We can now adapt the same principle at the heart of GPT-4 to automate cyberattacks with more nuance and unprecedented speed and scale thanks to generative AI. Here are four ways that cyberattackers are initiating attacks using the new toolboxes that generative AI has made accessible.
Enhanced Social Engineering
Generative AI offers a way for cybercriminals to construct ultra-lifelike phishing messages and emails. It can also deliver deepfake audio or video that mimics the style, tone or in-person identity of someone the target knows, trusts and respects. Even our highest-level “trusted insiders” could find it difficult to distinguish a synthetic communication from something genuine.
Automated Malware Creation
AI models can generate new malware variants or modify existing ones, potentially evading traditional signature-based detection methods. This capability allows attackers to produce a higher volume of malicious code with greater efficiency.
Intelligent Password Cracking
Generative AI can analyze patterns in known password databases to create more effective password-guessing algorithms, potentially compromising user accounts more quickly than traditional brute-force methods.
Sophisticated Reconnaissance
Machine-learning tools can scan the entire deep and dark web and other publicly available data to identify potentially profitable targets and exploitable vulnerabilities, speeding the burglar’s reconnaissance.
The Defensive: Applications Of GenAI For Cybersecurity
While generative AI gives hackers new weapons to exploit, it also gives defenses new weapons to mount.
Advanced Threat Detection
Systems built solely around rules cannot detect new and previously unrecognized anomalies, unlike the synthesis of network traffic patterns, user behavior and system logs. This allows security operation systems to identify and respond to new threats much faster.
Automated Incident Response
Generative AI can assist security teams in creating and executing incident response plans—providing steps to take in the event of an attack—that can enable a rapid and strong response. AI response systems can also take on some of the manual work that consumes security analysts’ time.
Vulnerability Assessment And Patching
With these AI models, you can perform static analysis and code review, where you look at the code or system configuration and identify vulnerabilities. You can even have AI models that generate the patches or suggest them. An organization can look at these security issues proactively before they are actually exploitable.
Enhanced Security Training
Generative AI can create realistic simulations of cyberattacks for training purposes, helping security professionals and employees develop better skills in identifying and responding to threats.
Protecting Against AI-Powered Threats
As generative AI begins to find its place in both offensive and defensive cybersecurity operations, defenders will have to come up with ways to finish off generative algorithms that are already being used against them.
Implementing AI-Powered Security Solutions
By utilizing AI-enhanced security tools and infrastructure, we can establish an AI defense that can identify and react to attacks instantly, preventing any harm from occurring.
Enhancing Employee Training
Regularly update all cybersecurity awareness training to include AI-generated threats and teach employees to scrutinize digital communications and question requests via secondary cables.
Adopting A Zero-Trust Architecture
Implement a zero-trust security model that assumes no user or system is inherently trustworthy, requiring continuous authentication and authorization for all network access.
Investing In Robust Authentication Methods
Create stronger authentication methods, such as multifactor authentication (MFA) and biometric or behavioral authentication types, which are harder to imitate with AI.
Monitoring AI Model Usage
Create guidelines for the responsible use of AI models. Implement controls that restrict the unintentional exposure of sensitive data to third-party AI systems.
Staying Informed And Collaborating
Stay aware of new AI and cybersecurity developments. To stay ahead of the game, join awareness-raising groups and exchange information with your peers and competitors.
The Ongoing Arms Race
Cybersecurity will be part of an endless cat-and-mouse game between the attackers and the defenders. The more advanced the attackers’ generative AI becomes, the more advanced the technology defenses have to get. However, I view this ongoing back-and-forth of research, development and investment as hugely positive, as it puts extra impetus on the industry to come up with new and unique generative AI approaches for cybersecurity.
While generative AI presents significant challenges, it also provides organizations with tools to enhance their security architecture. If businesses apply AI defense technologies and address their approach to security comprehensively, they can better defend themselves against the growing menace of attacks by AI-equipped attackers. At the same time, we should approach its entry into this space with a combination of trepidation and hope, as generative AI within cybersecurity is expected to emerge from both dark and light directions.
Chirag Shah is Global Information Security Officer & DPO of Model N, Inc. Read Chirag Shah’s full executive profile here.
This article was originally published on Forbes.
