Defense in Depth: Another Angle to Securing AI Systems Against Exploits

On March 10, an unsuspecting financial analyst at a major U.S. bank turned to ChatGPT for help drafting a regulatory compliance summary. Within seconds, the AI’s response included a hyperlink—seemingly relevant but actually a gateway to a credential-stealing phishing site. Before security teams caught on, multiple employees had entered login details, handing attackers an open door to the institution’s systems.

The attack exploited CVE-2024-27564, an SSRF vulnerability in OpenAI’s ChatGPT infrastructure, allowing cybercriminals to manipulate AI interactions and redirect users to malicious domains. This incident drives home an important reality: AI-driven systems aren’t immune to classic cyber threats. To stay ahead of attackers, organizations need to take a Defense in Depth approach—layering security controls to keep risks in check.

Beyond Compliance: A Strategy for Resilience

For decades (no, really—too many to count), I’ve lived by the mantra: “Do the right things, and compliance will come.” It’s more than just a catchy phrase—it’s a guiding principle. Instead of focusing on checkbox security measures, organizations should focus on multi-layered defenses that shrink their attack surface. No single control is a silver bullet; instead, overlapping security layers help ensure that if one fails, another picks up the slack.

Taking inspiration from the Bowtie threat matrix, let’s walk through the essential security layers for mitigating this kind of AI-focused attack.

Layer 1: The Moat & Drawbridge (Firewalls, WAFs, and IPS/IDS)

Your first line of defense is perimeter security—keeping the bad actors at arm’s length. A well-configured Web Application Firewall (WAF) can detect and block SSRF exploit attempts before they reach their target. Intrusion Prevention Systems (IPS) and Intrusion Detection Systems (IDS) provide another layer of defense by monitoring for suspicious outbound traffic patterns that could indicate unauthorized AI activity.

Key Actions:

• Keep WAF rules up to date to recognize and block SSRF payloads.
• Tune IPS/IDS configurations to detect AI-driven anomalies before they become incidents.
• Lock down outbound AI service requests to a controlled set of approved domains.

Layer 2: The Inner Walls (Zero Trust and Network Segmentation/Micro-segmentation)

Not everyone who looks like an ally is actually on your side. A Zero Trust model treats even legitimate AI-driven applications with skepticism. Network segmentation helps contain threats by limiting lateral movement, and micro-segmentation takes it a step further, restricting reconnaissance within critical zones.

Key Actions:

• Apply least privilege access to AI applications and restrict API calls to only what’s necessary.
• Isolate AI services from critical systems using segmented network structures.
• Strengthen authentication using OAuth, API keys, and mutual TLS to validate AI-based communications.

Layer 3: The Guards & Watchtowers (AI Behavior Monitoring and Anomaly Detection)

Traditional security tools don’t always grasp AI-driven threats. That’s where AI-powered anomaly detection steps in—spotting deviations from expected usage patterns before they become full-blown incidents.

Key Actions:

• Use AI-driven security analytics (e.g., Darktrace, Vectra AI, or ExtraHop) to monitor ChatGPT interactions.
• Define behavioral baselines for AI activity and set alerts for deviations.
• Watch for unusual redirection patterns that could indicate exploit attempts.

Layer 4: The Intelligence Network (SIEM and Threat Intelligence Integration)

A good Security Information and Event Management (SIEM) platform does more than log events—it correlates attack data, identifies patterns, and helps security teams act fast when an incident unfolds.

Key Actions:

• Feed AI-generated logs into SIEM solutions (e.g., Secureonix, Splunk, ELK, QRadar) for real-time security event analysis.
• Stay ahead of threats with intelligence feeds on active exploitation campaigns.
• Use Open-Source Intelligence (OSINT) tools to track attacker infrastructure and mitigate risks.

Layer 5: The Secret Escape Routes (Endpoint and User Awareness Security)

No security strategy is complete without human awareness and endpoint protections. Attackers often rely on human error—so training and proactive security tools are critical.

Key Actions:

• Educate employees on AI-related social engineering tactics and phishing scams.
• Deploy Endpoint Detection and Response (EDR) solutions to catch and block malicious activity.
• Create an easy process for employees to report suspicious AI behavior before it becomes a bigger issue.

What Arrows Are in Your Quiver?

Vulnerabilities like CVE-2024-27564 aren’t one-offs—they’re part of a larger shift in the attack landscape as AI adoption skyrockets. A Defense in Depth approach ensures organizations aren’t banking on just one security measure but instead layering multiple reinforcements to safeguard against evolving threats. By rolling out firewalls, network segmentation, AI anomaly detection, SIEM, and endpoint security, organizations can take control of their AI security rather than just reacting to the next exploit.

You Have to Ask Yourself...

How strong is your AI security? If this exploit had been aimed at your company, how quickly would you have caught it? Would your security layers have held up, or would an attacker have slipped through?

Now’s the time to assess your defenses and shore up your AI security—before you become the next case study.