Hinton AIConsulting Group

Practice 03 · AI Security & Secure Architecture

Secure AI Before Risk Becomes Operational

Enterprise AI introduces a new attack surface. Models accept untrusted language as input, retrieval connects them to sensitive data, and agents can take actions in business systems. Our AI security consulting designs controls for how AI actually behaves.

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The new attack surface

Risks we assess and address

Traditional application security remains necessary, but it does not cover how language models interpret instructions, retrieve data, or call tools.

Input

Prompt injection

Direct instructions that override system behavior or safeguards.

Input

Indirect prompt injection

Malicious instructions hidden in documents, emails, or web content the model reads.

Data

Sensitive-data disclosure

Confidential or regulated data surfacing in outputs or logs.

Data

RAG security

Retrieval that ignores document permissions or ingests poisoned content.

Data

Vector database security

Access control, tenancy, and integrity for embedding stores.

Agent

AI agent permissions

Least-privilege design for agents that act on users' behalf.

Agent

Excessive agency

Agents with more autonomy or reach than the task requires.

Agent

Tool / API abuse

Agents manipulated into misusing the tools and APIs they can call.

Secrets

Secrets exposure

Keys and credentials leaking through prompts, code, or configuration.

Access

Model access

Who and what can call each model, and under which policies.

Access

Identity and access

Authentication, authorization, and user context carried end to end.

Supply chain

Third-party AI risk

Security posture of AI vendors, models, and embedded AI features.

Detect

AI logging

Capturing prompts, outputs, retrievals, and tool calls for review.

Detect

SIEM integration

Routing AI telemetry into existing detection and response workflows.

Respond

AI incident response

Playbooks for AI-specific incidents, from data leakage to agent misuse.

Secure AI reference architecture

Controls at every layer of the AI stack

Each layer gets its own controls, and all of them feed monitoring and incident response.

Secure AI reference architecture with seven layers, from users through identity and access, AI gateway, model, RAG and enterprise data, AI agents and tools, to enterprise systems. Each layer lists its security controls, and all layers feed monitoring, logging, SIEM, and incident response.
Users
  • Acceptable-use policy
  • Security awareness
  • Device posture
Identity & Access
  • SSO + MFA
  • Role-based access
  • Conditional access
  • User context propagation
AI Gateway
  • Input / output filtering
  • Prompt-injection screening
  • Rate limiting
  • DLP
  • Model routing policy
Model / LLM
  • Approved model registry
  • Key management
  • Vendor assessment
  • Evaluation testing
RAG / Enterprise Data
  • Permission-aware retrieval
  • Data classification
  • Vector store access control
  • Ingestion validation
AI Agents & Tools
  • Least-privilege tool scopes
  • Human approval gates
  • Action allowlists
  • Secrets vaulting
Enterprise Systems
  • API security
  • Service-account governance
  • Change control
  • Audit trails

Services

How we engage

AI security assessment

Review of existing AI systems, integrations, and controls with prioritized findings.

AI threat modeling

Structured analysis of how a specific AI solution could be attacked or misused.

Secure AI architecture

Reference designs and control requirements for new AI initiatives.

AI agent security and RAG review

Permission, retrieval, and tool-access analysis for agentic systems.

AI detection engineering

Logging design, SIEM integration, and detection content for AI activity.

AI incident response readiness

Playbooks, escalation paths, and tabletop exercises for AI incidents.

Security assessments identify and reduce risk. No assessment or control set can eliminate all risk.

Find the exposure before an attacker does.

An AI security assessment covers your models, data paths, agents, and integrations.

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