MAESTRO: An Agentic AI Threat Modeling Framework

AI is disrupting the security landscape in many ways, and traditional threat models are no longer relevant to modern organizations. 

New threats are emerging from AI systems that learn, adapt, and act autonomously, because legacy security responses are unable to adequately defend against intelligent, evolving, and unpredictable AI-enabled systems.

MAESTRO will introduce a new framework for managing AI security risks, enabling organizations to detect and mitigate AI-specific vulnerabilities, enhance defenses, and quickly respond to dynamically evolving attack strategies, thus empowering organizations with the capabilities to thrive in the new era of AI-enabled security.

How can you effectively understand and
Apply Threat Modeling Fundamentals?

Core Principles of Threat Modeling

Threat modeling can be used to identify threats, and the goal is to reduce the risk. Security is critical because traditional threat modeling approaches are pretty decent. Some old tools are STRIDE, PASTA, and LINDDUN; however, traditional approaches cannot be used for AI because AI is dynamic. 

It is very difficult to perform threat modeling on AI; thus, self-learning AI makes this hard. We need new ways of doing threat modeling for AI; therefore, we must develop new methods to address this challenge.

The Need for AI-Centric Threat Modeling

Traditional security practices are inadequate to analyze AI systems. They are unable to identify adversarial attacks, spot data poisoning, and detect model manipulation.

AI introduces new attack points, such as data pipelines; it creates a new attack points for model updates. Threat modeling must evolve to match the pace of AI; therefore, traditional security practices are capable of analyzing AI systems.

Also read about the Threat Modeling Life Cycle

What are the key differences between traditional and modern threat modeling frameworks?

There are several approaches to threat modeling, and each has its advantages and disadvantages. Each approach is suited to certain tasks better than others, so threat models are not well suited for AI systems. Some common approaches and how they apply to AI are listed below because they are required to understand the limitations of threat modeling in AI systems.

Framework	Strengths	Application Scenarios	Limitations in AI Contexts
STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege)	Clear categorization of threats; widely adopted	Static systems, software development	Struggles with dynamic, autonomous AI behaviors and multi-agent interactions
PASTA (Process for Attack Simulation and Threat Analysis)	Risk-centric, focuses on attacker perspective	Complex systems requiring detailed risk analysis	Difficult to integrate AI-specific threats like model poisoning or learning manipulation
OCTAVE (Operationally Critical Threat, Asset, and Vulnerability Evaluation)	Emphasizes organizational assets and processes	Enterprise-wide security planning	Limited scalability and adaptability for fast-evolving AI environments
VAST (Visual, Agile, and Simple Threat Modeling)	Agile-friendly, strong visualization tools	Agile development teams, rapid iteration	Visualization alone doesn’t address AI-specific threat vectors or model dynamics

This demonstrates that previous approaches are useful as a starting point, and they do not address the emerging and evolving risks facing AI.

What is the MAESTRO Framework, and how does it help with modern threat modeling?

MAESTRO is a novel threat modeling framework, and it was designed for AI-based systems. It is layered and modular; thus, it covers all aspects of an AI agent’s behavior and interaction.

It contrasts with existing frameworks because it addresses risks for AI, and it emphasizes continual monitoring. Likewise, it adapts to shifts in AI agent behavior; therefore, it is a great way to protect the modern, evolving AI systems, so it provides a comprehensive solution for AI security.

MAESTRO Architecture and Components

Breakdown of MAESTRO’s Seven-Layer Architecture: A Deep Dive into Each Layer

Layer 1: Foundation Models – Threats to the Core AI Brain

What it is: The base LLMs (and related foundation models) that provide core reasoning, generation, and tool-usage capabilities to agents. MAESTRO treats this as the “brain” layer because compromise or weakness here propagates upward into every agent behavior (CSA MAESTRO intro).

Why it matters: Agents inherit the model’s vulnerabilities: if the model can be manipulated, extracted, or degraded, the entire system’s trust, safety, and reliability collapse, even when higher layers have controls.

Key threats

• Adversarial examples / evasion inputs: Carefully crafted prompts or inputs that cause unsafe or incorrect outputs (policy bypass, hallucinated actions, tool misuse).
• Model stealing / extraction: Attackers reproduce capabilities via repeated querying or misuse of APIs, undermining IP and enabling offline attacks.
• Backdoor attacks: Hidden triggers embedded via training/fine-tuning or supply chain manipulation that cause targeted malicious behavior when activated.
• Membership inference: The attacker infers whether specific records were in training data (privacy/regulatory exposure).
• DoS “sponge” attacks: Inputs designed to maximize compute (very long contexts, adversarial token patterns), degrading availability and increasing cost.

Mitigations 

Model access controls: strong authN/authZ, per-tenant rate limits, and anomaly-based throttling for “spongey” patterns.

• Output and tool-use constraints: enforce policy and allowlists outside the model (don’t rely on “the model will behave”).
• Red teaming + adversarial testing: continuously probe for bypasses/backdoors, especially after model updates.
• Privacy defenses: data minimization, differential privacy where feasible, and training-data governance to reduce membership inference impact.
• Model provenance & supply chain checks: validate model sources, versions, and artifact integrity.

Layer 2: Data Operations – Securing the AI’s Fuel Supply

What it is: The data plumbing: ingestion pipelines, storage, preprocessing, labeling, embedding generation, vector databases, and RAG retrieval flows. MAESTRO elevates this because agentic systems are often “data-defined”: their behavior is heavily shaped by what they retrieve and remember. 

Why it matters: Even if the foundation model is robust, poisoned or compromised data can “steer” the agent into unsafe decisions, leaks, or persistent malicious behavior.

Key threats

• Data poisoning: Malicious or skewed data introduced into training, fine-tuning, or retrieval corpora to bias outputs or embed triggers.
• RAG pipeline compromise: Attackers manipulate documents, connectors, indexing, or retrieval logic so the agent receives attacker-controlled context (indirect prompt injection via retrieved text is a common mechanism).
• PII exfiltration: Sensitive data leaks through retrieval, logging, embeddings, or tool outputs.
• Embedding poisoning: Adversarial content crafted to dominate similarity search (e.g., high-recall malicious chunks), causing systematic retrieval of attacker content.

Mitigations

• Provenance + integrity controls: signed data sources, checksums, ingestion allowlists, and tamper-evident logs.
• RAG hardening: content sanitization, prompt-injection filtering, retrieval isolation (separate indexes by trust level), and “cite-then-use” policies.
• PII controls: automated detection/redaction, least-privilege access to corpora, and strict retention policies.
• Anomaly detection on embeddings/retrieval: monitor unusual similarity patterns, sudden shifts in top-k sources, and spikes in “new” documents driving behavior.

Layer 3: Agent Frameworks – Protecting the Orchestration Layer

What it is: The orchestration software that wires models to tools and workflows (e.g., LangChain, AutoGen), including planners, routers, tool-call schemas, and agent memory interfaces. This layer often becomes the “control plane” of autonomy.

Why it matters: Many real-world failures happen here: framework defaults, unsafe tool wrappers, weak validation, and hidden agent-to-agent channels can create “no clear trust boundary,” which MAESTRO explicitly calls out as an agentic risk. 

Key threats

• Compromised LangChain/AutoGen components: malicious extensions, vulnerable plugins, or unsafe tool wrappers leading to execution of unintended actions.
• Prompt injection (direct + indirect): attacker instructions hijack the agent’s goals, tool usage, or data access. In agentic systems this becomes more dangerous because the agent can act on the injection, not just respond.
• Supply chain attacks on frameworks: dependency confusion, typosquatting, poisoned packages, compromised CI artifacts.

Mitigations

• Dependency hygiene: SBOMs, pinned versions, signed artifacts, and continuous dependency scanning.
• Tool-call validation gates: schema validation, allowlisted tools/actions, parameter constraints, and human-in-the-loop for high-risk operations.
• Prompt boundary design: separate system policies from retrieved/untrusted content; treat retrieved text as data, not instructions.
• Sandboxing: run tools with least privilege and in constrained execution environments.

Layer 4: Deployment & Infrastructure – Hardening the AI’s Operating Environment

What it is: The runtime environment: cloud accounts, networks, container images, Kubernetes, secrets, IAM, service mesh, and CI/CD that deploys agents and their tool backends. MAESTRO includes this because agent compromise often becomes classic infra compromise and vice versa. 

Why it matters: Infrastructure weaknesses turn agent incidents into full platform breaches (data theft, credential compromise, lateral movement), and infrastructure attackers can also tamper with agent behavior by modifying configs, prompts, or tool endpoints.

Key threats

• Container image tampering: poisoned images, altered base layers, malicious startup scripts.
• Kubernetes orchestration attacks: RBAC abuse, admission controller bypass, malicious sidecars, compromised nodes.
• IaC manipulation: Terraform/Helm/GitOps changes that silently weaken IAM, networking, or secrets handling.
• Lateral movement: compromise of one agent/tool pod leading to broader service access (vector DB, secret stores, CI runners).

Mitigations

• Hardened supply chain: signed images, verified provenance (SLSA-style practices), and restricted registries.
• K8s security baseline: least-privilege RBAC, network policies, pod security standards, runtime security monitoring.
• GitOps/IaC controls: code review enforcement, policy-as-code checks, drift detection.
• Secret isolation: short-lived credentials, per-service identities, and strict egress controls.

Layer 5: Evaluation & Observability – Ensuring You Can See What Your Agent Is Doing

What it is: Telemetry, logging, tracing, eval harnesses, safety monitors, red-team regression suites, and incident forensics. MAESTRO treats this as essential because agentic behavior is non-deterministic; without strong observability, you can’t detect slow-burn attacks or emergent failure modes.

Why it matters: If monitoring is blind or corrupted, you won’t know the agent is being steered, leaking data, or degrading until after impact.

Key threats

• Manipulation of evaluation metrics: gaming safety benchmarks, selective test passing, and biased eval datasets that hide failures.
• Evasion of detection: attackers craft interactions that avoid triggers (e.g., gradual goal steering, low-and-slow exfil).
• Poisoning observability data: tampering with logs/traces, flooding signals, or injecting misleading telemetry to mask real behavior.

Mitigations

• Tamper-evident logging: append-only logs, strong access controls, separation of duties.
• Diverse evals + adversarial suites: evaluate across multiple datasets, threat scenarios, and regression tests after every change.
• Behavioral anomaly detection: monitor tool-call distributions, unusual retrieval sources, and “policy near-misses.”
• Secure telemetry pipelines: authenticated collectors, integrity checks, and rate limits to prevent log DoS.

Layer 6: Security & Compliance (Vertical Layer) – The Cross-Cutting Governance Layer

What it is: A cross-layer set of controls: policy enforcement, risk management, auditability, regulatory alignment, and security automation that applies to every other layer. 

Why it matters: Agentic AI failures often aren’t just “a bug”; they become governance incidents: privacy, model risk, explainability requirements, and accountability.

Key threats

• Security agent data poisoning: If you use AI agents to triage security alerts or enforce policy, attackers may poison their inputs (logs, tickets, KBs) so the security agent makes unsafe decisions.
• Regulatory non-compliance: uncontrolled PII access, weak consent boundaries, missing audit trails, unsafe retention.
• Bias in security AI agents: biased detections or enforcement leading to uneven treatment, false positives/negatives, or discriminatory outcomes.
• Lack of explainability: inability to justify actions/decisions (especially for high-impact workflows) creates audit and safety failures.

Mitigations

• Policy-as-code + approvals: codify allowed tools, data classes, and approval workflows for sensitive actions.
• Audit-ready traceability: “who/what/why” logs across prompts, retrieved sources, tool calls, and outputs.
• Bias and fairness governance: testing, monitoring, and escalation paths when disparate impacts appear.
• Human accountability points: clearly defined responsibility for overrides, incident response, and model updates.

Layer 7: Agent Ecosystem – Securing the Marketplace of Agents

What it is: The “top” environment where agents meet users, other agents, tools, plugins, registries, and marketplaces. This is where multi-agent dynamics, impersonation, and economic manipulation become central. 

Why it matters: Even if a single agent is secure in isolation, the ecosystem introduces emergent risks: unsafe inter-agent delegation, trust confusion, and exploit chains across vendors and capabilities.

Key threats

• Agent impersonation: malicious agents masquerade as trusted ones, capturing credentials, tasks, or payments.
• Marketplace manipulation: fake reviews/ratings, traffic shaping, or collusive agent networks to dominate selection.
• Compromised agent registry: tampered metadata, poisoned discovery results, malicious version updates.
• Pricing model manipulation: token/cost abuse, billing fraud, induced overuse (“make the agent call the tool 10,000 times”), or economic denial of service.

Mitigations

• Strong agent identity: signing, verification, and provenance for agent packages and updates.
• Registry controls: integrity checks, staged rollouts, and transparent versioning.
• Reputation hardening: anti-sybil measures, anomaly detection on ratings/usage, and independent verification.
• Economic guardrails: budgets, per-task quotas, cost-aware planners, and billing anomaly detection.

What are the key advanced considerations in threat modeling agentic AI systems?

Unique Threat Vectors in AI Systems

Model-Specific Threats

There are also some special issues that are unique to AI models, and adversarial attacks can be used to fool the model. Data poisoning can be used to corrupt the model’s training data, so model extraction can be used to steal the logic or IP of the model.

These issues make AI models vulnerable and easy to cheat because they are unique to AI models and can be exploited by various methods; therefore, model extraction and data poisoning are particularly problematic.

AI Pipeline Vulnerabilities

Artificial intelligence systems are under attack, and the attackers may introduce bad data into the training set. The attackers may try to break the model during inference, so they may try to break the AI at its decision-making time.

There are multiple ways that the AI can be attacked; therefore, the attackers may use various methods to achieve their goals. Thus, the attackers may attempt to break the model during inference and at its decision-making time.

Governance and Compliance Challenges

Rules for agentic AI are not clear yet, and they must be ethical. They must respect the law on data and transparency because it should work on bias and fairness. This is to avoid unfair or bad results; thus, it is a crucial step in the development of agentic AI.

MAESTRO’s Approach to AI System Protection

• MAESTRO makes AI safer, and this is achieved by identifying vulnerabilities. It can quickly identify new vulnerabilities, and it can identify which vulnerabilities are the most harmful.
•  It then prioritizes these, and this is a time- and cost-efficient approach; therefore, MAESTRO defends against AI attacks. 
• The defense capability improves with age; thus, it monitors for threats constantly. This ensures that the AI remains protected, no matter how it evolves, because it is constantly being monitored.

Also read about Threat Modeling Vs Penetration Testing

How have enterprises successfully implemented modern threat modeling frameworks like MAESTRO?

Robotic Process Automation (RPA) in Enterprises:
MAESTRO was applied to AI systems exhibiting human-like behavior, and, for instance, it was applied to reimbursement programs that pay back workers’ money spent. In this case, MAESTRO detected vulnerabilities that were not identified by other tools; therefore, this enabled the companies to ensure the security of their reimbursement programs.

OpenAI API Security:
Teams have utilized MAESTRO against agentic AI APIs, such as OpenAI’s Responses API, to detect and help mitigate risks at all stages of the AI lifecycle, from data handling to deployment and regulatory compliance, and this utilization has been crucial for ensuring the safe and responsible development of AI systems.

AI Security Operations Centers (SOCs):
MAESTRO is used for AI-driven SOCs, and it performs dynamic and on-demand risk discovery and management. AI is employed, such as for threat triage and remediation, because it enables the system to respond quickly to new threats. 

The examples demonstrate how MAESTRO can detect threats against AI, thus providing a layer of security for the system.

It continuously monitors risks, therefore allowing for real-time mitigation of potential threats. It enables safe deployment of agentic AI in practical business environments, so businesses can leverage the power of AI while minimizing risks.

A SAP advisor, Helen Oakley stated 

An example of a new risk is goal manipulation, as described in the OWASP guide, and in agentic systems, attackers can slowly influence how an agent behaves by subtly manipulating its goals over time. 

Threat modeling helps you understand how agents form and update goals, how memory and feedback loops reinforce undesired behaviors, and how downstream actions are influenced, and it helps you design targeted mitigations.

Security can be enhanced, and goal-setting logic can be isolated from external data. Changes in behavior can be monitored, and human supervision can be applied to critical decisions.

For example, an agent that determines which financial transactions are significant could be manipulated, so attackers could persuade the agent to approve harmful payments that violate the rules using either indirect prompt injection, adversarial data, or synthetic tool outputs. 

These attacks can be used to gradually shift an agent’s beliefs and behavior; therefore, this can lead to it making poor decisions.

What are the gaps in existing frameworks for Agentic AI Threat Modeling in 2025?

The introduction of agentic AI systems, which are autonomous agents with the ability to reason, plan, and take action independent of human intervention, has brought down the traditional cybersecurity frameworks.

Research and industry analysis reveal the areas of major shortcomings of our current threat modeling frameworks when applied to these advanced forms of autonomous systems in 2025.

Core Framework Inadequacies

Well-established threat modeling frameworks such as STRIDE, PASTA, LINDDUN, and OCTAVE were developed to be used for static and predictable systems under the oversight of the human economy. There are inherent limitations of these frameworks in reference to agentic AI.

STRIDE Framework Gaps:
While STRIDE provides a solid category to understand spoofing, tampering, and privilege escalation, it does not provide specific direction around AI-based threats, such as adversarial machine learning, data poisoning, and goal misalignment. Additionally, the framework cannot describe the spontaneous nature of the actions of autonomous agents and their decision-making capabilities.

PASTA and LINDDUN Limitations:
PASTA’s risk-centric approach implicitly presupposes static modes of risk assessment that cannot encompass the dynamic risk-learning behavior of AI agents. Similarly, LINDDUN’s privacy focus is useful, but it cannot address the risks associated with agent-to-agent communication and dynamic consent management related to autonomous systems.

The introduction of agentic AI systems, which are autonomous agents with the ability to reason, plan, and take action independent of human intervention, has brought down the traditional cybersecurity frameworks.

Research and industry analysis reveals the areas of major shortcomings of our current threat modeling frameworks when applied to these advanced forms of autonomous systems in 2025.

Core Framework Inadequacies
Well-established threat modeling frameworks such as STRIDE, PASTA, LINDDUN, and OCTAVE were developed to be used for static and predictable systems under the oversight of the human economy. There are inherent limitations of these frameworks in reference to agentic AI.

STRIDE Framework Gaps:
While STRIDE provides a solid category to understand spoofing, tampering, and privilege escalation, it does not provide specific direction around AI-based threats, such as adversarial machine learning, data poisoning, and goal misalignment. Additionally, the framework cannot describe the spontaneous nature of the actions of autonomous agents and their decision-making capabilities.

PASTA and LINDDUN Limitations:
PASTA’s risk-centric approach implicitly presupposes static modes of risk assessment that cannot encompass the dynamic risk-learning behavior of AI agents. Similarly, LINDDUN’s privacy focus is useful, but it cannot address the risks associated with agent-to-agent communication and dynamic consent management related to autonomous systems.

Autonomy-Related Gaps

Unpredictable Agent Behavior
Traditional frameworks have difficulty characterizing the non-deterministic nature of agentic AI systems. Traditional applications involve predictable control flows in code; AI agents might demonstrate complex, emergent behaviors, apart from what you could imagine and describe in threat modeling in advance.

Unpredictability of agents: Frameworks cannot adequately characterize the potential threats due to independent decisions made by agents.
Goal misalignment: There is no ability to leverage frameworks to characterize risks of an agent’s intended purposes falling out-of-alignment with what is intended.
Dynamic learning: Static threat models do not apply to agents that learn and modify their behavior after deployment.

Multi-Agent Complexity

The switch from single-agent systems to multi-agent systems (MAS) brings about a new level of complexity that existing frameworks cannot support. These systems fall under.

Inter-Agent Communication: Multiple agents will add complexity to the communication between agents, which creates new opportunities for attacks
Emergent System Behaviors: With the interaction between multiple agents, the effect can be system-wide and totally unpredictable.
Cascading Failures: If one agent is compromised, this can cascade throughout the network of agents.

Machine Learning-Specific Gaps

AI-Centric Threat Vectors
Current frameworks do not adequately address AI-specific attacks that are unique to machine learning systems. For example:

Adversarial Machine Learning: Current frameworks do not offer frameworks or guidance for attacks against ML in general, and there are no attacks targeting ML models, i.e.,

• Data Poisoning: contaminating training data to poison agent behavior
• Evasion Attacks: designing inputs to gain the agent’s trust
• Model Extraction: using API calls to steal underlying models
• Prompt Injection: manipulatively altering an agent prompt by entering a specific input value

Memory Poisoning: A chief threat associated with agentic AI. Unlike traditional systems, where there is little to no reliance on persistent memory, an attacker can modify an agent’s memory persistence to corrupt its decision-making process. This threat does not seem to exist for traditional systems in the same way and is also not considered in the frameworks described.

Interaction-Based Gaps

Agent-to-Agent Communications:

As standardized protocols like Model Context Protocol (MCP) and Agent2Agent (A2A) began to proliferate, they produced new interaction paradigms that traditional protocols can’t evaluate:

Agent Communication Poisoning: A malicious agent injects bogus data into inter-agent communications.
Trust Exploitation: Trust relationships established by a thread of legitimate agents are now being manipulated by malicious agents.
Negotiation Hijacking: A coordinated protocol between agents can be manipulated by a malicious agent.

Human-Agent Interaction Risks

Agentic AI systems represent new threats to human-computer interaction not contemplated in prior frameworks:

Manipulating Human Trust: Agents taking advantage of human trust with conversational interfaces.
Overwhelm Human-in-the-Loop (HITL): Send more complicated interactions than humans can even read.
Confusion of Authority: Related to the hindsight extremities of humans is a lack of accountability, wherein agents are making decisions on behalf of humans.

System-Level Gaps

Explainability and Auditability

Conventional audit and compliance procedures presume decision pathways are human-readable. Agentic AI systems challenge this presumption:

Opaque Decision-Making – AI reasoning is often complicated to articulate or audit.
Non-Linear Execution – Agents can execute multiple, simultaneous, parallel reasoning paths.
Dynamic Goal Setting – Agents can change goals during execution.

Supply Chain Security

Agentic AI systems rely on complex supply chains of models, datasets, and frameworks that are inadequately covered by traditional security frameworks.

Compromised Pre-trained Models: Risks from using third-party foundation models.
ML Library Vulnerabilities: Security risks in machine learning libraries.
Training Data Provenance: No tracking of data used to train the agent.

Governance and Compliance Gaps

Regulatory Framework Inadequacy

Current regulations reflect the world of decision-makers instead of autonomous systems.

Policy Governance: Organizations do not have policies or accountability frameworks regarding agent- behavior. Role-based access control does not account for systems that can autonomously delegate tasks and escalate permissions.

Compliance Problems: Regulatory compliance issues, which were already complex, become astronomically more complex because AI systems can automate thousands of actions per day, and such actions are taken without human review. 

Traditional compliance approaches provide tools for periodic review and audits, and approval workflows do not work in the context of real-time autonomous actions.

How to embed MAESTRO into CI/CD pipelines

To embed MAESTRO into CI/CD pipelines, organizations should integrate automated scanning tools, such as the MAESTRO Threat Analyzer or TITO, directly into the build and deployment stages. These tools can be configured to perform layer-by-layer security checks, ranging from foundation model provenance and dependency scanning for agent frameworks to infrastructure-as-code (IaC) validation. 

By treating the MAESTRO layers as a checklist for automated unit and integration tests, security teams can ensure that every code change or model update is evaluated for agent-specific risks like prompt injection vulnerabilities or unsafe tool-call schemas before reaching production.

Beyond static analysis, the pipeline should include automated “AI Red Teaming” and regression testing suites that simulate adversarial attacks across the seven layers. This involves triggering dynamic evaluation harnesses that probe the agent’s reasoning and planning logic for goal misalignment or data poisoning vulnerabilities. 

By failing builds that exceed risk thresholds or deviate from established safety benchmarks, developers can maintain a continuous security posture that adapts to the non-deterministic nature of agentic AI, ensuring that observability and compliance controls are verified at every release.

What strategic AI security skills does the Certified AI Security Professional course teach?

The CAISP course trains security practitioners on how to secure AI against emerging threats as well as regulatory compliance, and it trains you on the following:

• Implement MITRE ATLAS and OWASP Top 10 LLM in hands-on labs so you can gain practical experience.
• Implement robust defenses such as model signing, model scanning, and pipeline security, because these are essential for securing AI systems.
• Apply STRIDE to identify and mitigate threats, thus enabling you to protect your AI models.
• Secure CI/CD pipelines against tampering, therefore ensuring the integrity of your AI systems.
• Defend production against data poisoning, model theft, and evasion attacks, and comply with relevant regulations.
• Comply with ISO/IEC 42001 and the EU AI Act, because regulatory compliance is crucial for AI development.

Conclusion

As AI becomes increasingly intelligent and autonomous, traditional methods of securing AI are no longer effective, and MAESTRO provides a significant advance towards the security of AI by a novel approach of securing AI in a multi-layered and dynamic manner. 

New approaches are required to secure AI against attacks from adversaries, so MAESTRO secures AI models, data, and AI-to-AI communications against adversarial attacks.

Ready to learn next-generation AI threat modeling? The Certified AI Security Professional Course equips you with frameworks (STRIDE, BIML Risk, MITRE ATLAS,and PASTA) ensuring your team can protect modern AI-driven systems effectively.

Also read about Threat Modeling Data Flow Diagrams

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