A single prompt injection in a customer-facing chatbot can leak sensitive data, damage trust, and draw regulatory scrutiny in hours. The technical breach is only the first step. The real risk comes from how quickly one weakness in an AI system can trigger a chain of business, legal, and societal impacts. Researchers at KDDI Research have developed the AI Security Map to connect those dots, showing how technical failures lead to harm that reaches far beyond the system itself.
Where current thinking falls short
Most AI security discussions focus on one slice of the problem. Researchers often study specific attack types such as poisoning, backdoors, or prompt injection. Others focus on individual AI qualities like fairness, privacy, or explainability. This leaves a gap in understanding how technical weaknesses connect to real-world impacts.
For example, a poisoning attack on a model may lower accuracy. This could produce misleading results for users, which might then cause financial loss or safety risks. The connection between the original attack and the eventual harm is often left unexplored in technical discussions.
Two sides of the map
The AI Security Map divides AI security into two linked parts.
The first is the Information System Aspect (ISA). This covers the elements AI must meet to be secure within a system. It includes the traditional security trio of confidentiality, integrity, and availability. It also adds AI-specific needs such as explainability, fairness, safety, accuracy, controllability, and trustworthiness.
The second is the External Influence Aspect (EIA). This focuses on impacts to people, organizations, and society when AI is attacked or misused. These impacts can include privacy breaches, misinformation, economic harm, threats to critical infrastructure, and violations of laws.
The model links each ISA element to potential EIA outcomes. If integrity is breached, it could lead to unfair outputs, safety risks, or loss of trust. A confidentiality breach could trigger privacy violations, reputational harm, or legal issues.
Direct and indirect chains of harm
The researchers found that impacts can spread in two ways. Some are direct. A breach of confidentiality can immediately lead to a privacy violation. Others are indirect. For example, a prompt injection attack might first undermine controllability. That could allow an attacker to generate disinformation. If that content spreads, it could influence decisions by people who never used the AI system.
This matters because AI misuse can cause harm even when the core system is working as intended. Attackers can exploit features such as high accuracy or wide availability to automate cyberattacks or produce convincing false content.
Kat Traxler, Principal Security Researcher at Vectra AI, told Help Net Security that this challenge goes beyond individual organizations. “The AI Security Map correctly highlights that misuse can cause harm even when AI systems function as intended. Organizations must recognize that biases and vulnerabilities can be exploited even in properly functioning systems. The whole industry is grappling with what is essentially an intractable problem around explainability and fairness. At this point, it’s simply too complex for the average Fortune 500 company to solve independently,” she says. Her advice: avoid building bespoke large models.
“Leverage commercially built models like Gemini, ChatGPT, or Claude. By doing so, you shift a significant portion of the responsibility for explainability and fairness to the larger players who are better positioned to contribute to the collective, industry-wide effort needed for progress.”
What this means for CISOs
The AI Security Map highlights some important points for leaders.
First, integrity is the most influential element in the ISA. Once it is compromised, many other elements are at risk. Protecting integrity is difficult but it reduces the chance of large-scale harm.
Second, confidentiality is often the first target in attacks. This means that privacy-focused controls such as access limits, encryption, and differential privacy remain essential in AI environments.
Third, the model can guide security planning beyond technical countermeasures. It can help in risk mapping, tabletop exercises, and incident communication. Showing how a technical failure could lead to business disruption or legal exposure can make the case for investment in defenses.
How to use the map
CISOs can apply the AI Security Map in several ways:
- Map known vulnerabilities in AI systems to possible stakeholder impacts.
- Use it in vendor assessments to see if AI service providers have covered both ISA and EIA risks.
- Run scenario planning that explores both direct and indirect impact chains.
- Use it as a communication tool for boards and executives who need to understand how AI risks translate into organizational risks.
Melissa Ruzzi, Director of AI at AppOmni, says the framework can be strengthened with careful mapping of both users and data. “The first step to include both technical and societal impacts of AI security in a risk assessment is to map the AI functionality. Map the users – for example, internal employees, business customers, or public end users. Then, it is important to map which type of domain the AI answer will be involved in, such as medical suggestions, weather predictions or cybersecurity analysis. A combination of these two aspects will guide the social impact aspect,” she explains.
She adds that mapping data flow is equally important. “Understand where the data is coming from, how it is being treated, and what other data is being aggregated to it. This will include mapping the ETL pipeline, the data flow itself, and the MLOps involved, as monitoring and observability will also be part of the flow and may impact how the AI may function overall.” For CISOs, this provides a way to expand traditional risk assessments to include AI-specific risks that stretch beyond the purely technical.
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