What’s the best requirements pack for secure LLM feature design?

If by “requirements pack” you mean a reusable set of security requirements that product/engineering teams can apply when designing an LLM feature, my pick is:

Best overall: OWASP LLMSVS + OWASP GenAI Top 10 + NIST AI RMF

The strongest practical combination I found is:

1. OWASP Large Language Model Security Verification Standard (LLMSVS) v2.0 — the requirements backbone.
   This is explicitly designed as a set of concrete security requirements/tests for architects, developers, testers and security teams. Its coverage includes prompt/input security, model memory, LLM integration, agents/plugins, dependencies, and monitoring.

2. OWASP GenAI LLM Top 10 2026 — the threat/risk checklist.
   Use it to make sure the feature design addresses the current LLM-specific attack surface. The 2026 edition was published August 4, 2026, so I'd use this rather than older Top-10 versions.

3. NIST AI RMF: Generative AI Profile — the governance/risk layer.
   It gives you the broader lifecycle structure—risk identification, measurement, management and trustworthy-AI considerations—rather than just application vulnerabilities.

4. NIST SP 800-218A (SSDF for GenAI) — the SDLC layer.
   Add this when the requirement pack needs to govern development, supply chain, model/data handling, testing and release processes, not merely the feature's runtime architecture.

What I'd actually put in the pack

I'd turn those sources into a one-page feature security requirements template with these sections:

That structure is particularly important because modern guidance treats prompts, retrieved content, memory and tool results as potentially untrusted, rather than assuming the model will distinguish trusted instructions from hostile content.

Bottom line: if you want one artifact to hand engineers, start with LLMSVS v2.0. If you want a genuinely enterprise-grade requirements pack, make LLMSVS the control/requirement catalog, OWASP GenAI Top 10 the threat taxonomy, and NIST AI RMF + SP 800-218A the governance/SDLC framework.

OWASP LLM Applications Cybersecurity & Governance Checklist is also worth adding for the product/security/legal review layer. 

When designing secure Large Language Model (LLM) features, relying on traditional web application checklists leaves critical gaps (like prompt injection, excessive agency, or data poisoning).

The gold standard "requirements pack" is a composite framework built by mapping the OWASP Top 10 for LLMs and the OWASP LLM Security Verification Standard (LLMSVS) to the lifecycle controls of the NIST AI Risk Management Framework (AI RMF).

An actionable, enterprise-ready requirements pack for secure LLM feature design is organized into five core pillars:

1. Input & Prompt Surface Controls (Mitigating OWASP LLM01)

• Structured Prompt Boundaries: Enforce strict separation between system instructions and user-provided text using delimiters or structural API roles rather than raw concatenated strings.
• Schema & Length Validation: Parameterize and validate all input sizes, types, and character sets before they touch the model's context window.
• Semantic Input Sanitization: Deploy pre-processing guardrails (such as open-source toolkits like LLM Guard ) to intercept indirect/direct prompt injection attempts.

2. Data Governance & Context Hygiene (Mitigating OWASP LLM02, LLM06)

• PII & Secret Redaction: Implement dynamic masking to strip sensitive business data, credentials, and PII before data enters the prompt context or embedding pipeline.
• Granular Vector Access Controls: If using Retrieval-Augmented Generation (RAG), enforce per-tenant and per-user permission filters directly on the vector database to prevent cross-tenant data leakage.
• Data Provenance Tracking: Maintain a Machine Learning Bill of Materials (ML-BOM) using standards like OWASP CycloneDX to track dataset lineage and screen for training data poisoning.

3. Execution & Agency Containment (Mitigating OWASP LLM07, LLM08)

• Principle of Least Privilege for Tools: Constrain downstream plugins, APIs, and model-driven tool calls (like Model Context Protocol implementations) to explicit allow-lists.
• Human-in-the-Loop Triggers: Mandate explicit user confirmation gates before any agentic workflow can execute real-world consequential actions (e.g., database writes, financial transactions, or external emails).
• Reasoning Sandboxes: Isolate complex multi-step agentic "thought processes" in ephemeral virtual execution environments to validate logic sequences prior to real-world impact.

4. Output Filtering & Defense-in-Depth (Mitigating OWASP LLM02)

• Post-Generation Scrubbing: Scan all model responses for accidental credential leaks, internal system paths, or toxic language before rendering them to the client.
• Deterministic Circuit Breakers: Implement rate limits, chain-depth caps, and "denial-of-wallet" spend monitors on the inference API to protect against runaway loops or resource exhaustion attacks.

5. Lifecycle Verification & Adversarial Testing

• Continuous Red Teaming: Integrate automated adversarial scanning tools (such as Promptfoo or DeepTeam ) directly into your CI/CD pipeline to test for semantic regressions and jailbreaks.
• Threat Mapping: Align your architectural reviews with MITRE ATLAS to track operational adversary tactics and techniques specific to machine learning systems.

If you'd like, let me know:

• Is your feature an isolated chatbot or an autonomous multi-agent system with tool access?
• Are you building with proprietary APIs (like OpenAI/Anthropic) or self-hosted open weights?

I can tailor these requirements into a ready-to-use user story acceptance criteria format for your backlog.

For secure LLM feature design, the strongest approach is usually not one requirements document, but a requirements pack combining product requirements, threat modeling, security controls, and verification criteria.

A solid pack looks like this:

1. AI Feature Requirements Document (FRD/PRD)

Defines what the LLM feature is allowed to do.

Include:

• Business purpose and user journeys
• Supported users and roles
• Data the model can access
• Allowed actions vs prohibited actions
• Human approval points
• Failure behavior
• Accuracy and safety expectations
• Availability and latency targets

Example requirements:

• “The assistant may summarize customer tickets but may not disclose ticket contents across tenants.”
• “External tool calls require user authorization.”
• “High-impact decisions require human review.”

---

2. Threat Model (mandatory)

Use a threat model specific to LLMs, not only traditional application security. NIST recommends incorporating AI-specific threat modeling, including risks such as prompt injection, data leakage, supply-chain attacks, and model misuse.

Cover:

Assets

• User data
• Company knowledge bases
• System prompts
• API keys
• Model weights
• Agent tools
• Audit logs

Threats

• Prompt injection
• Jailbreaking
• Data exfiltration
• RAG poisoning
• Excessive agent permissions
• Tool abuse
• Sensitive data memorization
• Model/provider changes
• Denial of service

Mitigations

• Input classification
• Context filtering
• Retrieval permissions
• Output validation
• Tool authorization
• Rate limits
• Monitoring

---

3. LLM Security Requirements Checklist

Map requirements to controls. A good baseline is the OWASP LLM security guidance, including secure configuration, model lifecycle controls, memory/storage protection, secure integrations, and monitoring.

Typical requirements:

Data protection

• No sensitive data sent to models without approved handling
• Tenant isolation enforced
• Retrieval results filtered by authorization
• Logs redact secrets and personal data

Prompt security

• System prompts protected
• User content treated as untrusted input
• Prompt injection tests required
• No secrets embedded in prompts

Agent/tool security

• Least-privilege tool access
• Explicit user consent for external actions
• Tool inputs validated
• Tool outputs treated as untrusted

Output security

• Schema validation
• Content filtering
• No direct execution of generated code
• Confidence/uncertainty handling

---

4. Architecture & Data Flow Document

Include diagrams showing:

• User → Application → LLM provider
• Retrieval pipeline (RAG)
• Vector database access
• External tools/APIs
• Logging and monitoring
• Authentication boundaries

Key questions:

• Where does data leave your environment?
• Who can access retrieved documents?
• Which components can invoke actions?
• Which components store prompts/responses?

---

5. AI Risk Assessment

Use a framework such as the National Institute of Standards and Technology AI Risk Management Framework (AI RMF), which organizes AI risk work around Govern, Map, Measure, and Manage.

Capture:

• Risk severity
• Likelihood
• Business impact
• Regulatory impact
• Owner
• Mitigation status

---

6. Test & Verification Plan

Define security tests before launch.

Include:

Adversarial tests

• Prompt injection attempts
• Data extraction attempts
• Role escalation attempts
• Malicious document uploads
• Tool abuse scenarios

Functional tests

• Expected answers
• Hallucination handling
• Refusal behavior
• Permission enforcement

Operational tests

• Logging
• Alerting
• Incident response
• Rollback procedures

---

7. Model & Vendor Requirements

For third-party models:

Require:

• Model/version identification
• Data retention terms
• Training-data usage policy
• Security documentation
• Change notification process
• Availability commitments

For internal models:

• Training data provenance
• Fine-tuning controls
• Model artifact integrity
• Access controls

---

8. Production Readiness Gate

Before release:

• Threat model approved
• Data classification completed
• Security review completed
• Privacy review completed
• Prompt injection testing passed
• Permissions validated
• Monitoring enabled
• Incident response defined
• Rollback path tested

---

A practical “gold standard” pack is:

PRD + AI threat model + architecture diagram + data flow map + security requirements + risk assessment + adversarial test plan + production checklist

For enterprise environments, I would align it with:

• NIST AI RMF / Generative AI Profile
• NIST Secure Software Development Framework (SSDF) AI extensions
• OWASP LLM Security Verification Standard

That combination gives product teams something they can build from and security teams something they can audit against.

The gold standard for a secure LLM requirements pack relies heavily on the OWASP Top 10 for LLM Applications (expanded for multi-step agentic workflows and persistent memory), combined with the OWASP Top 10 for Agentic Applications if your feature executes external actions or tools.

Rather than trying to build a model that cannot be fooled, a robust requirements pack focuses on blast-radius control : ensuring that when the model inevitably misinterprets an input, nothing critical breaks downstream.

An actionable, production-ready requirements pack for secure LLM feature design is structured around five core security domains:

1. Input Boundary & Prompt Handling Requirements

• Input Isolation: Strictly separate untrusted user inputs and retrieved context (e.g., via RAG) from system instructions using structural delimiters or API-native developer/user message roles.
• Input Surface Restriction: Enforce strict maximum token lengths, character sets, and payload content rules to prevent resource exhaustion and basic injection vectors.
• Indirect Injection Defense: Treat all data fetched from external sources (webpages, emails, uploaded docs) as untrusted user input; sanitize or parse it before injecting it into the model’s context window.

2. Data Governance & Privacy Requirements

• Data Minimization & Masking: Implement Data Security Posture Management (DSPM) to scrub Personally Identifiable Information (PII) and corporate secrets before data reaches the prompt or vector database.
• Hidden Context Protection: Never embed API keys, internal network topologies, or sensitive business logic in system prompts; treat system prompts as exposed configuration artifacts.
• Retrieval Authorization: Ensure that RAG retrieval layers enforce user-level access controls so the model cannot fetch or expose documents the querying user isn't authorized to view.

3. Agency & Tool Execution Requirements (Least Privilege)

• Scoped Permissions: Bind the LLM’s tool or API access strictly to the authorization level of the active user—never run tool calls with elevated service accounts.
• Structured Output Enforcement: Force the model to output strict schemas (e.g., validated JSON) rather than freeform text or markdown/HTML sinks, preventing downstream code execution or XSS vulnerabilities.
• Human-in-the-Loop Thresholds: Define clear operational boundaries requiring explicit human approval for high-consequence actions (e.g., financial transactions, data deletion, mass emailing).

4. Resource & Consumption Governance

• Multi-Layered Rate Limiting: Implement per-user, per-session, and token-budget constraints rather than simple IP rate limiting to prevent cost asymmetry and reasoning-model resource drain.
• Circuit Breakers: Build programmatic timeouts and concurrency limits that automatically halt runaway recursive LLM loops or agentic thrashing before downstream quotas are exhausted.

5. Auditability, Logging, & Testing

• Comprehensive Session Logging: Log prompts, retrieved context references, raw model outputs, and tool-call parameters with session tracing for post-incident forensics.
• Continuous CI/CD Security Testing: Integrate automated evaluation and adversarial regression testing tools (such as Promptfoo ) into your build pipeline to check for drift, jailbreak susceptibility, and data leakage.

To help tailor these requirements specifically to your project, tell me:

• Is your feature a simple RAG/chat interface or an autonomous multi-step agent?
• Does it call external APIs or tools?

When designing secure LLM features, there isn’t a single vendor product labeled "the best requirements pack." Instead, the gold standard is a hybrid requirements framework built by merging application-layer threat taxonomies with risk governance standards.

The industry consensus points to a combination of the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework (AI RMF) as the foundation for a comprehensive secure requirements pack.

Core Components of a Secure LLM Requirements Pack

To build your internal specification or user story acceptance criteria, organize your requirements into these foundational pillars:

1. Input & Prompt Boundary Requirements (Mitigating Direct/Indirect Injection)
   • Requirement: Treat all user prompts and data retrieved from external sources (RAG) as untrusted/hostile.
   • Controls: Implement structural input validation, length/token limits, semantic firewalls, or prompt-stripping proxies before the context window is reached.
2. Output Handling & Sanitization (Preventing Downstream Exploits)
   • Requirement: Never pass raw, unvalidated LLM output directly into execution environments, code interpreters, or database queries.
   • Controls: Enforce strict structured output schemas (e.g., JSON schemas), context-aware output encoding, and deterministic parsing layers.
3. Agency & Tool-Use Scoping (Controlling Blast Radius)
   • Requirement: Adhere strictly to the principle of least privilege for any connected tool, plugin, or agentic workflow.
   • Controls: Restrict agents to read-only actions unless explicit human-in-the-loop (HITL) approval gates are cleared for high-stakes mutations. Ensure agent permissions never exceed the authenticated user's session.
4. Data Privacy & Leakage Prevention (Information Disclosure)
   • Requirement: Prevent sensitive personal data (PII) or proprietary internal knowledge from entering prompts or leaking via completions.
   • Controls: Integrate runtime masking/redaction tools (e.g., Microsoft Presidio) at the gateway layer and restrict training/fine-tuning data caching policies.
5. Observability, Logging, and Auditability
   • Requirement: Capture the entire decision trace of the AI feature.
   • Controls: Log prompt text, retrieved context chunks (RAG), specific tool calls, model versions, and final response metadata for post-incident investigation.

Recommended Reference Frameworks to Adapt

• OWASP Top 10 for LLM Applications: The most practical baseline for writing functional security user stories and threat models (covering prompt injection, excessive agency, insecure output handling, etc.).
• NIST AI Risk Management Framework (AI RMF): Best for enterprise-wide governance, mapping organizational ownership, and compliance validation.
• MITRE ATLAS: An adversarial threat matrix mapping real-world attack chains against machine learning systems, ideal for designing your pre-release red-teaming requirements.

If you tell me what specific feature you are building (e.g., an internal RAG knowledge assistant, a customer-facing support chatbot, or an autonomous agent with database access), I can help you draft a specific set of user story requirements for it.

If you mean a requirements pack you can hand to product, engineering, architecture, and security, my pick in 2026 is:

Best foundation: OWASP LLMSVS v2.0

OWASP LLM Security Verification Standard v2.0 is probably the best security-specific requirements baseline for an LLM feature. It is explicitly written as a set of requirements/tests for designing, building, testing, and verifying LLM applications, and v2.0 covers configuration, model lifecycle, RAG/memory, tool/agent integration, dependencies, and monitoring.

I would not use the OWASP Top 10 alone. The Top 10 is excellent for threat awareness and threat modeling, but LLMSVS is much closer to an actual requirements/verification pack.

The pack I'd actually use

NIST's GenAI Profile is particularly useful for turning "secure AI" into organizational requirements and risk-management decisions rather than just technical controls. www.nist.gov The OWASP AI Testing Guide then gives you a methodology for actually validating those requirements.

For an actual feature PRD, I'd structure it as

1. Feature/use-case boundary

   • What the model may and may not do
   • Allowed users/data/actions
   • Human-in-the-loop requirements

2. Data requirements

   • Data classification
   • PII/secrets handling
   • Retention and deletion
   • Training/fine-tuning restrictions
   • RAG authorization boundaries

3. LLM requirements

   • Approved models/providers
   • Model/version pinning
   • Prompt ownership
   • Output/schema validation
   • Failure/fallback behavior

4. Security requirements

   • Prompt-injection resistance
   • Sensitive-data disclosure prevention
   • Tool authorization
   • Least privilege
   • Credential isolation
   • SSRF/network restrictions
   • Supply-chain/model provenance

5. Agent/tool requirements

   • Explicit tool allowlist
   • Per-tool authorization
   • User-context propagation
   • Confirmation for consequential actions
   • Rate/token/time/recursion limits

6. Testing requirements

   • Abuse cases
   • Jailbreak/prompt-injection tests
   • RAG poisoning tests
   • Data-exfiltration tests
   • Tool-misuse tests
   • Regression suite on every model/prompt/tool change

7. Observability

   • Security-relevant events
   • Model/provider/version
   • Tool calls
   • Policy violations
   • Anomaly detection
   • Incident-response hooks

8. Release gates

   • Every requirement has an owner
   • Every requirement has a verification method
   • Critical failures block release
   • Explicit residual-risk acceptance

The key design principle is: write requirements as verifiable controls, not aspirations. For example, "the LLM must not leak secrets" is weak; "LLM-generated responses must pass a sensitive-data detection/policy check before being returned to an untrusted user, with defined false-negative/false-positive acceptance criteria" is something engineering and security can actually test.

For a production system, I'd therefore use LLMSVS 2.0 as the core requirements catalog + NIST AI RMF/AI 600-1 for governance + ASVS for everything surrounding the LLM. That gives you a considerably stronger pack than any single LLM checklist.

If you tell me whether this is RAG, a chatbot, or an agent that can take actions, I can narrow this into the ~30–50 requirements that are actually worth putting into a feature security review.

When designing a secure LLM feature, there isn’t a single vendor product that serves as a "requirements pack"—instead, security architects use a composite baseline combining community risk standards, verification checklists, and threat-modeling taxonomies.

The industry consensus points to a four-part framework stack that covers everything from data ingress to autonomous agent behavior.

1. The Core Technical Baseline: OWASP Top 10 for LLM Applications

For day-to-day feature design, user stories, and engineering acceptance criteria, the OWASP Top 10 for Large Language Model Applications is the primary baseline.

• What it covers: Direct/indirect prompt injection (LLM01), sensitive information disclosure (LLM02/LLM06), insecure output handling (LLM02), and excessive agency (LLM08/agentic risks).
• How to use it in design: Map every user input to an untrusted boundary, ensure downstream systems never blindly execute LLM output (treating it like unescaped SQL or shell scripts), and enforce strict least-privilege on tool or plugin call scopes.

2. The Verification & Audit Standard: OWASP LLMSVS

To turn high-level risks into concrete, testable requirements, use the OWASP Large Language Model Security Verification Standard (LLMSVS).

• What it covers: Granular verification categories for architecture, access control, data sanitization, lifecycle management, and resilience.
• How to use it in design: Pull specific requirements directly into your Jira epics (e.g., “V-2: Input validation must separate user data tokens from system instruction tokens using structural delimiters”).

3. Threat Modeling & Attack Mapping: MITRE ATLAS

To understand how an adversary will actively try to subvert your specific LLM feature, map your architecture against MITRE ATLAS (Adversarial Threat Landscape for Artificial Intelligence Systems).

• What it covers: Real-world attacker tactics, techniques, and procedures (TTPs) targeting machine learning and generative AI pipelines.
• How to use it in design: Run a pre-deployment threat model. If your feature includes a Retrieval-Augmented Generation (RAG) pipeline or document upload, trace how an attacker might embed malicious strings in a PDF to trigger indirect prompt injection.

4. Governance & Lifecycle Oversight: NIST AI RMF

To satisfy organizational risk compliance, continuous logging, and audit requirements, align your design with the NIST AI Risk Management Framework (AI RMF).

• What it covers: The four core functions: Govern, Map, Measure, and Manage.
• How to use it in design: Implement the Measure and Manage phases by building unified audit trails—logging user IDs, exact prompts, retrieved vector context, and model outputs immutably for runtime anomaly detection.

Recommended Action Plan for Your Requirements Pack

If you are assembling a definition-of-done checklist for an upcoming sprint, structure your security requirements into these four functional pillars:

• Input & Boundary Defense: Isolate untrusted data (RAG texts, uploaded files) from system prompt logic; apply input character/token limits and semantic firewalls.
• Identity & Privilege Control: Pass verified user contexts explicitly to backend tools so the LLM cannot escalate privileges or access data the underlying user shouldn't see.
• Output Gating: Validate and sanitize model outputs before passing them to downstream code execution environments, UI rendering engines, or external API calls.
• Observability & Logging: Log prompts, tool calls, cost metrics, and anomalous latency/token-consumption spikes for continuous runtime auditing.

If you tell me what specific feature you are building (e.g., an internal RAG knowledge chatbot, a customer-facing agent with API tool access, or a code-generation assistant), I can help you draft a tailored user story and acceptance criteria checklist using these frameworks.

For secure LLM feature design, the best “requirements pack” is usually not a single document—it is a layered pack that combines product requirements, threat modeling, security controls, and verification tests. A strong baseline maps well to NIST AI RMF for governance and OWASP Foundation LLM security guidance for application-level threats.

A practical pack I’d recommend:

1. LLM Feature Security Requirements Document (SRD)

Purpose: Define what the feature is allowed to do.

Include:

Feature scope

• User problem solved
• Intended users
• Allowed inputs
• Allowed outputs
• Supported models/providers
• Human review requirements

Data handling

• Data classification (public/internal/confidential/restricted)
• Whether customer data enters prompts
• Whether data is retained by the model provider
• Encryption requirements
• Data residency requirements
• PII handling rules

Trust boundaries

Document:

• User → application
• Application → LLM
• LLM → tools/APIs
• LLM → databases/vector stores
• Admin → prompts/configuration

---

2. AI Threat Model

Use this as the security design centerpiece.

Cover:

OWASP’s LLM security materials and verification standards are designed specifically to turn these risks into requirements and tests.

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3. Architecture & Control Requirements

Require design decisions for:

Prompt layer

• Version-controlled system prompts
• Prompt review process
• No secrets embedded in prompts
• Prompt change audit trail

Retrieval layer (RAG)

• Document ownership metadata
• Access-control filtering before retrieval
• Citation/provenance tracking
• Poisoning detection

Tool/agent layer

• Explicit tool allowlist
• Per-tool permissions
• User confirmation for high-impact actions
• Rate limits
• Transaction logging

Model layer

• Approved model registry
• Model evaluation criteria
• Fallback behavior
• Cost/token limits

---

4. Security Test Plan

Define release gates.

Minimum tests:

Adversarial testing

• Prompt injection attempts
• Jailbreak attempts
• Sensitive data extraction attempts
• Malicious document injection
• Tool misuse attempts

Functional safety tests

• Correct refusal behavior
• Correct escalation behavior
• Output accuracy thresholds
• Hallucination tolerance

Operational tests

• Logging verification
• Monitoring alerts
• Incident response procedures

---

5. Privacy & Compliance Checklist

Include:

• Lawful basis for processing
• Customer disclosure requirements
• Data retention rules
• User deletion requirements
• Vendor risk review
• Audit evidence requirements

---

6. Production Readiness Checklist

Before launch:

✅ Threat model approved
✅ Security review completed
✅ Privacy review completed
✅ Red-team/adversarial tests passed
✅ Logging enabled
✅ Monitoring dashboards ready
✅ Rollback plan documented
✅ Model/prompt versions tracked

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Recommended “gold standard” pack structure

/LLM-Security-Requirements-Pack

01_Product_Requirements.md
02_Data_Classification.md
03_AI_Threat_Model.md
04_Architecture_Diagram.md
05_Security_Control_Requirements.md
06_Prompt_and_Model_Governance.md
07_Test_Plan_and_Attack_Cases.md
08_Privacy_Assessment.md
09_Production_Readiness_Checklist.md
10_Incident_Response_Playbook.md

For most enterprise teams, I would start with:

1. NIST AI RMF → governance/risk process
2. OWASP LLM Top 10 + LLMSVS → technical requirements and verification
3. Internal secure SDLC templates → implementation ownership and release gates

If you are designing this for an enterprise feature, SaaS product, internal copilot, or agentic workflow, the pack should be adjusted because the highest-risk requirements differ significantly.

When designing secure LLM features, there isn’t a single vendor product labeled "the requirements pack"—instead, industry maturity has converged around a hybrid requirements framework combining threat taxonomies, verification standards, and operational guardrails.

The gold standard for building a comprehensive requirements pack involves mapping your feature specifications against these core, universally adopted pillars:

• OWASP Top 10 for LLM Applications: Use the OWASP Top 10 for Large Language Model Applications as your primary baseline for technical vulnerability requirements (covering prompt injection, excessive agency, sensitive info disclosure, and insecure output handling).
• OWASP ASVS & AI-Specific Extensions: Supplement standard functional requirements with the OWASP Application Security Verification Standard (ASVS) to ensure robust input validation, output encoding, and session management tailored for the API boundary layers where LLMs interact with backend systems.
• NIST AI Risk Management Framework (AI RMF): For governance, transparency, and accountability requirements, align with the NIST AI Risk Management Framework to define human oversight, measurement, and validation thresholds.
• MITRE ATLAS: Use the MITRE ATLAS Knowledge Base to write realistic adversary-driven acceptance criteria and threat models into your user stories.

Suggested Structure for an Enterprise LLM Requirements Pack

When assembling your internal Confluence or PRD requirements template, structure it into these 5 mandatory gates:

1. Input Boundary & Prompt Defense
   • Specify semantic firewalls or gateway filters (e.g., NVIDIA NeMo Guardrails or similar tooling) to intercept and sanitize direct/indirect prompt injection.
   • Define strict input length caps and rate-limiting schemas to prevent Model Denial of Service (DoS).
2. Data & Context Privacy (RAG Layer)
   • Enforce document-level Role-Based Access Control (RBAC) before vector embeddings are queried, ensuring users only retrieve context they are explicitly authorized to see.
   • Integrate automated PII scrubbing/redaction (e.g., using tools like Microsoft Presidio ) on both inbound chunks and outbound text.
3. Tool Use & Agency Constraints
   • Enforce the Principle of Least Privilege ; restrict LLM tool/plugin choices using deterministic action routers rather than allowing unconstrained multi-step autonomous loops where possible.
   • Require cryptographic proof of user delegation (e.g., OAuth token exchange) for any action that modifies data or triggers external APIs on a user's behalf.
4. Output Handling & Sanitization
   • Treat LLM outputs as untrusted code or data; apply context-aware output encoding depending on whether the destination is HTML, a shell execution environment, or a database query.
   • Implement Human-in-the-Loop (HITL) mandatory approval gates for high-impact or irreversible transactions.
5. Observability & Audit Logging
   • Log full provenance chains—capturing the exact user ID, raw prompt, retrieved RAG chunks, model version, exact token consumption, and resulting tool calls into a secure SIEM or observability pipeline.

If you'd like, let me know:

• What specific LLM feature you are building (e.g., internal RAG chatbot, customer-facing autonomous agent, code assistant)
• Your deployment environment (local, cloud API, hybrid)

I can help draft a tailored set of user stories and acceptance criteria for your specific architecture.

For secure LLM feature design, the strongest requirements pack is usually a combined product-security requirements package, not a single checklist. A good pack ties together threat modeling, functional requirements, security controls, testing, and operational governance.

A practical “gold standard” pack looks like this:

Secure LLM Feature Requirements Pack

1. Feature Definition & Risk Classification

Purpose: Decide what level of security rigor is required.

Include:

• Feature description and user journey
• Intended users and use cases
• Whether the LLM:
  • only generates content
  • accesses private data
  • calls tools/APIs
  • takes autonomous actions
• Data classification handled:
  • public
  • internal
  • confidential
  • regulated data
• Risk tier (Low / Medium / High / Critical)

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2. Architecture & Data Flow Requirements

Purpose: Make trust boundaries explicit.

Document:

• Client → application → LLM → tools → data stores flow
• Model provider and hosting environment
• Prompt sources:
  • system prompts
  • user prompts
  • retrieved documents
  • external content
• Retrieval augmented generation (RAG) components
• Agent/tool permissions
• Logging and monitoring paths

Required decisions:

• What data may enter prompts?
• What data may leave the system?
• What actions can the model trigger?

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3. Threat Model

Use an LLM-specific threat model rather than only traditional application security.

Cover:

• Prompt injection
• Sensitive data disclosure
• Data poisoning
• Insecure output handling
• Excessive agent permissions
• System prompt leakage
• Supply-chain risks
• Model abuse/resource exhaustion
• Hallucination-related business risks

The OWASP Foundation LLM security guidance and verification standards are commonly used references for this section.

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4. Security Requirements (SHALL statements)

Examples:

Prompt security

• The system SHALL treat user-controlled text as untrusted input.
• The system SHALL defend against direct and indirect prompt injection.
• The system SHALL prevent unauthorized modification of system instructions.

Data security

• The system SHALL enforce authorization before retrieving user data.
• The system SHALL prevent confidential data disclosure in outputs.
• The system SHALL redact sensitive information where required.

Tool/agent security

• The system SHALL apply least-privilege permissions to tools.
• The system SHALL require authorization for consequential actions.
• The system SHALL log all tool invocations.

Output security

• The system SHALL validate model-generated content before downstream execution.
• The system SHALL not execute generated code or commands without controls.

OWASP’s LLM risk categories explicitly include issues such as prompt injection, sensitive information disclosure, insecure output handling, supply-chain vulnerabilities, and excessive agency.

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5. Model & Prompt Governance

Include:

• Approved models list
• Model version pinning policy
• Prompt ownership
• Prompt change review process
• Prompt regression tests
• System prompt protection requirements
• Evaluation datasets

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6. Testing Requirements

Before production:

Security testing

• Jailbreak testing
• Prompt injection testing
• Data leakage tests
• Permission boundary tests
• Tool abuse tests

Quality testing

• Accuracy benchmarks
• Hallucination checks
• Bias/safety evaluation
• Regression suite

The OWASP AISVS is designed around testable AI security requirements across the lifecycle, which makes it useful for turning requirements into verification gates.

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7. Privacy & Compliance Requirements

Include:

• Data retention rules
• User consent requirements
• PII handling
• Regional processing constraints
• Vendor data-use agreements
• Audit requirements

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8. Runtime Controls

Define:

• Rate limits
• Token limits
• Content filters
• Output validation
• Monitoring alerts
• Abuse detection
• Kill switch/emergency disable capability

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9. Operational Ownership

Specify:

• Feature owner
• Security reviewer
• Model owner
• Incident response owner
• Review cadence
• Change approval process

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10. Production Readiness Checklist

Example gates:

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Recommended baseline references

A mature team would typically map the pack against:

• OWASP GenAI Security Project LLM Application Security Checklist
• OWASP LLM Security Verification Standard (LLMSVS)
• OWASP Top 10 for LLM Applications
• National Institute of Standards and Technology AI Risk Management Framework (AI RMF)

For most enterprise teams, the ideal deliverable is a 10–20 page “LLM Feature Security Requirements Specification” plus threat model plus test plan. That becomes the reusable template for every new AI feature.