Harnessing Generative AI for Cybersecurity: A Comprehensive Guide

Generative AI, which can create new content such as text, audio, and video, is rapidly being adopted in enterprise settings. However, it has also raised concerns in the cybersecurity industry, particularly regarding regulatory compliance. Despite these concerns, proponents are quickly integrating this technology into new security tools.

In this guide, you’ll explore the advantages and disadvantages of generative AI in cybersecurity, how leading companies are leveraging this technology to enhance their security tools, and how you can use generative AI effectively while adhering to cybersecurity and ethical best practices.

How Generative AI Works

Generative AI leverages advanced algorithms and neural networks trained on vast datasets to produce content that mimics the original data’s form and structure.

These models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), learn to generate text, images, audio, and video that are increasingly indistinguishable from human-created content. The training process involves continuous feedback loops, where the model’s outputs are constantly evaluated and refined, enhancing their accuracy and realism over time.

Pros & Cons of Generative AI in Cybersecurity

Pros

• Enhanced threat detection: Simulates cyberattack scenarios, enabling more effective response.

• Automated security measures: Automates security tasks, and reduces the workload.

• Innovative problem-solving: Brings creative solutions to security challenges, identifying more vulnerabilities.

Cons

• Sophisticated phishing attacks: Hackers can create highly convincing phishing content.

• Data privacy concerns: Training requires access to vast amounts of data.

• Unpredictable behaviour: This can lead to unforeseen vulnerabilities, including harmful outputs.

Generative AI’s Top Cybersecurity Risks

Generative AI poses several potential security risks to businesses and consumers, particularly at the data level. Here are a few of the top security risks that arise when using generative AI:

Vulnerabilities During and After Model Training

Because generative AI models are trained on data that is collected from all kinds of sources — and not always in a transparent fashion — it is unknown exactly what data gets exposed to this additional attack surface.

Combined with the fact that these generative AI tools sometimes store data for extended periods and don’t always have the best security rules and safeguards in place, threat actors can access and manipulate training data at any stage of the training process. These threat actors can introduce backdoors or biases into the AI model, which can be exploited once the model is deployed.

Violation of Personal Data Privacy

There’s little to no structure in place to regulate what kinds of data users input into generative models. This means corporate users — and anyone else — can use sensitive or personal data without adhering to regulations or getting permission from the source. A particularly troubling scenario: the models might inadvertently learn and replicate private information in their outputs, which may lead to potential data breaches.

Again, with how these models are trained and how data is stored, personally identifiable information (PII) can easily get into the wrong hands and lead to anything from account takeover to credit card theft.

Exposure of Intellectual Property

Incorporating proprietary data into the training process of generative AI models can lead to unintended exposure of intellectual property (IP). This is particularly concerning when models are trained on codebases or documents containing confidential information, as the generated outputs might reveal insights into the IP.

Companies, such as Samsung, have already unintentionally exposed proprietary company data to generative models in harmful ways. This exposure most often occurs when employees upload company code to the system, exposing intellectual property, API keys, and other confidential information.

Cybersecurity Jailbreaks and Workarounds

Many online forums offer “jailbreaks,” or secret ways for users to teach generative models to work against their established rules. With these jailbreaks and other workarounds, security issues have arisen. Attackers can exploit these vulnerabilities to use these models for malicious purposes, such as generating deceptive content or automating cyberattacks.

For example, ChatGPT was recently able to trick a human into solving a CAPTCHA puzzle on its behalf. The ability to use generative AI tools to generate content in so many different, human-like ways has enabled sophisticated phishing and malware schemes that are more difficult to detect than traditional hacking attempts.

Cybersecurity Tips and Best Practices for Using Generative AI

Although the risks are high when using generative AI, many of those risks can be mitigated or entirely avoided if you follow cybersecurity best practices, including:

Closely Read Security Policies From Generative AI Vendors

After so much initial outcry about generative AI vendors’ lack of transparency in their model training and other processes, many major vendors have begun to offer extensive documentation that explains how their tools work and how user agreements work.

To best know what’s happening to your data inputs, look at your vendors’ policies on data handling, storage, deletion and deletion timeframes, and what information they use to train their models. It’s also a good idea to scour their documentation for mentions of traceability, log history, anonymization, and other features you may need for your specific regulatory compliance requirements.

Most importantly: Look for any mention of opt-ins and opt-outs and how you can choose to opt in or out of your data being used or stored.

Don’t Input Sensitive Data When Using Generative Models

The best way to protect your most sensitive data is to keep it out of generative models, especially ones with which you’re less familiar.

It’s often difficult to say how much of your data can or will be used to train future iterations of a generative model, not to mention how much of and how long your data will be stored in the vendor’s data logs.

Instead of blindly trusting whatever security protocols these vendors may or may not have in place, it’s a better idea to create synthetic data copies or avoid using these tools entirely when working with classified data. Instead, use generative AI to supplement your projects when working with less sensitive information.

Keep Your Generative AI Models Updated

Like any software, generative AI models and their environments must be kept up-to-date with the latest security patches and updates.

Generative models receive regular updates, and sometimes those updates include bug fixes and other security optimizations. Keep an eye out for opportunities to upgrade your tools so they stay at peak performance.

Train Employees on Appropriate Use

Generative AI tools are simple to use and misuse. It’s important your employees know what kinds of data they are allowed to use as inputs, what parts of their workflow can benefit from generative AI tools and regulatory compliance expectations. Additionally, any other best practices and procedures that they are expected to follow as members of the organization are essential.

It’s also helpful to train employees on basic cybersecurity awareness so they can help identify phishing attempts and other attack vectors before they go too far.

Use Data Governance and Security Tools

Several data governance and security tools can protect your entire attack surface, including any third-party generative AI tools you may be using.

Consider investing in data loss prevention, threat intelligence, cloud-native application protection platform (CNAPP), and/or extended detection and response (XDR) tools to stay ahead of the curve.

How Generative AI Can Support Cybersecurity Efforts

Generative AI can expose organizations to new attack vectors and security risks, but when these tools are used strategically, they can greatly support cybersecurity goals as well. Here are just a few ways generative AI tools can be used in cybersecurity:

• Scenario-driven cybersecurity training: Uses synthetic data and other features to generate simulated attacks, scenarios, and environments for cybersecurity training.

• Synthetic data generation: This can be used to securely generate anonymized data copies for AI and software app development. Synthetic data generation is rapidly becoming a key player in the security sector.

• Contextualized security monitoring, reporting, and recommendations: Helps security teams search existing code and networks for vulnerabilities and offers contextualized recommendations for remediation.

• Supply chain and third-party risk management: Supports risk management, predictive maintenance, fraud detection, relationship management, and other components of supply chain and partner cybersecurity management.

• Threat intelligence and hunting: Can assess massive amounts of data all at once, looking for security vulnerabilities and bigger issues. Some tools can also make recommendations about what tools you should use and infrastructure changes you should make for better security outcomes.

• Digital forensics and incident analysis: Can analyze the traces left by attackers to understand their tactics and entry points following a security incident to prevent future breaches by identifying and mitigating the exploited vulnerabilities.

• Automated patch management: Capable of automating the identification and application of necessary software patches across an organization’s digital infrastructure.

• Phishing detection and prevention: This can be used to detect subtle cues of phishing, such as unusual language patterns or malicious links, alerting users and preventing potential compromises.

13 Top Generative AI and Cybersecurity Solutions

• Google Cloud Security AI Workbench: Utilizes Google Cloud’s AI and ML capabilities to offer advanced threat detection and analysis, helping organizations proactively identify and mitigate cyber threats.

• Microsoft Security Copilot: Integrates with Microsoft’s security ecosystem, with insights from tools like Microsoft Sentinel, Microsoft Defender, and Microsoft Intune, and leverages AI to enhance threat intelligence, automate incident responses, and streamline security operations.

• CrowdStrike Charlotte AI: Lets users manage cybersecurity through natural language on the Falcon platform and is used to support threat hunting and detection and remediation efforts.

• Cisco Security Cloud: Incorporates generative AI into Cisco Security Cloud to improve threat detection and policy management and simplify security operations through advanced AI analytics.

• Airgap Networks ThreatGPT: Combines GPT-3 technology and graph databases with sophisticated network analysis to offer thorough threat detection and response, particularly effective in complex network environments.

• SentinelOne: Features generative AI and reinforcement learning capabilities to detect, halt, and autonomously remediate cyberattacks for enterprise users.

• Synthesis Humans: Specializes in creating diverse synthetic human models for various applications, including cybersecurity training and biometric authentication, which enhance the realism and effectiveness of security simulations.

• SecurityScorecard: Leverages OpenAI’s GPT-4 to offer detailed security ratings and assessments that allow organizations to understand their security posture through natural language queries and receive actionable insights.

• MOSTLY AI: Synthetic data generation tool that’s specifically designed to generate anonymized data that meets various security and compliance requirements.

• Sophos: Integrates generative AI policy enforcement within its Sophos Firewall, offering comprehensive control over the use of generative AI solutions, which allows for the blocking, acceleration, or monitoring of generative AI applications, ensuring that Sophos Firewall users can manage generative AI technologies.

• Cybereason: Incorporates generative AI and machine learning models to enhance its cybersecurity platform and provide accurate classification of malicious operations and malware.

• Cylance by BlackBerry: Introduced a generative AI-powered cybersecurity assistant for its Cylance AI customers, aiming to predict customer needs and proactively provide information.

• Trellix: Has cybersecurity innovations powered by generative AI that allows for custom AI training while ensuring data and results remain private, showcasing Trellix’s commitment to secure and privacy-conscious AI applications.

Bottom Line: Generative AI and Cybersecurity

Generative AI could be looked at as either a blessing or a curse for cybersecurity, depending on how businesses (and threat actors) choose to take advantage of the technology.

The most important initiative every business can launch is to accept generative AI’s growing presence, learn how the technology works, and establish rules and best practices for how to use generative AI technology in security settings. From there, more adventurous companies should consider investing in one of the many emerging AI tools that leverage generative AI models to streamline and simplify cybersecurity efforts.

Conclusion

Generative AI is a double-edged sword in the realm of cybersecurity. While it introduces new vulnerabilities and risks, it also offers innovative solutions for threat detection, automated security measures, and advanced problem-solving. By understanding the technology, adhering to best practices, and leveraging cutting-edge tools, businesses can harness the power of generative AI to bolster their cybersecurity defenses. Embracing this technology with a strategic and informed approach will enable organizations to stay ahead of cyber threats and protect their valuable data and assets.