The CISO’s Handbook to AI Security: Frameworks, Controls, and What’s Next
What Changes with AI
AI is not just another application to harden. It is a moving system that learns, adapts, and interacts with people and data in new ways. The risk surface now spans training sets, prompts, model weights, vector stores, agents, tools, and downstream automations. Treating it like ordinary software misses the point.
A good starting point is a shared language for risk and a shared map of the system. NIST’s AI Risk Management Framework provides a structure with four functions: Govern, Map, Measure, and Manage. Use it as scaffolding for your program, then make it operational inside your environment.
Step 1: Make the Invisible Visible
Before controls, build a complete inventory. Keep it lightweight but live.
Inventory Essentials
Models: base, fine-tuned, hosted, local. Capture version, source, license, owner, and deployment scope.
Data: training, evaluation, prompts, outputs, embeddings, vector stores. Tag sensitivity, residency, and retention.
Pipelines and Endpoints: training jobs, inference services, retrieval flows, agent tools.
Identities: human, service, and machine accounts; secrets; scopes.
Vendors: LLM APIs, model hubs, labeling partners, SaaS plugins.
Proof You’ll Need
Model and system cards
Data lineage for training and evaluation sets
Change logs for prompts, policies, and model versions
Map this inventory to ISO/IEC 42001 so governance and lifecycle obligations are explicit from day one.
Step 2: Control the Lifecycle Where It Matters
Security work lands in three places: data, model, and runtime.
Data Controls
Classify training and inference data before use. Keep minimization and masking close to the data source.
Track provenance for training sets. Record source, license, consent basis, and hashing for tamper detection.
Validate for poisoning or contamination as a routine gate. Even small amounts of poisoned content can degrade a model.
Model Controls
Enforce role-based changes on training, fine-tuning, export, and rollback.
Monitor for drift and inversion attempts. Use adversarial testing and internal red-teaming.
Log inference inputs and outputs with privacy controls so you can audit behavior without storing more than you need.
Runtime and Application Controls
Route LLM calls through a secure gateway. Apply allowlists for tools and connectors.
Test and filter prompts and outputs. OWASP’s LLM Top 10 highlights prompt injection, insecure output handling, and supply chain issues. Use it to seed your guardrails and test suites.
Step 3: Turn Policy into Evidence
Policy is only useful if it ties to a control, an owner, and a proof point.
Make Each Policy Atomic
Requirement: protect sensitive training data
Control: mask defined fields before ingestion and block unknown sources
Owner: data platform lead
Evidence: masking policy in code, scan report, lineage record, and a daily exception log
Anchor these controls to recognized frameworks so audit conversations stay simple. Use NIST AI RMF for structure, ISO/IEC 42001 for management expectations, and the EU AI Act to understand obligations for high-risk or limited-risk systems.
For maturity benchmarking and crosswalks, track Forrester’s AEGIS model. It maps governance, identity, data security, and Zero Trust into an AI context and provides templates you can mirror in your control register.
Step 4: Report AI Risk the Way the Board Understands
Boards care about business impact and readiness, not model internals.
Metrics That Matter
Top AI systems and their risk ratings, with owners
High-risk data sources under remediation and time to close
Control health: failed pre-deployment gates, drift alerts acknowledged, red-team findings mitigated
Regulatory readiness milestones by framework and region
Keep the narrative simple. Confidentiality, integrity, availability, and compliance still frame the conversation. The artifacts are new; the goals are not.
Step 5: Update Incident Response for AI
Define what an AI incident looks like and who calls it.
Playbook Additions
Triggers: leakage in outputs, prompt injection, poisoning indicators, inversion signals, drift beyond thresholds
Actions: freeze or roll back a model version, revoke keys, quarantine vector stores or embeddings, rotate secrets
Escalation: coordinate with privacy and legal for data exposure; prepare public communication if customer-facing models are affected
Learning loop: add new tests and guardrails based on post-incident reviews
Step 6: Align Globally Without Duplicating Work
Most enterprises operate in multiple jurisdictions. Avoid parallel programs.
A Practical Alignment Pattern
Use NIST AI RMF as the common structure for roles and risk.
Map management processes to ISO/IEC 42001 for governance and continual improvement.
Identify systems that meet the EU AI Act definition of high risk and prepare conformity evidence early.
Track AEGIS and similar models to align enterprise security and governance as teams adopt agents and new AI tooling.
This approach maintains one control set and one evidence trail that can satisfy multiple frameworks.
Step 7: Build Shared Intelligence
AI threats evolve quickly and repeat across sectors. Invest in community signals.
Adopt a shared taxonomy for AI incidents so teams describe them consistently.
Participate in coordinated disclosure and information-sharing forums.
Encourage internal red-teaming and publish sanitized lessons learned so partners can harden their systems too.
The cybersecurity world advanced when communities shared indicators and techniques. AI security will mature the same way.
What to Do Next
Publish a one-page AI security standard naming owners and minimum pre-deployment controls.
Register every model and endpoint in one catalog. Add sensitivity tags, a risk rating, and a rollback contact.
Add automated pre-deployment gates for data scanning, model validation, and abuse testing.
Instrument drift and leakage detection for production models and route alerts to the SOC.
Update incident response with AI-specific triggers and rollback procedures.
Build a crosswalk showing how your controls satisfy NIST, ISO 42001, and EU AI Act requirements.
Looking Ahead
Expect new research on data and AI risk, inversion, and provenance verification. Expect clearer regulatory guidance on high-risk classification and conformity assessments. Expect deeper integration between AI and cybersecurity programs.
The goal is not to eliminate AI risk but to make it measurable, managed, and aligned with enterprise resilience.
Executive Summary
Inventory and classify first.
Embed controls at the data, model, and runtime layers.
Tie every policy to an owner and evidence.
Report risk in business terms.
Be prepared to roll back quickly.
Align once; answer many.
Share intelligence across the ecosystem.
AI will redefine how enterprises operate — and how CISOs lead. Those who build visibility, governance, and adaptability now will define what “secure AI” means in practice.
