Organizations across every sector, from higher education and healthcare to financial services and government, are rapidly embedding Artificial Intelligence (AI) into their core operations.
With that acceleration comes a double-edged reality; AI’s greatest strengths, speed, access, and autonomy, are the same traits that make it a high-value target for today’s attackers.
According to a March 2026 Gartner report, by 2028, at least 50% of all enterprise cybersecurity incident response efforts will be devoted to managing security issues connected to custom-built AI applications. Gartner VP Analyst, Christopher Mixter, stated, “AI is evolving quickly, yet many tools — especially custom-built AI applications — are being deployed before they’re fully tested. These systems are complex, dynamic, and difficult to secure over time. Most security teams still lack clear processes for handling AI-related incidents.”
That gap between AI adoption and AI security is exactly where attackers can exploit, and exactly where system testing and preparedness exercises can make the difference. In this article, we explore two of the most important tools organizations can use to close that gap: AI-focused Penetration Testing and AI-focused Tabletop Exercises.
The AI Attack Surface
Traditional penetration testing and tabletop scenarios were built around networks, endpoints, and applications. Today’s AI environment demands a larger and more advanced scope. When an organization integrates an AI chatbot, a code assistant, or a custom-built language model into its workflows, it introduces a new class of vulnerabilities, including ones that conventional security assessments are not designed to detect yet.
The most common AI attack vectors security teams must now account for include:
- Prompt Injection: Malicious instructions embedded in user prompt inputs designed to bypass safety guardrails, extract sensitive system data, or trigger unauthorized actions.
- Data Poisoning: Corrupting an AI model’s training data to introduce bias, degrade performance, or enable malicious outputs.
- Model Inversion and Exfiltration: Reverse-engineering a model to reconstruct sensitive training data or, in extreme cases, transfer full ownership of the model.
- AI Impersonation and Deepfakes: AI-generated audio and video used to impersonate executives or bypass authentication; this accounted for 35% of AI-powered breaches in 2025, according to IBM’s Data Breach Report.
- Shadow AI Exploitation: Targeting unsanctioned AI tools used by employees that bypass corporate security monitoring has quickly become one of the top three costly breach factors, according to IBM.
Each of these vectors requires a specialized method of action for testing and response planning. A plan that goes far beyond standard vulnerability scans or legacy tabletop scenarios.
AI Penetration Testing
AI penetration testing is a structured assessment designed to find and identify exploitable weaknesses across an organization’s AI systems, integrations, and data pipelines. This is done before a real attacker is able to find them. It mirrors how actual threat actors approach AI systems in the wild and produces actionable findings that security and development teams can work to correct.
What AI Penetration Testing Covers
- Prompt Injection and Jailbreak Testing: Simulating malicious inputs against AI interfaces to test whether safety controls and output filters can be bypassed.
- API and Integration Security: Testing third-party AI plugin connections and API endpoints for unauthorized access, data leakage, or the ability to interact with malicious external domains.
- AI Access Assessment: Evaluating whether proper access controls are enforced around the use of AI mode, a necessity, given that 97% of organizations that suffered an AI-related breach in 2025 lacked proper AI access controls.
- Shadow AI Discovery: Scanning the network for unsanctioned AI tools actively in use by employees, mapping the organization’s true AI footprint and its associated risk exposure.
The goal is not simply to identify vulnerabilities, but to understand how those vulnerabilities could be chained together by an attacker and what the real-world impact would be on the organization’s data, operations, and reputation.
AI Tabletop Exercises
Even the most well-designed technical controls can fail if the people responsible for responding to an AI-related incident do not know what to do. That is the critical point of AI-focused tabletop exercises. They work to test the human side of your security posture, such as decision-making, communication, and coordination.
Gartner’s 2026 research found that most security teams still lack defined processes for handling AI-related incidents, meaning that when an AI breach occurs, teams are often improvising in real time. Tabletop exercises change that.
What AI Tabletop Exercises Simulate
Effective AI tabletop scenarios are built around the real-world attack patterns that are most likely to impact your organization. Common scenario types include:
- Deepfake Executive Impersonation: A simulated scenario in which an attacker uses AI-generated audio or video to impersonate a senior leader, requesting an unauthorized financial transfer or system access. This gave an attacker access to UXLINK’s internal system and resulted in $11 million in damages.
- AI-Generated Phishing Campaign: A scenario simulating a hyper-personalized phishing campaign made using generative AI that targets employees across departments, testing detection, reporting, and escalation procedures.
- Shadow AI Data Exfiltration: An exercise in which a business is using an illegitimate AI tool that has been quietly forwarding internal data to an external server, testing discovery, containment, and policy enforcement workflows.
- Prompt Injection on a Production AI System: Simulating the discovery that an organization’s customer-facing AI chatbot has been manipulated via adversarial inputs to leak internal data, testing the coordination between security, IT, legal, and communications teams.
What These Exercises Reveal
- Do your incident response runbooks address current AI-specific attack types?
- Can your team identify the difference between a software defect and a security incident involving an AI model?
- Do communication and escalation chains hold up under an AI-specific breach scenario?
- Are your governance policies and AI inventory able to support an effective response?
Why Pen Testing and Tabletops Work Better Together
AI penetration testing and tabletop exercises are complementary, not mutually exclusive, activities. Pen testing reveals what is technically exploitable. Tabletop exercises reveal whether your team can respond effectively when an attack occurs. Organizations that use one without the other are leaving a critical gap in their AI security posture.
Together, they provide a complete picture of organizational readiness: where your AI systems are vulnerable, and whether your team and processes are equipped to handle it when those vulnerabilities are targeted.
Next Steps
You cannot defend against threats you have not tested. Whether your organization is in the early stages of AI adoption or deeply embedded in AI-powered workflows, the time to assess your readiness is now, not after an incident.
Organizations should start by asking these foundational questions:
- Do we have a complete inventory of every AI system and tool in use across our organization? Does this include tools used by individual departments without IT approval?
- Have our AI systems ever been tested by an independent adversarial assessment, not just scanned for traditional vulnerabilities?
- Does our incident response team have a documented, practiced playbook for AI-specific breaches, including deepfake impersonation, prompt injection, and shadow AI data loss?
If the answer to any of these questions is “no” or you’re unsure, your organization is likely among the majority that Gartner warns are not yet prepared for the AI incident response demands needed for 2028.
Final Thoughts
AI security is no longer a future concern; it is a current operational risk. As AI adoption accelerates, so does the depth of AI-targeting attacks. Organizations that treat AI security as an afterthought will find themselves managing costly, disruptive incidents with unprepared teams, plans that do not apply, and governance frameworks that never accounted for this new technology in use.
Proactive AI penetration testing and well-designed tabletop exercises change that situation. They transform AI security from a crisis-driven event into a structured one that builds the confidence, capability, and institutional knowledge your organization needs to stay ahead of an evolving threat landscape.
Ready to test your AI security readiness? CampusGuard and its security division, RedLens InfoSec, offer comprehensive AI-focused penetration testing and social engineering tests to ensure your adoption of AI doesn’t become your biggest liability.
Contact us to learn more about how we can help you strengthen your organization’s security posture.
