Confronting the Dark Side of GenAI: Recommendations for business leaders, CISOs and security teams

As the threat landscape evolves with the emergence of malicious generative AI tools like Evil-GPT, WolfGPT, DarkBard and PoisonGPT, organizations must adopt a multi-pronged approach to confront AI-enabled threats. Here are strategic recommendations for security leaders to consider:

1. Threat intelligence and monitoring

Staying informed about the latest malicious AI tools and tactics is crucial. Subscribe to threat intelligence feeds or services that track dark web chatter about AI, such as those offered by Kela and Flashpoint. Given the 219% increase in discussions about these tools in 2024, visibility into this underground trend is essential. Security teams should monitor for keywords related to these tools (e.g., “GPT” variants, “jailbreak prompts”) in threat intelligence platforms. Additionally, implement dark web monitoring for your organization’s mentions — if criminals are customizing AI to target your company or industry, you want to know early.

2. Enhance email and content security

Since phishing is a primary use case for malicious AI, it’s vital to fortify your email security stack. Upgrade to advanced filters that use AI and machine learning to detect phishing attempts, as legacy rule-based systems may miss AI-crafted messages. Some solutions now specifically claim to detect AI-generated text by analyzing linguistic patterns. While not foolproof, these measures add an important layer of defense. Encourage (or even script) your secure email gateways to flag emails with suspiciously well-crafted content or context that doesn’t match historical norms for the sender. Training your existing spam filters on known AI-generated phishing emails can also improve detection. Finally, consider deploying AI-assisted anti-phishing tools that can do real-time content analysis and URL scanning, as AI phishing often comes with novel URLs or slight impersonations that automated tools can catch faster than busy employees.

3. Robust employee training — Emphasize content over form

Update your security awareness training to reflect the new realism of AI scams. Employees should be shown examples of phishing emails that are grammatically perfect and contextually relevant, so they don’t rely on outdated cues. Emphasize the importance of verifying the legitimacy of requests through secondary channels. Fo example, if an “executive” emails an urgent request for a transfer of funds, verify by phone. Teach staff to be vigilant for subtle signs of automation, such as unusual phrasing or inconsistencies that might slip through an AI (like an odd date format or irrelevant detail). Include scenarios with deepfake voices or videos in executive risk training to prepare employees for potential threats. For instance, have a drill where a deepfake “CEO” leaves a voicemail with instructions, and test if procedures are followed. The goal is to inoculate the organization against trusting communications just because they sound professional.

4. Model and API security

As organizations integrate AI into their operations, it’s essential to implement controls to prevent model misuse and poisoning. If your company uses AI models (chatbots, assistants, etc.), establish strict access control and monitoring on those systems to detect abuse, such as suspiciously structured requests that may indicate prompt injection or data exfiltration attempts. Validate the source and integrity of any third-party AI models or datasets you use. For example, use checksums or signatures for models, and prefer models from official repositories. Consider adopting emerging tools for model provenance (like the AICert initiative or other AI supply chain security frameworks) to verify that a model hasn’t been tampered with. Internally, implement rate-limiting and anomaly detection for your AI APIs to catch unusual activity. If an account suddenly starts making thousands of queries that produce data (which could indicate an attacker repurposing your AI), you want to catch that. Essentially, treat your AI services with the same security mindset as you would a critical database or server, because attackers might target them, either to abuse them or poison them.

5. Technical controls for malware and bots

Strengthen your endpoint and network defenses to handle AI-generated malware. Use endpoint detection and response (EDR) solutions that focus on behavior, allowing for the detection of processes that attempt to access user data in suspicious ways, such as detecting if a process tries to access user data and zip it. EDR can catch the behavior even if the code signature is new. Leverage threat intelligence to quickly update indicators of compromise (IoCs) when new AI-generated malware strains are discovered. On the network side, employ anomaly detection to identify patterns indicative of AI-generated attacks. Many AI-generated attacks might still exhibit machine-like patterns at scale, such as a sudden burst of phishing emails that are each slightly different or an unusual pattern of outbound connections if malware is exfiltrating data. Also consider AI-powered security tools that learn your baseline network behavior and alert on deviations. And of course, keep up with patching and basic cyber hygiene. AI-assisted attackers will still prey on unpatched systems and weak credentials as low-hanging fruit.

6. Incident response readiness

Update your incident response plans to account for AI elements. Develop playbooks for responding to deepfake or disinformation incidents, ensuring that your team knows who assesses the veracity of fake content, such as a fake video of your CEO, and how to inform stakeholders rapidly. For phishing incidents, be prepared that if one employee is compromised via an AI phishing email, the next phishing wave could be different because AI can mutate content. Ensure your incident response team has access to resources for analyzing suspicious content, like an AI text detection tool or relationships with AI experts. Sharing information is key. If you suffer an attack involving a malicious AI tool, consider sharing anonymized intelligence with industry ISACs or certs to enhance collective defenses. The faster the community learns about a new tactic (e.g., “This phishing campaign appears to have been written by WormGPT with certain stylistic markers”), the faster collective defenses can adjust.

7. Policy and vendor management

From a governance perspective, implement clear policies regarding AI usage within your organization. Address the risks associated with “shadow AI,” where employees use unapproved AI tools. Shadow AI can introduce risk, as highlighted by recent cases of data leakage and even malicious tools masquerading as legit AI apps. Clearly communicate which AI tools are approved, and prohibit the use of unsanctioned AI, especially with sensitive data. Require vendors and third parties to adhere to AI security practices, ensuring they safeguard their AI tools against misuse. For instance, if you use a vendor’s AI chatbot in your customer support, ask how they safeguard it against misuse and whether they vet their models for tampering. Misinformation threats should also be folded into business continuity or crisis management planning. This might involve PR teams, but the security team’s input is vital on the technical attribution and takedown aspects (like coordinating with platforms to remove deepfake content, etc.).

8. Embrace defensive AI

Finally, consider leveraging AI for defense. Just as attackers use AI, defenders can employ AI and machine learning to enhance threat hunting, user behavior analytics, and automated response. Many security operations centers are overwhelmed by alerts — AI can help correlate signals indicating an AI-generated attack is underway, allowing for quicker identification and response. For example, multiple low-confidence phishing alerts that share subtle similarities could be pieced together by an AI to reveal a widespread campaign. AI can also assist in digital risk protection — scanning the web for fraudulent content such as spoofed websites or fake news about your company. Some advanced systems use natural language processing (NLP) to monitor social media or the dark web for early signs of targeted misinformation or phishing themes. By harnessing these tools, organizations can flip the script and make AI a strength in their security posture rather than just a threat.

Conclusion

The rise of malicious generative AI tools marks a new chapter in cybersecurity, empowering threat actors to launch more frequent, sophisticated and deceptive attacks than ever before. For CISOs and security teams, the imperative is clear: adapt quickly. By understanding these tools, hardening defenses and fostering a culture of vigilance — augmented by defensive AI — organizations can mitigate risks. The threat landscape is evolving, but with informed strategies and preparation, defenses can evolve as well. In this AI-fueled arms race, knowledge and agility will be your greatest assets. Stay informed, stay prepared and approach every suspicious email or odd model output with the healthy skepticism that this new era demands.