09 AI Security Roles and Responsibilities

Securing enterprise AI is not the responsibility of one person or one team.

Modern AI systems combine:

Applications
Models
Enterprise data
Cloud infrastructure
APIs
RAG
Vector databases
AI agents
Identity
Security monitoring
Governance

Because these components span multiple technology and business domains, AI security requires shared responsibility.

An AI Security Engineer is an important part of this model, but cannot secure the complete environment alone.

This lesson explains:

Who is involved
What each team is responsible for
Where responsibilities overlap
How teams collaborate
How security ownership changes throughout the AI lifecycle

The objective is to help you understand how AI security works inside a real organization.

Learning Objectives

By the end of this lesson, you should be able to:

Explain the role of an AI Security Engineer.
Understand how AI Security differs from AI development and governance.
Identify the teams involved in enterprise AI security.
Understand responsibilities across the AI lifecycle.
Recognize shared responsibility between technical and business teams.
Understand where AppSec, Cloud Security, SOC and GRC contribute.
Understand the role of AI Red Teams.
Understand how privacy and data teams support AI security.
Use a simple RACI model to define ownership.
Understand how AI security teams collaborate during real projects.

Why Shared Responsibility Matters

Consider a modern enterprise AI assistant:

Employees
   │
   ▼
AI Application
   │
   ├── Identity Provider
   ├── LLM
   ├── RAG
   │     └── Enterprise Documents
   │
   ├── AI Agent
   │     └── Cloud APIs
   │
   └── Logging

Who secures this?

The answer is not simply:

The AI Security Team.

Different components may be owned by different teams.

For example:

Identity
    ↓
IAM Team

Application
    ↓
Development Team

Cloud Infrastructure
    ↓
Cloud Platform Team

Model
    ↓
AI / ML Team

Enterprise Data
    ↓
Data Team

Security Architecture
    ↓
AI Security / Security Architecture

Detection
    ↓
SOC

Privacy
    ↓
Privacy Team

Risk
    ↓
GRC

Business Decision
    ↓
Business Owner

Security succeeds when these responsibilities work together.

The AI Security Engineer

The AI Security Engineer helps organizations securely design, deploy and operate AI-enabled systems.

The role sits between several disciplines:

                AI / ML
                  │
                  │
Cloud ───── AI Security ───── Application Security
                  │
                  │
          Cybersecurity
                  │
                  │
         Data / Governance

The AI Security Engineer needs enough understanding of each area to identify security risks and coordinate appropriate controls.

Core Responsibilities of an AI Security Engineer

Typical responsibilities may include:

AI architecture security reviews
AI threat modeling
LLM security assessments
RAG security reviews
AI agent security
Cloud AI security
Identity and permission reviews
AI red team coordination
Security requirement definition
AI security testing
Monitoring requirement definition
Security findings and remediation
AI incident investigation support
Security standards development

The exact responsibilities vary by organization.

AI Security Engineer — Design Responsibilities

Before an AI system is built, the AI Security Engineer may help define:

Security architecture
Identity requirements
Data protection requirements
Logging requirements
Agent permission boundaries
RAG authorization requirements
Model security requirements

A useful model is:

Business Requirement
      ↓
Architecture
      ↓
AI Security Review
      ↓
Security Requirements
      ↓
Development

Security should influence design before implementation.

AI Security Engineer — Threat Modeling Responsibilities

The AI Security Engineer may lead or support threat modeling.

This includes:

Identify Assets
      ↓
Map Data Flows
      ↓
Identify Trust Boundaries
      ↓
Identify Threat Actors
      ↓
Build Attack Paths
      ↓
Define Controls

Frameworks such as:

OWASP GenAI guidance
MITRE ATLAS
Traditional threat modeling approaches

may help structure the process.

AI Security Engineer — Testing Responsibilities

Security testing may include:

Prompt injection testing
RAG authorization testing
Agent permission testing
API security testing
Infrastructure configuration review
Sensitive data exposure testing
Model and supply-chain review

Testing should always occur within authorized scope.

AI Security Engineer — Operational Responsibilities

Once an AI application reaches production, the AI Security Engineer may support:

Security monitoring design
Detection engineering
AI incident response
Agent activity review
Security control tuning
Vulnerability remediation
Threat intelligence analysis

The role therefore spans the full lifecycle.

AI Developer / Application Developer

AI applications are often built by software developers.

Developers may be responsible for:

Application code
LLM integration
Prompt construction
API integration
Input handling
Output handling
Session management
RAG integration
Agent workflows

Developer Security Responsibilities

Developers should implement:

Secure coding
Authentication integration
Authorization enforcement
Input validation
Output validation
Secure error handling
Dependency management
Secrets management

Important principle:

AI security cannot be added entirely by the security team after development.

Developers must implement the controls.

Example

Suppose security identifies:

Users should only retrieve documents they are authorized to access.

The AI Security Engineer defines the requirement.

The developer may need to implement:

User Identity
      ↓
Authorization Check
      ↓
RAG Query
      ↓
Authorized Documents Only

This demonstrates shared responsibility.

Machine Learning Engineer

ML Engineers build and operate Machine Learning systems.

Responsibilities may include:

Model training
Fine-tuning
Model evaluation
Data pipelines
Model deployment
Model monitoring
Model optimization

ML Security Responsibilities

ML Engineers may help protect:

Training environments
Models
Model registries
Training pipelines
Evaluation pipelines
Dataset integrity

Example:

Dataset
   ↓
Training
   ↓
Model
   ↓
Model Registry
   ↓
Deployment

Security controls should exist throughout this process.

AI / Data Scientist

Data Scientists may focus on:

Model experimentation
Dataset analysis
Model evaluation
Feature engineering
AI research

Their security responsibilities may include:

Protecting sensitive datasets
Using approved models
Maintaining data provenance
Avoiding insecure experimentation
Following organizational AI policies

AI security teams should work with Data Scientists early rather than only reviewing final deployments.

AI Platform Team

Larger organizations may operate centralized AI platforms.

The platform team may provide:

Model access
AI APIs
RAG services
Vector databases
Model routing
AI development environments
Agent frameworks

The AI platform team often becomes one of the most important security partners.

Platform Security Responsibilities

They may be responsible for:

Platform IAM
Tenant isolation
Model access controls
Platform logging
Secure defaults
Network boundaries
Secret integration

A strong AI platform can provide reusable security controls to multiple application teams.

Cloud Platform Team

Many enterprise AI workloads run in cloud environments.

The Cloud Platform Team may be responsible for:

Cloud accounts
Networking
IAM foundations
Logging
Encryption
Cloud security services
Kubernetes
Container infrastructure

Example

AI Application
      ↓
Cloud Platform
      │
      ├── IAM
      ├── Network
      ├── Storage
      ├── Kubernetes
      └── Logging

If cloud foundations are insecure, AI controls alone cannot protect the system.

Cloud Security Team

Cloud Security Engineers may focus on:

IAM
Network security
Cloud configuration
Workload security
Logging
Cloud detection
Kubernetes security

The AI Security Engineer and Cloud Security Engineer may collaborate closely.

For example:

AI Security Engineer
       │
       ├── Defines Agent Security Requirements
       │
       ▼
Cloud Security Engineer
       │
       └── Implements Restricted Cloud Role

This is a good example of specialization working together.

Identity and Access Management Team

Identity is central to AI security.

The IAM team may manage:

SSO
MFA
User identities
Service accounts
Workload identities
RBAC
Privileged access

AI-Specific IAM Questions

The IAM team may work with AI Security to answer:

Who can access the AI platform?
What can each user retrieve?
What identity does the AI agent use?
What permissions does the agent have?
How are model API credentials managed?

AI systems should integrate with enterprise identity rather than creating unmanaged identity islands.

Application Security Team

AppSec teams already protect:

Web applications
APIs
Authentication
Authorization
Secure development
Software dependencies

These skills remain critical in AI applications.

AppSec + AI Security

Think of the relationship as:

Application Security
      +
AI-Specific Security
      =
Secure AI Application

AppSec may identify:

Broken access control
Injection
API weaknesses

AI Security may identify:

Prompt injection
RAG risks
Agent abuse

Both perspectives are necessary.

DevSecOps Team

DevSecOps integrates security into development and deployment.

Responsibilities may include:

CI/CD security
Infrastructure as Code security
Secret scanning
Dependency scanning
Container scanning
Policy-as-code

For AI systems, DevSecOps may also support:

Model deployment pipelines
Model artifact controls
AI infrastructure deployments

Example:

Developer
   ↓
Source Code
   ↓
CI/CD
   ↓
Security Checks
   ↓
AI Application
   ↓
Production

MLOps Team

MLOps focuses specifically on Machine Learning operations.

Responsibilities may include:

Training pipelines
Model registries
Model promotion
Model deployment
Monitoring
Retraining

Security teams should work with MLOps to protect the AI lifecycle.

Example

Dataset
   ↓
Training
   ↓
Evaluation
   ↓
Model Registry
   ↓
Production

Questions include:

Who can publish models?
Can models be replaced?
Is integrity validated?
Can the system roll back?

Data Engineering Team

Data engineers build pipelines that move and transform data.

Responsibilities may include:

Data ingestion
Processing
Transformation
Data storage
Data pipelines

AI Security depends heavily on data integrity.

If data pipelines are compromised:

Trusted Source
     ↓
Compromised Pipeline
     ↓
AI Dataset

the downstream AI system may also be affected.

Data Security Team

Data Security focuses on protecting:

Sensitive data
Access controls
Encryption
Data classification
Data Loss Prevention

In AI systems, the team may help determine:

What data can enter AI?

What data can leave AI?

What data can be stored?

Who can retrieve it?

This becomes particularly important with RAG and Generative AI.

Data Governance Team

Data Governance defines how organizational information should be managed.

Responsibilities may include:

Data ownership
Classification
Data quality
Retention
Approved usage
Metadata

AI systems should not ignore existing data governance.

For example:

Restricted Data
      ↓
Enterprise AI

should trigger specific requirements.

Privacy Team

AI applications may process personal information.

Privacy professionals may evaluate:

Personal data usage
Consent
Data minimization
Data retention
Cross-border transfers
Third-party processing

Privacy Questions

They may ask:

What personal data enters the model?

Is it necessary?

Where is it stored?

How long is it retained?

Is it sent externally?

AI Security and privacy frequently overlap.

Security Operations Centre

The SOC is responsible for security monitoring and investigation.

AI systems create new telemetry sources.

Examples include:

Model API logs
Prompt metadata
Agent activity
RAG retrieval
Cloud activity
Administrative changes

The SOC needs enough visibility to identify suspicious behavior.

SOC Responsibilities for AI

SOC teams may:

Monitor AI security alerts
Investigate suspicious usage
Correlate AI events with identity events
Investigate agent actions
Support incident response

Example:

Suspicious AI Activity
      ↓
SIEM Alert
      ↓
SOC Analyst
      ↓
Investigation

Detection Engineering Team

Detection engineers design rules and analytics.

For AI systems they may develop detections for:

Unusual AI usage
Abnormal retrieval
Sensitive tool invocation
Model registry modification
Large-scale API usage
Agent permission failures

This is an emerging area of AI security operations.

Incident Response Team

The Incident Response team handles security incidents.

AI incidents may involve:

Data leakage
Compromised AI credentials
Malicious agent actions
Model tampering
RAG poisoning
Supply-chain compromise

The response process may look like:

Detect
  ↓
Validate
  ↓
Contain
  ↓
Investigate
  ↓
Recover
  ↓
Improve

However, AI-specific evidence may also be required.

AI Incident Evidence

Investigators may need:

User prompts
Model responses
RAG retrieval records
Agent decisions
Tool calls
Model versions
Prompt versions
Cloud logs

AI Security Engineers can help IR teams understand this new evidence.

AI Red Team

AI Red Teams perform authorized adversarial testing.

They may test:

Prompt injection
Indirect prompt injection
Data exposure
Agent manipulation
Tool abuse
RAG security
Model behavior

Their objective is to identify weaknesses before real adversaries do.

AI Red Team vs AI Security Engineer

AI Red Team:

How can we break this?

AI Security Engineer:

How should we design, protect,
monitor and improve this?

The roles overlap but have different emphasis.

Penetration Testing Team

Traditional penetration testers may assess:

Web applications
APIs
Cloud infrastructure
Networks

AI systems still require these assessments.

A complete engagement may combine:

Application Pentest
       +
Cloud Assessment
       +
AI Red Teaming

This gives broader coverage.

Threat Intelligence Team

Threat Intelligence teams monitor adversary behavior and emerging threats.

For AI environments they may track:

AI attack techniques
New vulnerabilities
Supply-chain threats
Threat actor activity
Model-related security research

MITRE ATLAS may help structure AI threat intelligence.

Security Architecture Team

Security Architects help define enterprise security design.

Responsibilities may include:

Security patterns
Technology standards
Zero Trust
Identity architecture
Network architecture
Cloud architecture

AI Security Engineers may work closely with Security Architects.

For example:

AI Use Case
   ↓
AI Security Engineer
   ↓
AI Threat Model
   ↓
Security Architect
   ↓
Enterprise Security Pattern

AI Security Architect

In mature organizations, an AI Security Architect may focus on broader strategic design.

Responsibilities may include:

Enterprise AI security architecture
Approved security patterns
AI platform architecture
Cross-cloud AI security
Security standards
AI Zero Trust architecture

This can be a natural career progression from AI Security Engineer.

GRC Team

Governance, Risk and Compliance teams help organizations manage:

Risk
Policies
Control frameworks
Compliance
Audits
Evidence

AI introduces new governance requirements.

GRC may ask:

Has the AI system been risk assessed?
Are required controls implemented?
Is evidence available?
Are vendors assessed?

AI Governance Team

Some organizations establish dedicated AI Governance functions.

Responsibilities may include:

Approved AI use cases
Responsible AI policy
Model governance
AI risk classification
AI inventory
AI accountability

AI Governance often coordinates across:

Business
+
AI
+
Security
+
Privacy
+
Risk
+
Legal

Legal Team

Legal teams may help assess:

Contracts
Licensing
Intellectual property
Regulatory requirements
Third-party AI terms

For example:

Can this data legally be processed by this external AI provider?

This is not purely a security question.

Procurement / Vendor Risk Team

Organizations frequently adopt external AI services.

Procurement and Vendor Risk teams may evaluate:

Vendor security
Contract terms
Data handling
Service availability
Subprocessors

AI Security Engineers may contribute technical assessment questions.

Business Owner

Every enterprise AI system should have a business owner.

The business owner should understand:

Why the system exists
What business process it supports
What risks may be acceptable
What impact failure may create

Security teams advise.

Business owners often make or accept business risk decisions.

Product Owner

The product owner may manage:

Features
Requirements
Priorities
Release planning

Security requirements should become part of the product backlog.

For example:

Feature:
AI can query HR documents

Security Requirement:
Retrieval must enforce user authorization

Security becomes part of development rather than a separate afterthought.

End Users

Users also have responsibilities.

They should understand:

Approved AI usage
Data handling rules
Sensitive information restrictions
How to report unusual behavior

Security controls should not rely entirely on users, but awareness remains important.

Executive Leadership

Leadership sets priorities and risk appetite.

Leadership responsibilities may include:

AI security investment
Governance sponsorship
Risk acceptance
Security accountability

Without executive support, enterprise AI security programs may struggle to enforce consistent controls.

Shared Responsibility Across the AI Lifecycle

Now let’s map responsibility across the lifecycle.

Plan
 ↓
Design
 ↓
Develop
 ↓
Test
 ↓
Deploy
 ↓
Operate
 ↓
Monitor
 ↓
Respond

Planning Stage

Typical participants:

Business Owner
Product Owner
AI Team
AI Governance
Security
Privacy
GRC

Responsibilities include:

Define use case
Identify data
Classify risk
Identify regulatory requirements

Design Stage

Participants:

AI Security Engineer
AI Architect
Security Architect
Cloud Architect
Development Team
Data Team

Activities include:

Architecture review
Threat modeling
IAM design
Data flow analysis
Security control definition

Development Stage

Participants:

Developers
ML Engineers
MLOps
DevSecOps

Activities include:

Secure coding
Model development
Dependency management
Secrets management
Secure pipelines

Testing Stage

Participants:

AI Security
AppSec
Penetration Testers
AI Red Team

Activities include:

Application testing
Cloud security testing
LLM testing
RAG testing
Agent testing

Deployment Stage

Participants:

DevOps
MLOps
Cloud Platform
Security

Activities include:

Secure deployment
Access configuration
Logging
Monitoring
Production validation

Operational Stage

Participants:

Application Team
AI Platform Team
Cloud Operations
Security Operations

Activities include:

Availability
Maintenance
Security monitoring
Vulnerability remediation

Incident Stage

Participants:

SOC
Incident Response
AI Security
Application Team
Cloud Team
Privacy
Legal

Activities include:

Detect
 ↓
Contain
 ↓
Investigate
 ↓
Recover
 ↓
Report

RACI Model

Organizations often use a RACI model.

RACI stands for:

Responsible
Accountable
Consulted
Informed

Responsible

The team performing the activity.

Accountable

The person ultimately accountable for the outcome.

Consulted

People providing expertise.

Informed

People who need visibility.

Example AI RACI

Activity	AI Security	Developer	Cloud	Data	SOC	Business
Architecture Review	R	C	C	C	I	A
Threat Modeling	R	C	C	C	C	I
Application Development	C	R	I	C	I	A
Cloud Configuration	C	I	R	I	C	I
Data Classification	C	I	I	R	I	A
AI Security Testing	R	C	C	C	I	I
Monitoring	C	I	C	I	R	I
Incident Response	C	C	C	C	R	A

This is only an example.

Actual responsibility structures differ between organizations.

Why RACI Matters

Without defined ownership, organizations often experience situations such as:

Security:
"We thought developers were responsible."

Developers:
"We thought the platform team handled it."

Platform Team:
"We assumed security reviewed it."

The result:

Security Gap

Clear ownership helps prevent this.

Enterprise Example — Building an AI Assistant

Imagine an organization wants to deploy an AI assistant for cloud engineers.

Business Team

Defines:

The assistant should help engineers
understand cloud configuration.

Application Team

Builds:

Interface
Backend
LLM integration

AI Platform Team

Provides:

Approved model
RAG platform

Cloud Team

Provides:

Cloud environment
IAM
Networking

AI Security Engineer

Performs:

Architecture review
Threat modeling
Agent permission review
Security requirements

AppSec

Reviews:

Web application
APIs
Authentication

Data Team

Defines:

Approved knowledge sources
Classification

SOC

Defines:

Logging
Detection requirements

GRC

Evaluates:

Risk
Policy requirements

This is how enterprise AI security becomes a collaborative process.

Example Security Issue

During threat modeling, the AI Security Engineer discovers:

AI Agent
    ↓
Cloud Administrator Permission

The assistant only needs to:

Read Configuration

Who fixes it?

AI Security Engineer

Identifies the risk.

Cloud Security

Designs the restricted role.

Cloud Platform Team

Implements the role.

Application Team

Updates the application to use the identity.

SOC

Monitors usage.

This demonstrates real-world collaboration.

Responsibility for Prompt Injection

Who owns prompt injection risk?

Not one team.

AI Developer

Implements prompt and application controls.

AI Security

Defines security requirements and testing.

IAM Team

Limits permissions.

Data Team

Controls retrieved data.

Agent Team

Limits tools.

SOC

Monitors suspicious behavior.

Prompt injection risk is managed through defense in depth.

Responsibility for RAG Security

RAG security may involve:

Data Team
    ↓
Controls Document Sources

IAM
    ↓
Defines Access

Application Team
    ↓
Implements Authorization

AI Platform
    ↓
Provides Retrieval

AI Security
    ↓
Reviews Architecture & Tests

SOC
    ↓
Monitors Retrieval

This is why AI Security requires cross-functional thinking.

Responsibility for AI Agent Security

Agent security may involve:

Application Team

Defines agent workflows.

AI Security

Reviews abuse cases.

IAM / Cloud Security

Restricts tool permissions.

Business Owner

Defines which actions should be allowed.

SOC

Monitors tool activity.

For high-risk actions:

Agent
  ↓
Proposed Action
  ↓
Human Approval
  ↓
Execution

Human approval may be an explicit business requirement.

Responsibility for AI Supply Chain

Supply-chain security may involve:

AI / ML Team

Chooses models.

DevSecOps

Scans software dependencies.

MLOps

Protects model pipelines.

AI Security

Defines provenance and integrity requirements.

Vendor Risk

Assesses external providers.

This requires coordinated controls.

Responsibility During AI Incident Response

Imagine an AI agent performs an unauthorized cloud action.

Response may require:

SOC
 ↓
Detects Alert

Incident Response
 ↓
Coordinates Investigation

AI Security
 ↓
Analyzes AI Context

Cloud Team
 ↓
Analyzes Cloud Activity

Application Team
 ↓
Reviews Agent Workflow

IAM
 ↓
Revokes Credential

Business Owner
 ↓
Evaluates Operational Impact

AI incidents require multi-team response.

AI Security Operating Model

A mature operating model may look like:

                 Executive Leadership
                         │
                         ▼
                    AI Governance
                         │
      ┌──────────────────┼──────────────────┐
      │                  │                  │
      ▼                  ▼                  ▼
AI Engineering      AI Security         Risk / Privacy
      │                  │
      └─────────┬────────┘
                ▼
          AI Platform
                │
        ┌───────┼────────┐
        │       │        │
        ▼       ▼        ▼
      Apps     Data     Cloud
                │
                ▼
          Security Operations

The exact organization may differ.

The important point is:

AI Security must be embedded across the operating model.

Centralized vs Federated AI Security

Organizations may structure AI Security differently.

Centralized Model

One central AI Security team reviews all projects.

Advantages:

Consistent standards
Deep expertise
Central governance

Challenges:

May become a bottleneck

Federated Model

Security responsibilities are distributed across teams.

Advantages:

Scales more easily
Security is closer to engineering

Challenges:

Standards may become inconsistent

Hybrid Model

A common approach:

Central AI Security Team
        ↓
Defines Standards & Patterns
        ↓
Security Champions
        ↓
Application / AI Teams

This combines centralized expertise with distributed execution.

Security Champions

Security Champions are engineers inside development or AI teams who have additional security knowledge.

They may help:

Apply security standards
Identify issues early
Coordinate with security teams
Improve secure development practices

This helps scale AI security.

AI Security Policies vs Engineering Controls

Policies say:

What should happen.

Engineering controls enforce:

What actually can happen.

Example policy:

AI agents must follow least privilege.

Engineering control:

Agent Service Account
      ↓
Read-Only Role

Both are required.

Responsibility for Risk Acceptance

Security engineers identify and communicate risk.

But security teams do not always own business risk.

For example:

Security Finding
      ↓
Risk Analysis
      ↓
Business Owner
      ↓
Remediate or Accept Risk

Risk acceptance should be formally documented where appropriate.

Accountability Does Not Mean Blame

The purpose of defining responsibility is not to assign blame.

It is to ensure that:

Tasks have owners.
Controls are maintained.
Risks are visible.
Incidents can be managed.
Security does not fall between teams.

Clear ownership improves engineering.

AI Security Engineer Collaboration Skills

Technical knowledge is only part of the role.

You must also be able to communicate with:

Developers
Cloud Engineers
Data Scientists
ML Engineers
Security Analysts
Risk Teams
Business Leaders

You should be able to explain the same security issue differently depending on the audience.

Communicating With Developers

Focus on:

Technical weakness
Reproduction
Recommended implementation

Example:

The RAG query does not include the user’s authorization context, allowing documents outside the user’s permitted scope to be retrieved.

Communicating With Business

Focus on:

Business risk
Data impact
Operational impact

Example:

Employees may be able to retrieve confidential HR information through the AI assistant.

Same risk.

Different communication.

Communicating With SOC

Focus on:

Telemetry
Detection
Investigation

Example:

We need to log the authenticated user, retrieved document set and agent tool invocation so unauthorized retrieval can be investigated.

Communicating With GRC

Focus on:

Controls
Evidence
Risk
Policy

Example:

The current architecture does not enforce documented access-control requirements for restricted data.

This communication skill is extremely important.

AI Security Responsibility Checklist

When reviewing a new AI project, identify ownership for:

Business

Business Owner
Product Owner

AI

AI / ML Team
AI Platform Team
MLOps

Application

Development Team
AppSec

Infrastructure

Cloud Platform
Cloud Security
IAM

Data

Data Owner
Data Engineering
Data Security
Data Governance

Security

AI Security
Security Architecture
AI Red Team

Operations

SOC
Incident Response
Detection Engineering

Governance

GRC
Privacy
AI Governance
Legal
Vendor Risk

If responsibility cannot be identified, that itself may represent an operational risk.

AI Security Project Responsibility Checklist

For each project ask:

Who owns the system?

Who owns the data?

Who owns the model?

Who owns the cloud environment?

Who manages identities?

Who reviews security?

Who performs testing?

Who monitors it?

Who responds to incidents?

Who accepts residual risk?

These questions should have clear answers before production deployment.

Common Beginner Mistakes

Mistake 1 — Thinking AI Security Owns Everything

AI Security coordinates and enables security but cannot own every technical system.

Mistake 2 — Ignoring Developers

Many important controls must be implemented in application code.

Mistake 3 — Ignoring IAM

Agent and service identities can determine the real security impact.

Mistake 4 — Ignoring Data Teams

AI security depends heavily on data security and provenance.

Mistake 5 — Bringing Security in Too Late

Security should participate during design.

Mistake 6 — Ignoring Operations

A secure design still requires monitoring and incident response.

Mistake 7 — Unclear Ownership

Security gaps often occur between teams.

Mistake 8 — Communicating Every Risk Technically

Security engineers must translate risk for different audiences.

Interview Perspective

You may be asked:

What does an AI Security Engineer do?

A strong answer is:

An AI Security Engineer helps securely design, deploy and operate AI-enabled systems. Responsibilities can include architecture security reviews, AI threat modeling, LLM and RAG security assessments, AI agent security, cloud and IAM reviews, security testing, monitoring requirements, vulnerability remediation and incident-response support. The role works closely with AI developers, cloud security, AppSec, data teams, SOC and governance teams.

Another common question may be:

Who is responsible for AI security in an enterprise?

A strong answer is:

AI security is a shared responsibility. Developers secure application logic, AI and ML teams protect models and pipelines, cloud teams secure infrastructure, IAM teams manage identities, data teams protect enterprise information, security teams define and validate controls, SOC teams monitor production, and business owners remain accountable for the risk associated with the use case.

Another question may be:

What is the difference between an AI Security Engineer and an AI Red Teamer?

A strong answer is:

An AI Red Teamer focuses primarily on adversarial testing and identifying ways AI systems can be abused or bypassed. An AI Security Engineer has broader responsibility across secure architecture, threat modeling, infrastructure, IAM, data protection, testing, remediation, monitoring and operational security.

Another question may be:

Why is shared responsibility particularly important for AI agents?

A strong answer is:

Because agent security spans application logic, model behavior, tool permissions, identity, enterprise systems and business authorization. The application team may build the workflow, IAM teams restrict the agent identity, AI Security reviews abuse cases, business owners define permitted actions and the SOC monitors actual execution. No single team controls the entire risk.

Key Takeaways

AI Security is a shared enterprise responsibility.

The ecosystem may include:

AI Security
+
AI / ML Engineering
+
Application Development
+
Cloud
+
IAM
+
AppSec
+
DevSecOps
+
MLOps
+
Data Security
+
Privacy
+
SOC
+
Incident Response
+
GRC
+
AI Governance
+
Business

The AI Security Engineer acts as a security specialist across these domains.

The role typically helps:

Understand Architecture
        ↓
Identify Risks
        ↓
Define Security Requirements
        ↓
Validate Controls
        ↓
Support Remediation
        ↓
Design Monitoring
        ↓
Support Incident Response

But each technology component still needs an appropriate owner.

The most important organizational question is:

Who is responsible for what?

If the answer is unclear, security gaps are likely.

What’s Next?

➡️ 10 — Enterprise AI Security Architecture

You now understand:

The AI ecosystem
The AI attack surface
The threat landscape
OWASP GenAI risks
MITRE ATLAS
The teams responsible for securing AI

The final lesson in Module 01 — AI Security Foundations will bring these concepts together.

In the next lesson, you will learn how to design a secure enterprise AI architecture.

You will explore:

Enterprise AI security architecture principles
Zero Trust for AI
Identity architecture
Least privilege
RAG security architecture
AI agent security architecture
Model and data security
Network segmentation
Secrets management
Secure AI supply chain
Logging and monitoring architecture
Security control placement
Defense in depth
Secure deployment patterns
Reference AI security architecture
Architecture review methodology

You will move from:

Understanding AI Security
        ↓
Understanding Threats
        ↓
Understanding Responsibilities
        ↓
Designing Secure AI Systems

➡️ Next: 10 — Enterprise AI Security Architecture