After identifying a vulnerability, your next critical question is: “So what?” An Impact Analysis Framework provides the structured answer. It moves beyond a purely technical description of a flaw to quantify its potential consequences across the organization and beyond. This framework is the tool you use to translate a red team finding into a business-relevant risk, forming the crucial ‘Impact’ dimension of your risk matrix.
The Three Dimensions of AI System Impact
A comprehensive impact analysis for an AI system must consider three distinct but interconnected dimensions. A failure in one area often cascades into others. Assessing each one ensures you capture the full scope of potential harm.
1. Technical Impact
This dimension focuses on the direct effects on the AI system and its surrounding infrastructure. It answers: “How is the system’s functionality or integrity compromised?”
- Model Performance: Degradation of accuracy, precision, recall, or other key metrics. Evasion attacks fall here.
- System Availability: The system becomes unresponsive or unusable (Denial of Service).
- Data & Model Integrity: Unauthorized modification of training data, model weights, or system outputs. This includes data poisoning and backdoor attacks.
- Confidentiality: Unauthorized access to or extraction of sensitive training data, model architecture, or intellectual property.
2. Business & Operational Impact
Here, you translate technical failures into business consequences. It answers: “How does this affect our organization’s ability to operate and succeed?”
- Financial Loss: Direct theft, costs of remediation, lost revenue due to system downtime, or fraudulent transactions enabled by the AI’s compromise.
- Reputational Damage: Loss of customer trust, negative media attention, and damage to the brand. This is often the most significant long-term impact.
- Regulatory & Compliance Failure: Fines or legal action resulting from violating laws like GDPR, HIPAA, or industry-specific regulations.
- Operational Disruption: Interruption of critical business processes that rely on the AI system’s outputs.
3. User & Societal Impact
This dimension considers the external effects on individuals and society at large. It answers: “Who is harmed by this failure, and how?”
- Harm to Individuals: Physical, psychological, or financial harm to users. Examples include biased loan decisions, incorrect medical diagnoses, or unsafe autonomous vehicle behavior.
- Erosion of Public Trust: A high-profile failure can damage public trust not just in your product, but in AI technology as a whole.
- Ethical Violations: Use of the system for manipulation, perpetuating systemic bias, or violating privacy on a large scale.
- Safety & Security Risks: Compromise of systems controlling physical infrastructure (e.g., power grids, traffic control).
Impact Scoring Matrix
To standardize the analysis, use a scoring system. The following table provides definitions for a 1-5 scale, where 1 is minimal impact and 5 is catastrophic. You should score each of the three dimensions separately before aggregating.
| Score | Level | Impact Description (Examples) |
|---|---|---|
| 1 | Insignificant | Minor performance degradation, negligible financial loss (< $1k), no noticeable reputational or user harm. Easily contained. |
| 2 | Minor | Noticeable but non-critical performance issues. Minor operational disruption. Low financial loss. Contained reputational harm. Reversible harm to a small number of users. |
| 3 | Moderate | Significant system impairment but not total failure. Moderate financial loss. Negative local media attention. Non-compliance with internal policies. Reversible harm to a group of users. |
| 4 | Major | System rendered inoperable or gives dangerously incorrect outputs. Substantial financial loss. National media attention and brand damage. Violation of regulations with likely fines. Significant, potentially irreversible harm to users. |
| 5 | Catastrophic | Total system compromise leading to cascading failures. Severe financial loss threatening business viability. International scandal. Major regulatory action. Widespread, irreversible harm or risk to life. |
The Analysis Process in Practice
Follow these steps to apply the framework consistently:
- Describe the Scenario: Clearly articulate the vulnerability and a plausible attack scenario based on the red team’s findings.
- Score Each Dimension: Using the matrix above, assign a score (1-5) to the Technical, Business, and User/Societal impacts. Be objective and base scores on evidence.
- Justify Each Score: For each score, write a brief but clear rationale. Why is the business impact a 4? What specific regulation would be violated? This documentation is vital for decision-making.
- Determine the Final Impact Score: The final score for the risk matrix is typically the highest score from any of the three dimensions. A catastrophic user impact (5) should not be averaged down by a moderate technical impact (3). The risk is defined by its worst possible outcome.
Example Application: Evasion of a Content Moderation AI
Let’s apply this to a finding where an attacker can use special characters to bypass a moderation model designed to block hate speech.
| Dimension | Score | Justification |
|---|---|---|
| Technical | 3 | The core model function (classification) is defeated for a specific class of inputs, but the system remains available and other functions are intact. It’s a significant impairment, not a total failure. |
| Business | 4 | Significant reputational damage is highly likely as harmful content proliferates. Advertisers may pull support, leading to major financial loss. Potential for regulatory scrutiny under online safety acts. |
| User & Societal | 4 | Widespread exposure of users, including minors, to harmful content can cause psychological distress. The platform becomes a vehicle for hate speech, causing significant harm to targeted groups and eroding public trust. |
| Final Aggregated Impact Score: 4 (Major) | ||
In this example, the final impact score of 4 is taken to the risk matrix. This high impact rating, even if the likelihood is assessed as moderate, will flag the vulnerability for urgent attention and resource allocation in the mitigation plan.