A red team engagement’s value is not measured by the number of vulnerabilities found, but by the tangible security improvements it inspires. The recommendations section is where you translate your findings into a strategic roadmap for resilience. Moving beyond a simple list of fixes, your goal is to provide clear, actionable, and prioritized guidance that empowers the blue team to build a more secure AI system.
The Anatomy of a High-Impact Recommendation
An effective recommendation is more than just a suggestion; it’s a complete, self-contained unit of work. Each one should be precise enough for a developer to understand and compelling enough for a manager to prioritize. Structure your recommendations around these five core components:
- Finding Reference: Explicitly link back to the specific finding(s) in the report (e.g., “In response to F-07: Indirect Prompt Injection via Document Analysis…”). This maintains a clear audit trail.
- Problem Statement: A one-sentence summary of the risk. This re-establishes context without requiring the reader to re-read the entire finding.
- Actionable Guidance: The core of the recommendation. Use imperative verbs (e.g., “Implement,” “Configure,” “Develop,” “Restrict”). Be specific about the technology, method, or process to be used. Avoid ambiguity.
- Rationale & Impact: Explain *why* this action is necessary. Describe the security benefit it provides and which attack vectors it mitigates. This justification is crucial for securing buy-in and resources.
- Prioritization Score: A composite score based on risk severity, implementation effort, and strategic importance. A simple “Priority: High / Medium / Low” and “Effort: High / Medium / Low” scale is often sufficient.
The R.I.S.E. Framework: A Structured Approach
To ensure comprehensive coverage, you can categorize your recommendations using a framework that addresses different layers of defense. The R.I.S.E. framework helps structure your thinking and provides a balanced set of remediations.
R — Remediate
These are direct fixes for specific, identified vulnerabilities. They are tactical, targeted, and often the most urgent actions to take.
# Pseudocode for a remediation recommendation FUNCTION sanitize_prompt(user_input): # Finding: F-11 (Model susceptible to instruction hijacking) # Action: Implement a denylist for meta-instructions. denylist = ["ignore previous instructions", "act as", "translate and then"] FOR instruction IN denylist: IF instruction IN user_input.lower(): RETURN "Potentially malicious prompt detected." RETURN user_input
I — Isolate
These recommendations focus on architectural changes to contain and limit the blast radius of a successful attack. Isolation assumes that a component might be compromised and seeks to prevent lateral movement or privilege escalation.
Example: “Run the model inference service in a minimal, network-isolated container with read-only access to model weights. The container should have no access to internal networks or sensitive data stores, communicating only via a strictly-defined API gateway.”
S — Strengthen
This category includes proactive hardening and defense-in-depth measures. These recommendations aren’t tied to a single vulnerability but improve the overall security posture of the AI system, making future attacks more difficult.
Example: “Introduce adversarial training into the model’s retraining pipeline. Use a mix of FGSM and PGD attacks on a 5% sample of the training data for each epoch to improve the model’s intrinsic robustness against evasion attacks.”
E — Educate
These are procedural, policy, or training-based recommendations. Technical controls are essential, but human processes are a critical part of the security lifecycle. These actions address systemic gaps in awareness, policy, or procedure.
Example: “Develop and mandate a Secure AI Development Lifecycle (SAIDL) policy. The policy must require threat modeling sessions for any new feature that alters model input processing or data handling, with participation from both the data science and application security teams.”
Practical Examples: Weak vs. Strong Recommendations
The difference between a vague suggestion and an actionable recommendation is clarity and detail. The following table illustrates how to transform a weak recommendation into a powerful one.
| Finding | Weak Recommendation | Strong, Actionable Recommendation |
|---|---|---|
| The model can be manipulated to leak its system prompt. | “You should fix the system prompt leakage.” | Recommendation R-01: Implement an output filter that canonicalizes the model’s response and scans for high-similarity matches against the system prompt’s core instructions. If a match exceeds a 90% Jaccard similarity threshold, replace the response with a generic refusal. Priority: High. Effort: Medium. |
| Training data can be poisoned with mislabeled examples. | “Clean the training data.” | Recommendation S-04: Establish a data integrity verification pipeline for all incoming training data. Implement outlier detection using an Isolation Forest algorithm to automatically flag and quarantine data points that deviate significantly from their cluster’s centroid before they are used for training. Priority: High. Effort: High. |
| The API endpoint for the model has no rate limiting. | “Add rate limiting.” | Recommendation I-03: Configure the API gateway to enforce strict rate limiting on the model inference endpoint. Set a limit of 10 requests per minute per IP address and 100 requests per minute per authenticated API key. Implement an exponential backoff for clients exceeding these limits. Priority: Medium. Effort: Low. |
Visualizing the Formulation Process
Thinking about the recommendation process as a structured workflow helps ensure that no steps are missed. It begins with a validated finding and ends with a prioritized, assigned action.
Finalizing and Assigning
Your work is not done once the recommendation is written. The final step is to ensure it can be tracked and implemented. When possible, categorize recommendations by the team responsible for implementation (e.g., Data Science, MLOps, Application Security, Platform Engineering). This facilitates communication and assigns clear ownership.
Ultimately, your recommendations are a collaborative tool. They should be written to foster a partnership with the blue team, providing them with the intelligence and guidance they need to not only fix current issues but also to build more secure and resilient AI systems for the future.