Version 2025-09-22 By Spencer Brawner

AI Security: Threats, Controls, and Evidence

AI security Threats Controls Prompt injection Model misuse

TL;DR

AI security addresses threats like prompt injection, data exfiltration, model misuse, supply-chain risks, and unsafe tool calls. Controls include input handling, retrieval hardening, capability gating, authZ, output validation, monitoring, incident response, and secure change management. Governance (ISO 42001) ensures these are designed, operated, and reviewed.

Published September 22, 2025

Section 01 // Key Facts

Key Facts

5 facts documented

AI systems introduce unique threats (instruction override, model misuse).
[2]
Layered controls reduce exploitability.
[1]
Logging and monitoring are essential for detection and forensics.
[1]
Secure change control prevents silent regressions.
[1]
Incident handling requires AI-specific playbooks.
[2]

Section 02 // Implementation

Implementation Steps

5 steps

01
Threat model → threat model doc.
02
Design controls → control list, allow-lists.
03
Test & validate → attack corpus, test logs.
04
Monitor & alert → log schema, alerts.
05
IR & lessons learned → IR reports, CAPA.

Section 03 // Glossary

Glossary

6 terms

Model misuse

Using AI systems for purposes beyond their intended design or capability

Capability gating

Controls that limit what actions an AI system can perform

Output validation

Verification that AI system outputs meet safety and security requirements

Retrieval hardening

Security measures applied to data retrieval processes in AI systems

Allow-list

Predefined list of permitted inputs, outputs, or actions

Telemetry

Automated collection and transmission of data for monitoring purposes

Section 04 // References

References

2 sources

[1]
ISO 42001 AI Management Systems Standard https://www.iso.org/standard/78380.html
[2]
NIST AI Risk Management Framework https://www.nist.gov/itl/ai-risk-management-framework

Section 05 // Facts

Machine-Readable Facts

3 claims

[
  {
    "id": "f-threats",
    "claim": "AI systems face threats such as prompt injection, misuse, and unsafe tool calls.",
    "source": "https://www.nist.gov/itl/ai-risk-management-framework"
  },
  {
    "id": "f-layers",
    "claim": "Layered controls across input, retrieval, tools, and outputs reduce risk.",
    "source": "https://www.nist.gov/itl/ai-risk-management-framework"
  },
  {
    "id": "f-logging",
    "claim": "Logging and monitoring are essential for detection and forensics in AI security.",
    "source": "https://www.iso.org/standard/78380.html"
  }
]