A 7-step framework for protecting organizations against AI threats—covering threat surface identification, governance controls, technical hardening, red teaming, monitoring, and incident response.

Who this is for: Security teams, risk officers, and technology leaders responsible for AI systems in their organization. No deep AI technical background required.

Protecting against AI threats requires a structured 7-step framework: (1) identify your AI threat surface, (2) classify threats by type using a consistent taxonomy, (3) apply governance controls, (4) harden AI inputs and outputs technically, (5) red team before deployment, (6) monitor post-deployment, and (7) maintain incident response readiness. The framework maps to the NIST AI Risk Management Framework Govern → Map → Measure → Manage functions and applies to both AI systems you build and AI-powered tools your organization uses.

Step 1: Identify Your AI Threat Surface

An AI threat surface consists of every point where an AI system interacts with data, users, or other systems and could be exploited or cause harm. Before applying any control, map your surface across five layers:

Models — which AI models does your organization use or operate? Include third-party APIs (OpenAI, Anthropic, Google), embedded models in SaaS tools, and internally fine-tuned models. Each model’s training data, capabilities, and safety mitigations differ.

Data — what data does each model access, process, or generate? Training data, retrieval corpora (RAG), user-provided input, and model outputs are all surface. Data that includes PII, financial information, or intellectual property represents elevated risk.

Integrations — what tools, APIs, and systems are connected to your AI? Each integration (email, calendar, databases, code executors, web browsers) is an additional attack vector for prompt injection and privilege escalation.

Users — who interacts with the AI? Internal users, customers, and anonymous public users represent different trust levels and require different controls.

Agents — do any AI systems take autonomous actions (send emails, execute code, write to databases, call external APIs)? Agentic systems have a fundamentally larger blast radius when compromised.

Document this surface in a simple inventory. A system you have not mapped is a system you cannot protect.

Step 2: Classify Threats by Type

AI threats fall into three categories requiring different controls. Using topaithreats’ classification system, categorize each risk in your inventory:

Technical threats — exploit weaknesses in the AI system itself:

Prompt injection — adversarial input overrides system instructions
Data poisoning — corrupted training or retrieval data causes systematic failures
Adversarial evasion — manipulated inputs cause incorrect model outputs
Model inversion — model outputs reveal private training data

Misuse threats — exploit AI capabilities for harm:

Deepfake fraud — synthetic media used for identity theft or scams
AI-enhanced social engineering — personalized phishing and manipulation at scale
Automated vulnerability discovery — AI-assisted attack tooling
Disinformation generation — AI-produced false content at scale

Systemic threats — emerge from how AI is deployed rather than from adversarial attack:

Automation bias — over-reliance on AI decisions without human oversight
Goal drift — AI agents pursuing objectives in unintended ways
Accountability gaps — unclear responsibility when AI causes harm
Regulatory non-compliance — AI systems that violate applicable law

Classification determines which controls apply. Technical threats require engineering solutions; misuse threats require use-case scoping and monitoring; systemic threats require governance and policy.

Step 3: Apply Governance Controls

Governance controls establish who is responsible for AI risk and what decisions require human oversight.

Assign AI risk ownership. Every AI system should have a named owner responsible for its risk posture. For high-risk systems, this should be a product or risk officer, not just an engineer.

Classify AI systems by risk tier. The EU AI Act provides a four-tier classification (unacceptable risk → high risk → limited risk → minimal risk) that is a useful starting point even for organizations not subject to EU law. High-risk systems (those affecting access to employment, credit, essential services, or safety-critical decisions) require formal risk management documentation.

Define human oversight requirements. Determine which AI decisions require human review before action. As a baseline: any irreversible action (sending external communications, financial transactions, access control changes) should require human approval when taken by an autonomous AI agent.

Establish an AI use policy. Define which AI tools employees may use, under what conditions, and with what data. Shadow AI—employees using unapproved AI tools—is itself a threat surface.

Step 4: Harden AI Inputs and Outputs

Technical hardening reduces the exploitability of your AI systems at the input and output layers. Key controls:

Input layer:

Enforce prompt separation between system instructions and user-provided content—treat all user input as untrusted (see How to Prevent Prompt Injection)
Apply input length limits and encoding normalization
For RAG systems, scan retrieved content for injection patterns at the indexing stage, not only at query time
Scope AI tool permissions to the minimum required for each task

Output layer:

Validate model outputs against expected format before downstream use
Apply content filtering for outputs that may contain PII, harmful content, or policy violations
For agentic systems, implement an action allowlist—the agent may only call tools on a pre-approved list

Model layer:

Prefer models with documented safety mitigations for your use case
Do not expose raw model APIs to untrusted users without an application layer enforcing scope
Apply fine-tuning data integrity checks if you fine-tune models on internal data

Step 5: Red Team Before Deployment

Red teaming is adversarial testing of your AI system before it reaches users—systematically attempting to cause it to behave in unsafe, harmful, or policy-violating ways. It surfaces failures that functional testing misses because functional testing assumes good-faith use.

Minimum red team coverage before any AI deployment:

Jailbreaks and guardrail bypass attempts
Prompt injection through all input channels (user input, retrieved documents, tool outputs)
Harmful content elicitation relevant to the system’s capabilities and deployment context
Bias and fairness testing across the demographic groups the system will affect

For detailed methodology, see AI Red Teaming. Critical and high-severity findings should block deployment until mitigated.

Step 6: Monitor Post-Deployment

AI threats do not stop at deployment. New attack techniques emerge continuously; fine-tuned models can degrade; threat actors probe production systems. Post-deployment monitoring requires:

Behavioral monitoring — log all model inputs and outputs. Flag statistical anomalies: unusual input patterns (injection attempt indicators), unexpected tool call sequences, output format deviations, and volume spikes.

Drift detection — monitor for model behavior drift over time, particularly after backend updates, fine-tuning, or changes to retrieved data. A model that behaved safely in March may behave differently in June if its retrieval corpus has changed.

Incident logging — maintain a log of anomalous events with enough detail for forensic analysis. Every confirmed security or safety incident should be logged with root cause and remediation actions.

Periodic re-evaluation — run automated red team tools (Garak, PyRIT) on a scheduled basis against production systems. New jailbreak and injection techniques emerge monthly; systems require ongoing evaluation, not just pre-deployment sign-off.

Step 7: Maintain Incident Response Readiness

When an AI threat materializes—a successful prompt injection, a harmful output reaching users, a data exfiltration event—you need a documented response process ready before the incident, not after.

An AI incident response plan covers five phases: detect, contain, investigate, remediate, and report. For a full template and regulatory reporting requirements (including EU AI Act Article 62 obligations), see How to Build an AI Incident Response Plan.

The minimum pre-incident readiness requirements are:

Named incident owner for each AI system
Defined severity tiers and escalation thresholds
Rollback capability for model or configuration changes
Contact list for regulatory notification if required by your jurisdiction

Summary: Threat Type → Protection Measure

Threat Type	Primary Controls	Governance Requirement
Prompt injection	Privilege separation, input validation, output validation	Red team before deployment
Data poisoning	Training data integrity, RAG content scanning	Data provenance documentation
Deepfake fraud	Detection tools, out-of-band verification procedures	User awareness policy
Social engineering	Scope restrictions, monitoring	AI use policy
Automation bias	Human oversight requirements, appeals process	Risk tier classification
Goal drift	Minimal agent permissions, human approval gates	Agentic system governance policy
Accountability gaps	Risk ownership assignment, incident logging	Named AI risk owner per system

AI Security Best Practices — technical controls for LLM applications
AI Red Teaming — adversarial testing methodology
How to Prevent Prompt Injection — the most commonly exploited AI vulnerability
How to Build an AI Incident Response Plan — when a threat materializes
NIST AI Risk Management Framework — the governance framework this guide maps to
AI Threat Taxonomy — classification system for all threat types

How to Protect Against AI Threats: A Practical Framework