AI Incident Response and Abuse Operations
Incident Response
1.4 / 5LowHigh
AIPSA · AI Product Security
Acme Assistant Platform · Lite · High
Sample report
Acme Corp B2B SaaS with RAG, agents, and enterprise customers. Scores, evidence gaps, and roadmap are computed from realistic mock responses.
This is a sample AIPSA Scorecard, not an assessment of a real company. Scores are maturity signals based on submitted evidence and reviewed answers. They do not certify that a product or organization is secure.
Acme Assistant Platform AIPSA Scorecard
Acme Corp · Security maturity assessment and evidence planning
Overall Maturity
Managed
3.0/ 5Executive Interpretation
AI product security is managed across important domains, but high-risk gaps remain in incident response, prompt-injection resilience, agentic permissions, and RAG authorization. The priority is reducing time to evidence, closing repeatable controls, and improving verification. The score reflects 1 foundational cap rule that bound the overall maturity claim.
Target Next: Measured· 3.75–4.49
3.0/ 5
Foundational cap applied
Incident response cap capped overall maturity at 3.0.
Capped from 3.25 to 3.00 because Without AI incident and abuse operations, the program cannot be considered managed even if design-time controls exist.
Ad Hoc
Repeatable
Managed
Measured
Adaptive
012345
Domain Maturity
Domain scores combine answers, evidence confidence, and cap rules.
UnseenAd HocRepeatableManagedMeasuredAdaptive
Domain Scores
Each domain is scored 0–5 using weighted question answers. Domains are sorted weakest first.
DomainScoreLevelConfidencePriority
AI Incident Response and Abuse Operations
Incident Response
1.4 / 5
Prompt Injection and Context Manipulation
Prompt Injection
2.6 / 5
Agentic Permissions and Tool Safety
Agentic Permissions
2.6 / 5
RAG, Data Access, and Authorization
RAG Authorization
2.7 / 5
Model, Dataset, and AI Supply Chain Security
Supply Chain
3.3 / 5
Governance, Policy, and Risk Acceptance
Governance
3.4 / 5
AI Product Threat Modeling
Threat Modeling
3.7 / 5
Evaluation, Testing, and Red Teaming
Evaluation
3.7 / 5
AI Inventory and System Boundaries
Inventory
4.0 / 5
Logging, Telemetry, and Forensics
Logging
4.0 / 5
Secure SDLC and Developer Enablement
SDLC
4.0 / 5
Customer Trust, Evidence, and Sales Enablement
Customer Trust
4.0 / 5
Ad Hoc
Prompt Injection and Context Manipulation
Prompt Injection
2.6 / 5LowMedium
Agentic Permissions and Tool Safety
Agentic Permissions
2.6 / 5LowMedium
RAG, Data Access, and Authorization
RAG Authorization
2.7 / 5LowMedium
Model, Dataset, and AI Supply Chain Security
Supply Chain
3.3 / 5LowMedium
Governance, Policy, and Risk Acceptance
Governance
3.4 / 5LowMedium
AI Product Threat Modeling
Threat Modeling
3.7 / 5MediumLow
Evaluation, Testing, and Red Teaming
Evaluation
3.7 / 5MediumLow
AI Inventory and System Boundaries
Inventory
4.0 / 5MediumLow
Logging, Telemetry, and Forensics
Logging
4.0 / 5MediumLow
Secure SDLC and Developer Enablement
SDLC
4.0 / 5MediumLow
Customer Trust, Evidence, and Sales Enablement
Customer Trust
4.0 / 5MediumLow
Top Gaps
Highest-risk blockers to the next maturity level.
#DomainScoreRiskWhy it matters
11.4Critical
AI Incident Response and Abuse Operations
Incident Response
AI incidents include prompt injection, data exposure, unauthorized tool actions, provider failures, model abuse, retrieval poisoning, and harmful automation. Traditional IR needs AI-specific playbooks.
22.6High
Prompt Injection and Context Manipulation
Prompt Injection
Prompt injection is not a clever demo trick. It is the input-validation and confused-deputy problem of LLM application security.
32.6High
Agentic Permissions and Tool Safety
Agentic Permissions
An agent with tools is a product actor. Its permissions, identity, approvals, blast radius, and audit trail must be engineered like any other privileged service path.
42.7High
RAG, Data Access, and Authorization
RAG Authorization
RAG turns search, embeddings, chunks, documents, and access control into one product-security boundary. Leakage often happens through retrieval, not generation.
53.3Medium
Model, Dataset, and AI Supply Chain Security
Supply Chain
AI products inherit risk from providers, models, data pipelines, prompts, plugins, dependencies, and evaluation artifacts. Supply chain visibility is product risk visibility.
1Critical
AI Incident Response and Abuse Operations
Incident Response
AI incidents include prompt injection, data exposure, unauthorized tool actions, provider failures, model abuse, retrieval poisoning, and harmful automation. Traditional IR needs AI-specific playbooks.
Score1.4
2High
Prompt Injection and Context Manipulation
Prompt Injection
Prompt injection is not a clever demo trick. It is the input-validation and confused-deputy problem of LLM application security.
Score2.6
3High
Agentic Permissions and Tool Safety
Agentic Permissions
An agent with tools is a product actor. Its permissions, identity, approvals, blast radius, and audit trail must be engineered like any other privileged service path.
Score2.6
4High
RAG, Data Access, and Authorization
RAG Authorization
RAG turns search, embeddings, chunks, documents, and access control into one product-security boundary. Leakage often happens through retrieval, not generation.
Score2.7
5Medium
Model, Dataset, and AI Supply Chain Security
Supply Chain
AI products inherit risk from providers, models, data pipelines, prompts, plugins, dependencies, and evaluation artifacts. Supply chain visibility is product risk visibility.
Score3.3
30 / 60 / 90 Day Remediation RoadmapPrioritized actions from your triggered findings, grouped by recommended time horizon.
Turn gaps into owners, evidence, and repeatable operating cadence.
30 Days
Foundation
Establish repeatable rails for critical AI systems.
1. 30 days
Create AI-specific incident runbooks
Create runbooks for prompt injection, data exposure, malicious tool action, retrieval poisoning, provider outage, model abuse, and harmful automation.
mediumhigh
60 Days
Scaling
Harden testing, logging, authorization, and incident handling.
1. 60 days
Implement tool-call mediation for prompt-influenced actions
Wrap sensitive tool calls with validation, policy checks, approval requirements, allowlists, deny rules, simulation, and audit logging.
largehigh
2. 60 days
Add approval safeguards for high-impact agent actions
Require preview, approval, simulation, staged execution, or explicit confirmation for high-impact, irreversible, external, financial, administrative, or customer-visible actions.
largehigh
3. 60 days
Add AI containment and kill switches
Add mechanisms to disable or constrain risky models, tools, agents, retrieval sources, prompts, providers, and AI features during incidents.
largehigh
4. 60 days
Add RAG provenance, deletion, and retention controls
Ensure chunks preserve source, owner, timestamp, ACL, deletion, retention, freshness, and policy state through ingestion, indexing, retrieval, and citation.
mediumhigh
90 Days
Optimization
Turn the program into a measurable operating model.
1. 90 days
Close the AI incident and findings learning loop
Ensure incidents, near misses, red-team findings, abuse reports, and eval failures update controls, tests, runbooks, launch gates, and training.
smallmedium
Evidence GapsEvidence items that are missing or insufficient based on low-scoring questions. Collect these to improve your maturity score.
What is missing, who owns it, and how long it should take to prove.
Missing EvidenceDomainImpactEst. EffortOwner
Do AI-specific incident runbooks exist?Incident ResponseHigh2–4 weeksSecurity Ops
Do AI incidents improve the program?Incident ResponseHigh1–2 weeksSecurity Ops
Are prompt-influenced tool calls mediated?Prompt InjectionMedium1–3 monthsAppSec
Do high-impact agent actions require safeguards?Agentic PermissionsMedium1–3 monthsAI Platform
Does retrieved context preserve provenance and lifecycle state?RAG AuthorizationMedium2–4 weeksProduct Security
Can risky AI behavior be contained quickly?Incident ResponseMedium1–3 monthsSecurity Ops
Do AI-specific incident runbooks exist?
Incident Response
Effort
2–4 weeks
Owner
Security Ops
Do AI incidents improve the program?
Incident Response
Effort
1–2 weeks
Owner
Security Ops
Are prompt-influenced tool calls mediated?
Prompt Injection
Effort
1–3 months
Owner
AppSec
Do high-impact agent actions require safeguards?
Agentic Permissions
Effort
1–3 months
Owner
AI Platform
Does retrieved context preserve provenance and lifecycle state?
RAG Authorization
Effort
2–4 weeks
Owner
Product Security
Can risky AI behavior be contained quickly?
Incident Response
Effort
1–3 months
Owner
Security Ops
Framework Crosswalk
Framework crosswalks are evidence-support tools. They do not replace formal audit or certification.
NIST
NIST AI RMF
Govern3.8
Strong evidence. Avg 3.81/5.
Map3.2
Partial evidence. Avg 3.19/5.
Measure3.4
Partial evidence. Avg 3.44/5.
Manage2.7
Partial evidence. Avg 2.67/5.
OWASP
OWASP AIMA
Strategy and Governance4.0
Strong evidence. Avg 4.00/5.
Design and Risk Assessment3.7
Partial evidence. Avg 3.69/5.
Implementation, Validation, Monitoring, and Assurance3.7
Partial evidence. Avg 3.69/5.
Implementation and Operations4.0
Strong evidence. Avg 4.00/5.
CSA
CSA AI Security Scorecard
AI Governance and Risk Management4.0
Strong evidence. Avg 4.00/5.
AI Risk Management3.7
Partial evidence. Avg 3.69/5.
AI Application Security and Deployment Governance2.6
Partial evidence. Avg 2.64/5.
Third-Party and Provider Risk3.3
Partial evidence. Avg 3.27/5.
Monitoring, Incident Response, and Assurance4.0
Strong evidence. Avg 4.00/5.
AI Incident Response and Abuse Operations1.4
Weak evidence. Avg 1.38/5.
Governance, Risk, and Compliance3.4
Partial evidence. Avg 3.42/5.
Compliance, Assurance, and Customer Trust4.0
Strong evidence. Avg 4.00/5.
ISO
ISO/IEC 42001
Context, planning, operation, and AI system inventory evidence4.0
Strong evidence. Avg 4.00/5.
Operational controls and data management evidence2.7
Partial evidence. Avg 2.69/5.
Supplier, resource, and operational control evidence3.3
Partial evidence. Avg 3.27/5.
Performance evaluation and improvement4.0
Strong evidence. Avg 4.00/5.
Operation, performance evaluation, and improvement1.4
Weak evidence. Avg 1.38/5.
Leadership, planning, operation, performance evaluation, and improvement3.4
Partial evidence. Avg 3.42/5.
Support, operation, and competence4.0
Strong evidence. Avg 4.00/5.
Documented information, performance evaluation, customer assurance, and improvement4.0
Strong evidence. Avg 4.00/5.
Aligned
Partially Aligned
Weak
Not Started
Badge Eligibility
Current level: Managed. Public badges require scope, date, evidence, and caveats.
Eligible marks
- AIPSA Program: Managed
- AIPSA Evidence Pack, if scoped assessment is completed
Not eligible yet
- AIPSA Verified Controls until retest evidence is complete
Public badges require scope, date, evidence, and caveats.
How scoring works
AIPSA combines domain responses, evidence confidence, and cap rules. High scores require not only stated controls, but evidence that controls are repeatable, monitored, and improving.
Export
Evidence pack outputs and shareable artifacts for briefing, customer review, and internal remediation tracking.