System boundary: MAESTRO reviews focus on models, AI agents, data flows, CI/CD pipelines, supporting tools and third-party APIs. Broader IT security hygiene (patching, identity governance, endpoint protection) is assumed to be managed by existing programs.Assumptions: organizations have the security baseline configurations and compliance, such as ISO 27XXX, in place. MAESTRO builds on these baselines and emphasizes AI-specific risks.Threat actors: The framework considers external adversaries, malicious insiders, careless insiders and compromised suppliers. Each actor may target different layers of the AI stack in an organizational context.This scoping ensures MAESTRO is applied where it is most impactful: securing the AI-driven systems at the heart of modern banking.
Minimum viable controls: Businesses should ask: What do we need to implement first? MAESTRO can be phased in, but a baseline set of controls per layer provides immediate value.
Layer
Minimum Specific Controls
Foundation Models
Input/output validation for chatbots, deny-by-default for API tool use, API key rotation, model provenance verification to prevent tampered LLMs
Data Operations
End-to-end transaction data lineage, signed datasets for regulatory reporting, schema validation at ingestion, anomaly detection on user behavior, card payments, data changes in the datasets, etc.
Agent Frameworks
Per-agent RBAC tied to transaction type, scoped tokens for fraud detection and credit agents, allowlist of banking APIs (e.g., SWIFT, ACH), execution sandboxing for risky operations
Deployment & Infrastructure
IaC scanning for different compliances, such as PCI-DSS, container image signing, egress restrictions for agents accessing APIs and least-privileged cloud service roles
Evaluation & Observability
Prompt-injection test suites for banking chatbots, drift monitoring in credit scoring models, transaction anomaly alerts and regulatory explainability logging
Security & Compliance
Controls mapped to Basel III, GDPR, PCI DSS, immutable audit trails for regulators and DLP policies applied to all model inputs/outputs
Agent Ecosystem
Dependency inventory of third-party APIs, blast-radius limits for cross-agent failures, circuit breakers in payment processing flows and multi-agent simulations before deployment
It is crucial to ensure there is a crystal clear, defined RACI table and everyone is briefed and aware of their roles and responsibilities.To obtain a better understanding, we need to have a deep dive into the 7 layers of MAESTRO by leveraging threats and use cases in the banking sector:
The 7 layers of MAESTRO in banking: MAESTRO organizes AI risk into seven interdependent layers. In banking, each layer presents unique challenges and must be addressed systematically.
1. Foundation models/core services
Large language models power virtual assistants, fraud detection models and risk analytics in banks. Any manipulation at this level can undermine trust in the entire system.
Purpose: Secure the LLMs and APIs underpinning AI services.Example threat: Adversarial prompts alter model outputs.Use case: A customer tricks a virtual banking assistant into disclosing loan approval criteria.Controls: Input/output sanitization, API lifecycle management, watermarking of model responses.
2. Data operations
Transaction, payment and credit data are the lifeblood of banking AI. Compromised data pipelines can poison decision-making and regulatory reporting.
Purpose: Safeguard the integrity and provenance of banking data pipelines.Example threat: Poisoned transaction records bias loan decisions.Use case: Attackers inject fake payment histories to secure unauthorized loans.Controls Include Data lineage, signed datasets and anomaly detection for unusual transaction flows.
3. Agent frameworks/application logic
Banks deploy AI agents for fraud monitoring, payment approvals and customer service. Without strict controls, these agents can be misused or over-privileged.
Purpose: Secure orchestration and business logic in AI agents.Example threat: Prompt injection disables fraud monitoring.Use case: A fraud detection agent is tricked into ignoring suspicious transactions during dispute resolution.Controls: Per-agent RBAC, scoped tokens, allowlisted API usage and sandboxing.
4. Deployment & infrastructure
AI in could be deployed across hybrid cloud and containerized environments, creating opportunities for supply chain attacks and misconfigurations.
Purpose: Secure runtime environments and CI/CD pipelines.Example threat: Compromised container images in deployment pipelines.Use case: Backdoored fraud detection images are deployed through an unscanned pipeline.Controls: IaC scanning for PCI compliance, container image signing and hardened Kubernetes.
5. Evaluation & observability
Banking AI must remain accurate, explainable and compliant. Continuous monitoring ensures that models don’t drift into unsafe or discriminatory behavior.
Purpose: Track AI system health and behavior over time.Example threat: Credit scoring model drifts, approving risky borrowers.Use case: Unmonitored drift leads to regulatory penalties for biased lending.Controls: Drift monitoring, explainability logging, anomaly detection on credit decisions.
6. Security & compliance
Banking is one of the most regulated industries. AI must align with global standards like Basel III, PCI DSS, GDPR and the EU AI Act.
Purpose: Ensure regulatory alignment and enterprise security policy enforcement.Example threat: Unauthorized data leakage in AI responses.Use case: A banking chatbot reveals customer account data to the wrong user.Controls: Immutable audit logs, DLP enforcement, redaction guardrails.
7. Agent ecosystem
Banks operate in complex ecosystems with multiple AI agents coordinating across payment networks, partners and fintech APIs. Risks at this layer are systemic.
Purpose: Manage systemic risks across agent interactions.Example threat: Feedback loops between payment and fraud agents.Use case: A fraud agent incorrectly blocks all ACH transactions, halting operations.Controls: Trust boundaries, multi-agent simulations, circuit breakers for payment systems.
Use cases in practice (Banking):
1. Credit scoring and loan approvals
A loan approval model is poisoned by manipulated transaction histories, resulting in biased or inaccurate credit decisions.
Layers: Data Operations, Evaluation & Observability, Security & Compliance.Mitigation: Signed datasets, drift monitoring and regulatory explainability requirements.
2. Fraud detection systems
Fraud models are manipulated by adversarial data, causing missed fraud alerts or false positives.
Layers: Foundation Models, Agent Frameworks, Deployment & Infrastructure.Mitigation: Input validation, scoped agent RBAC and container signing in pipelines.
3. Conversational banking assistants
Virtual assistants are tricked into disclosing sensitive account information.
Layers: Foundation Models, Security & Compliance.Mitigation: Input/output sanitization, DLP policies, compliance guardrails.
4. Payment processing and transaction flows
AI agents managing transactions create a feedback loop, freezing legitimate payments.
Layers: Agent Ecosystem, Agent Frameworks, Evaluation & Observability.Mitigation: Circuit breakers, trust boundaries and simulation of agent interactions.
5. Regulatory compliance reporting
AI systems generating regulatory reports drift over time, producing inaccurate filings.
Layers: Evaluation & Observability, Security & Compliance.Mitigation: Continuous monitoring, immutable audit logs, anomaly detection.
MAESTRO Layer
Example Threat
Banking Use Case
Key Controls
Foundation Models / Core Services
Adversarial prompts alter outputs
Virtual assistant tricked into revealing loan approval rules
Input/output sanitization, API lifecycle mgmt., model watermarking
Data Operations
Poisoned transaction data
Fake payment histories bias credit scoring
Data lineage, signed datasets, anomaly detection, schema validation
Agent Frameworks / Application Logic
Prompt injection disables fraud monitoring
Fraud detection agent ignores suspicious transactions
Per-agent RBAC, scoped tokens, allowlisted APIs and sandboxing
Deployment & Infrastructure
Compromised container images
Backdoored fraud detection models deployed via CI/CD
IaC scanning (PCI), image signing, hardened Kubernetes, egress restrictions
Evaluation & Observability
Model drift degrades performance
The credit scoring model starts approving risky borrowers
Drift monitoring, anomaly alerts, explainability logs, test harnesses
Security & Compliance
Unauthorized data leakage
Chatbot exposes customer account info
Immutable audit logs, DLP enforcement, automated redaction, compliance guardrails
Agent Ecosystem
Feedback loops disrupt workflows
Fraud and payment agents block legitimate ACH transfers
Trust boundaries, dependency mapping, circuit breakers and pre-production simulations
The infrastructure threat modeling for the AI could be hosted within the on-prem or cloud environment or a SaaS service should be assessed separately as well
A foundation for systemic AI security: AI in organizations is no longer experimental. With agentic AI, organizations face autonomy, unpredictability and systemic risks that traditional frameworks cannot fully address.MAESTRO provides a structured, layered, outcome-driven framework tailored to these realities. By applying it across data, models, infrastructure and ecosystems, organizations can secure and enhance the efficiency, cost and security and resiliency of their businesses.While frameworks like MITRE, OWASP, NIST and ISO add tactical and governance layers, MAESTRO serves as the foundation for systemic AI security in banking.This article is published as part of the Foundry Expert Contributor Network.Want to join?
First seen on csoonline.com
Jump to article: www.csoonline.com/article/4072341/introducing-maestro-a-framework-for-securing-generative-and-agentic-ai.html
| Layer | Minimum Specific Controls |
| Foundation Models | Input/output validation for chatbots, deny-by-default for API tool use, API key rotation, model provenance verification to prevent tampered LLMs |
| Data Operations | End-to-end transaction data lineage, signed datasets for regulatory reporting, schema validation at ingestion, anomaly detection on user behavior, card payments, data changes in the datasets, etc. |
| Agent Frameworks | Per-agent RBAC tied to transaction type, scoped tokens for fraud detection and credit agents, allowlist of banking APIs (e.g., SWIFT, ACH), execution sandboxing for risky operations |
| Deployment & Infrastructure | IaC scanning for different compliances, such as PCI-DSS, container image signing, egress restrictions for agents accessing APIs and least-privileged cloud service roles |
| Evaluation & Observability | Prompt-injection test suites for banking chatbots, drift monitoring in credit scoring models, transaction anomaly alerts and regulatory explainability logging |
| Security & Compliance | Controls mapped to Basel III, GDPR, PCI DSS, immutable audit trails for regulators and DLP policies applied to all model inputs/outputs |
| Agent Ecosystem | Dependency inventory of third-party APIs, blast-radius limits for cross-agent failures, circuit breakers in payment processing flows and multi-agent simulations before deployment |
It is crucial to ensure there is a crystal clear, defined RACI table and everyone is briefed and aware of their roles and responsibilities.To obtain a better understanding, we need to have a deep dive into the 7 layers of MAESTRO by leveraging threats and use cases in the banking sector:
The 7 layers of MAESTRO in banking: MAESTRO organizes AI risk into seven interdependent layers. In banking, each layer presents unique challenges and must be addressed systematically.
1. Foundation models/core services
Large language models power virtual assistants, fraud detection models and risk analytics in banks. Any manipulation at this level can undermine trust in the entire system.
Purpose: Secure the LLMs and APIs underpinning AI services.Example threat: Adversarial prompts alter model outputs.Use case: A customer tricks a virtual banking assistant into disclosing loan approval criteria.Controls: Input/output sanitization, API lifecycle management, watermarking of model responses.
2. Data operations
Transaction, payment and credit data are the lifeblood of banking AI. Compromised data pipelines can poison decision-making and regulatory reporting.
Purpose: Safeguard the integrity and provenance of banking data pipelines.Example threat: Poisoned transaction records bias loan decisions.Use case: Attackers inject fake payment histories to secure unauthorized loans.Controls Include Data lineage, signed datasets and anomaly detection for unusual transaction flows.
3. Agent frameworks/application logic
Banks deploy AI agents for fraud monitoring, payment approvals and customer service. Without strict controls, these agents can be misused or over-privileged.
Purpose: Secure orchestration and business logic in AI agents.Example threat: Prompt injection disables fraud monitoring.Use case: A fraud detection agent is tricked into ignoring suspicious transactions during dispute resolution.Controls: Per-agent RBAC, scoped tokens, allowlisted API usage and sandboxing.
4. Deployment & infrastructure
AI in could be deployed across hybrid cloud and containerized environments, creating opportunities for supply chain attacks and misconfigurations.
Purpose: Secure runtime environments and CI/CD pipelines.Example threat: Compromised container images in deployment pipelines.Use case: Backdoored fraud detection images are deployed through an unscanned pipeline.Controls: IaC scanning for PCI compliance, container image signing and hardened Kubernetes.
5. Evaluation & observability
Banking AI must remain accurate, explainable and compliant. Continuous monitoring ensures that models don’t drift into unsafe or discriminatory behavior.
Purpose: Track AI system health and behavior over time.Example threat: Credit scoring model drifts, approving risky borrowers.Use case: Unmonitored drift leads to regulatory penalties for biased lending.Controls: Drift monitoring, explainability logging, anomaly detection on credit decisions.
6. Security & compliance
Banking is one of the most regulated industries. AI must align with global standards like Basel III, PCI DSS, GDPR and the EU AI Act.
Purpose: Ensure regulatory alignment and enterprise security policy enforcement.Example threat: Unauthorized data leakage in AI responses.Use case: A banking chatbot reveals customer account data to the wrong user.Controls: Immutable audit logs, DLP enforcement, redaction guardrails.
7. Agent ecosystem
Banks operate in complex ecosystems with multiple AI agents coordinating across payment networks, partners and fintech APIs. Risks at this layer are systemic.
Purpose: Manage systemic risks across agent interactions.Example threat: Feedback loops between payment and fraud agents.Use case: A fraud agent incorrectly blocks all ACH transactions, halting operations.Controls: Trust boundaries, multi-agent simulations, circuit breakers for payment systems.
Use cases in practice (Banking):
1. Credit scoring and loan approvals
A loan approval model is poisoned by manipulated transaction histories, resulting in biased or inaccurate credit decisions.
Layers: Data Operations, Evaluation & Observability, Security & Compliance.Mitigation: Signed datasets, drift monitoring and regulatory explainability requirements.
2. Fraud detection systems
Fraud models are manipulated by adversarial data, causing missed fraud alerts or false positives.
Layers: Foundation Models, Agent Frameworks, Deployment & Infrastructure.Mitigation: Input validation, scoped agent RBAC and container signing in pipelines.
3. Conversational banking assistants
Virtual assistants are tricked into disclosing sensitive account information.
Layers: Foundation Models, Security & Compliance.Mitigation: Input/output sanitization, DLP policies, compliance guardrails.
4. Payment processing and transaction flows
AI agents managing transactions create a feedback loop, freezing legitimate payments.
Layers: Agent Ecosystem, Agent Frameworks, Evaluation & Observability.Mitigation: Circuit breakers, trust boundaries and simulation of agent interactions.
5. Regulatory compliance reporting
AI systems generating regulatory reports drift over time, producing inaccurate filings.
Layers: Evaluation & Observability, Security & Compliance.Mitigation: Continuous monitoring, immutable audit logs, anomaly detection.
| MAESTRO Layer | Example Threat | Banking Use Case | Key Controls |
| Foundation Models / Core Services | Adversarial prompts alter outputs | Virtual assistant tricked into revealing loan approval rules | Input/output sanitization, API lifecycle mgmt., model watermarking |
| Data Operations | Poisoned transaction data | Fake payment histories bias credit scoring | Data lineage, signed datasets, anomaly detection, schema validation |
| Agent Frameworks / Application Logic | Prompt injection disables fraud monitoring | Fraud detection agent ignores suspicious transactions | Per-agent RBAC, scoped tokens, allowlisted APIs and sandboxing |
| Deployment & Infrastructure | Compromised container images | Backdoored fraud detection models deployed via CI/CD | IaC scanning (PCI), image signing, hardened Kubernetes, egress restrictions |
| Evaluation & Observability | Model drift degrades performance | The credit scoring model starts approving risky borrowers | Drift monitoring, anomaly alerts, explainability logs, test harnesses |
| Security & Compliance | Unauthorized data leakage | Chatbot exposes customer account info | Immutable audit logs, DLP enforcement, automated redaction, compliance guardrails |
| Agent Ecosystem | Feedback loops disrupt workflows | Fraud and payment agents block legitimate ACH transfers | Trust boundaries, dependency mapping, circuit breakers and pre-production simulations |
The infrastructure threat modeling for the AI could be hosted within the on-prem or cloud environment or a SaaS service should be assessed separately as well
A foundation for systemic AI security: AI in organizations is no longer experimental. With agentic AI, organizations face autonomy, unpredictability and systemic risks that traditional frameworks cannot fully address.MAESTRO provides a structured, layered, outcome-driven framework tailored to these realities. By applying it across data, models, infrastructure and ecosystems, organizations can secure and enhance the efficiency, cost and security and resiliency of their businesses.While frameworks like MITRE, OWASP, NIST and ISO add tactical and governance layers, MAESTRO serves as the foundation for systemic AI security in banking.This article is published as part of the Foundry Expert Contributor Network.Want to join?
First seen on csoonline.com
Jump to article: www.csoonline.com/article/4072341/introducing-maestro-a-framework-for-securing-generative-and-agentic-ai.html