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AI Threat Mitigation Strategies
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series turns from naming AI threats to neutralizing them, grouping the many possible controls into practical families you can actually deploy. Understanding these mitigation families lets you recommend the right control for a given threat and recognize that a single strong control often defends against many threats at once. By the end you will know how to map threats to controls and why AI should push leaders to rethink their risk appetite from scratch.
What this episode covers
- The control-pairing mindset — why families beat lookup tables for AI threat mitigation.
- Monitoring and detection controls — the early-warning radar around an AI system.
- Fairness and ethics controls — addressing social and moral risks no patch can fix.
- Access and identity controls — defending against insider threats and synthetic-media impersonation.
- Input and output handling controls — patrolling the boundary between users and the model.
- Data and model integrity controls — keeping training data and weights trustworthy.
- Content authenticity defenses — countering deepfakes and disinformation.
- Governance, compliance, and human controls — including the kill switch as a critical organizational safeguard.
- Revisiting risk appetite — why AI’s expanded stakeholder circle forces a fresh look at tolerance.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are AI threat mitigation strategies?
AI threat mitigation strategies are the controls an organization deploys once it has chosen to mitigate a risk, pairing each AI threat with one or more safeguards. Rather than memorizing every individual pairing, security managers group controls into a manageable set of families so that a single strong control can defend against several threats at once.
What are the main families of AI controls?
The main families are monitoring and detection, fairness and ethics, access and identity, input and output handling, data and model integrity, content authenticity defenses, governance and compliance, and human and organizational controls. Together they cover the full landscape of AI threats from drift and poisoning to deepfakes and overreliance.
How do input and output controls defend AI systems?
Input sanitization, output validation, and structured prompt templates blunt prompt injection and improper output handling, while application interface throttling, rate limiting, and access controls defend against model theft and extraction through the interface.
Why should organizations revisit their risk appetite for AI?
AI dramatically expands the circle of stakeholders and threats, so organizations are strongly advised to revisit and, if needed, redefine their risk appetite and tolerance rather than assuming the limits set for ordinary operations still fit the new environment.
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Reference: This article is based on concepts discussed in AI Threat Mitigation Strategies.