A Practical Enterprise Guide to Governing, Securing, and Recovering AI at Scale
Why a Framework Matters Now
Enterprise AI adoption is no longer experimental. Organizations are deploying LLM-powered assistants that retrieve data from internal repositories, summarize sensitive content, create tickets, and take actions through tool integrations across email, IT service management (ITSM), identity and access management (IAM), cloud APIs, and DevOps pipelines. The speed of deployment is outpacing the speed of governance.
In previous posts, I covered the 10 common LLM attacks enterprises need to plan for and explained why LLM firewalls are emerging as a critical new security layer. Both of those discussions converge on the same fundamental question: How does an enterprise systematically govern AI risk across its entire data estate, not just at the model layer?
That is exactly the question the NIST AI Risk Management Framework (AI RMF 1.0) was designed to answer. Released in January 2023 by the National Institute of Standards and Technology (NIST), with its Generative AI Profile (NIST AI 600-1) following in July 2024, this voluntary framework gives organizations a structured, technology-agnostic approach to identifying, assessing, and mitigating AI risks across the entire lifecycle.
This post walks through the NIST AI RMF’s four core functions, explains what each demands from an enterprise, and then maps those requirements to concrete capabilities now available through the unified Veeam and Securiti AI platform, following Veeam’s acquisition of Securiti AI in December 2025.
What is the NIST AI Risk Management Framework?
The NIST AI RMF is a voluntary, rights-preserving, non-sector-specific, and use-case-agnostic framework. It is designed to help organizations that design, develop, deploy, or use AI systems manage the many risks that come with AI and promote trustworthy and responsible development.
The framework treats AI as a socio-technical system: Risks emerge not only from models and data but from how people build, deploy, and use them. It defines seven characteristics of trustworthy AI:
- Valid and reliable: Systems perform as intended under expected and unexpected conditions.
- Safe: AI does not cause harm to people, property, or the environment.
- Secure and resilient: Systems resist attack and recover from disruptions.
- Accountable and transparent: Organizations can explain how decisions are made and who is responsible.
- Explainable and interpretable: Outputs can be understood by stakeholders.
- Privacy-enhanced: Personal data is collected, used, and protected appropriately.
- Fair with harmful bias managed: Systematic errors that produce inequitable outcomes are identified and mitigated.
These characteristics are operationalized through four interconnected core functions: Govern, Map, Measure, and Manage.
The Four Core Functions of the NIST AI RMF
The AI RMF structures risk management into four functions. Govern applies across all stages; Map, Measure, and Manage are applied in AI system-specific contexts.
1. Govern: Establish Policies, Roles, and Accountability
The Govern function is the foundation. It ensures that AI risk management is embedded in organizational culture, policies, and oversight structures. It asks: Who is accountable for AI risk? What legal and regulatory requirements apply? Are AI usage policies defined and enforced?
What Govern demands from enterprises:
- Define organizational AI risk tolerance and acceptable use policies.
- Assign clear roles and responsibilities for AI governance, including escalation paths for AI incidents.
- Align AI governance with existing enterprise risk management (ERM) frameworks.
- Maintain documentation of AI components, data sources, and decision processes.
- Establish continuous monitoring and periodic review of AI risk management effectiveness.
How Veeam and Securiti AI supports Govern
Securiti AI’s Data Command Center provides a unified, real-time knowledge graph across structured and unstructured, primary and secondary data. This gives governance teams a single pane of glass to understand where AI-relevant data lives, who has access, and what policies apply. Combined with Veeam’s auditable backup and recovery controls, organizations can maintain provenance and change tracking across their entire data estate, satisfying the documentation and accountability requirements.
2. Map: Identify and Contextualize AI Risks
The Map function asks organizations to identify the context in which their AI operates: What data does it use? What are the intended and unintended impacts? Who are the stakeholders? What are the legal constraints?
What Map demands from enterprises:
- Inventory all AI systems, including models, data sources, tools, connectors, and retrieval pipelines.
- Identify and classify sensitive data before connecting it to AI workloads.
- Document likelihood and magnitude of potential impacts across stakeholder groups.
- Map AI-specific risks such as prompt injection, data poisoning, hallucinations, and supply chain compromise.
- Assess risks unique to generative AI, as outlined in NIST AI 600-1, including confabulation, data privacy leakage, and information integrity threats.
How Veeam and Securiti AI supports Map:
Securiti AI’s Data Security Posture Management (DSPM) capabilities provide automated discovery and classification of sensitive data across cloud, on-premises, SaaS, and hybrid environments. Before AI touches any data, organizations can identify personally identifiable information (PII)/protected health information/payment card industry (PCI), secrets, and regulated data, and define AI-allowed versus AI-blocked categories. Securiti AI’s entitlement and access governance engine then enforces retrieval permissions based on identity and document-level entitlements, not simply based on who asked the bot.
On the resilience side, Veeam maintains a comprehensive inventory of backup and recovery points for every system in the AI pipeline, including source data, configurations, prompt templates, indexes, and vector databases, ensuring that organizations can document and roll back their full AI component chain.
3. Measure: Assess and Quantify AI Risks
The Measure function calls for organizations to employ a mix of assessment techniques to evaluate AI risks: testing, evaluation, validation, and verification (TEVV), including red-teaming, bias assessment, and security testing.
What Measure demands from enterprises:
- Conduct pre-deployment testing for hallucinations, bias, data leakage, and adversarial robustness.
- Evaluate fairness and bias across AI outputs.
- Test for prompt injection resilience (both direct and indirect).
- Assess environmental impacts and resource consumption.
- Implement structured red-teaming programs for generative AI systems.
How Veeam and Securiti AI supports Measure:
Securiti AI’s AI Trust capabilities include runtime guardrails that function as an LLM firewall, a concept I explored in my previous post on why traditional firewalls are not enough for LLM security. These guardrails enable organizations to detect prompt injection patterns, scan for sensitive data in both prompts and responses, enforce topic and policy boundaries, and monitor for anomalous tool-call behavior.
Veeam’s threat detection and anomaly monitoring capabilities complement this by identifying integrity changes in backup data, detecting encryption anomalies that may indicate ransomware or data tampering, and ensuring that the systems and data your AI depends on have not been compromised. Together, they provide a measurement layer that spans both AI-specific and infrastructure-level risk signals.
4. Manage: Treat, Respond, and Recover
The Manage function is where risk treatment, incident response, and recovery planning come together. This is also where the gap in most AI programs becomes most visible: teams focus on prevention but underinvest in response and recovery.
What Manage demands from enterprises:
- Prioritize risks and define treatment strategies (mitigate, transfer, accept, or avoid).
- Develop AI-specific incident response playbooks, including kill switches to disable tools, block sources, pause ingestion, and isolate agents.
- Plan for rollback and clean recovery of AI systems, data, pipelines, models, and agents.
- Establish post-deployment monitoring, appeal and override mechanisms, and decommissioning procedures.
- Manage third-party and supply chain AI risks with continuous oversight.
How Veeam and Securiti AI supports Manage:
This is Veeam’s core strength, now supercharged with Securiti AI’s governance layer. Veeam provides immutable backups, cleanroom-validated restore (up to five-times faster recovery), and granular rollback for datasets, embeddings, model weights, and pipeline configurations. When a retrieval-augmented generation (RAG) corpus is poisoned, a vector store is compromised, or an agent takes unauthorized actions, the organization can revert to a known-good baseline with confidence.
Securiti AI’s continuous governance and compliance engine provides identity-aware runtime enforcement and Zero-Trust security across production and backups. Together, the unified platform delivers what I described in my earlier post as the enterprise approach: Govern what the AI can access and recover what it breaks.
IST AI RMF to Veeam and Securiti AI Mapping
The following table maps each NIST AI RMF function to the combined platform capabilities:
| NIST Function | Enterprise Requirement | Veeam + Securiti AI Capability |
|---|---|---|
| GOVERN | Policies, roles, accountability, documentation, continuous review | Securiti AI Data Command Center for unified visibility; Veeam audit trails and provenance tracking; combined policy enforcement across primary and secondary data |
| MAP | AI inventory, sensitive data discovery, risk identification, GenAI-specific risk assessment | Securiti AI DSPM for automated data discovery and classification; identity-based entitlements; Veeam backup inventory across all AI pipeline components |
| MEASURE | TEVV, red-teaming, prompt injection testing, bias assessment, anomaly detection | Securiti AI runtime guardrails (LLM firewall); prompt and response scanning; Veeam threat detection and integrity monitoring across backup data |
| MANAGE | Risk treatment, incident response, rollback, recovery, decommissioning, third-party oversight | Veeam immutable backups and cleanroom recovery; granular rollback for data, pipelines, models, and agents; Securiti AI identity-aware enforcement and zero-trust controls |
Addressing Generative AI Risks: NIST AI 600-1
The NIST Generative AI Profile (AI 600-1) supplements the base framework with risks that are novel to or exacerbated by generative AI. These include confabulation (hallucinations), data privacy leakage, information integrity threats, chemical, biological, radiological and nuclear (CBRN) information risks, intellectual property concerns, harmful content generation, and value chain vulnerabilities.
The profile’s four primary considerations align directly with the capabilities my previous posts explored:
GenAI governance: Maintain acceptable use policies and update them as AI risks evolve. Securiti AI’s policy engine automates governance enforcement across data access and AI interactions.
Pre-deployment testing: Conduct robust TEVV processes before deployment. The LLM firewall capabilities provide real-time detection of injection attacks, data leakage attempts, and policy violations.
Content provenance: Track and verify the origin and integrity of AI-generated content. Veeam’s provenance controls and immutable backup chains provide auditability across the entire data lifecycle.
Incident disclosure: Establish transparent processes for reporting AI incidents. The combined platform’s centralized logging of policy decisions, tool calls, and retrieval activities provides the audit trail necessary for disclosure.
Enterprise Adoption: NIST AI RMF with Veeam and Securiti AI
Use this as a practical starting point for aligning your AI program with NIST AI RMF requirements:
- Establish AI governance ownership: Assign clear roles across your chief information officer, general counsel, head of risk, and chief data officer. Use Securiti AI’s Data Command Center as the single pane of glass for all AI data governance.
- Discover and classify all data before AI touches it: Deploy Securiti AI DSPM to identify PII/PHI/PCI, secrets, and regulated data across your entire estate. Define AI-allowed and AI-blocked categories.
- Inventory every AI component end-to-end: Document all models, prompts, agent workflows, tools, connectors, data sources, and vector databases. Ensure Veeam protects each component with immutable backups.
- Enforce least-privilege retrieval and actions: Use Securiti AI’s identity-based entitlements to ensure retrieval respects document-level permissions. Scope tool permissions with short-lived credentials and allowlists.
- Deploy LLM firewall controls: Implement Securiti AI’s runtime guardrails to detect prompt injection, scan for data leakage in prompts and responses, and enforce topic and policy boundaries.
- Harden RAG and vector store infrastructure: Secure vector stores like production databases. Enforce tenant isolation, encryption, and auditing. Maintain Veeam backup versions of vector indexes and ingestion configurations.
- Build AI-specific incident response playbooks: Define kill switches to disable tools, block sources, pause ingestion, and isolate agents. Include key rotation procedures for every connector the AI can access.
- Plan for rollback and clean recovery: Use Veeam’s cleanroom-validated restore and granular rollback to recover data, pipelines, embeddings, model weights, and agent configurations to known-good baselines.
- Log, monitor, and alert on AI-specific signals: Centralize logging of prompts (redacted), retrieval hits, tool calls, and policy decisions. Detect anomalies such as injection patterns, tool-call spikes, and unusual retrieval volume.
- Conduct ongoing red-teaming and review: Schedule regular TEVV cycles. Review and update acceptable use policies as AI capabilities and threats evolve. Tie results back to the Govern function for continuous improvement.
Closing Thoughts
The NIST AI RMF is not a regulation, but it is rapidly becoming the benchmark that regulations reference. For enterprises deploying generative AI at scale, the question is not whether to adopt a risk management framework but how quickly you can operationalize one.
What makes the combined Veeam and Securiti AI platform significant is that it maps to every layer of the framework. It is not about adding another point solution; it is about having a unified command center that can see all your data, enforce security and privacy policies at AI speed, and recover anything when something goes wrong.
The realistic goal in AI security is not zero incidents. It is minimum blast radius, rapid detection, fast rollback and clean recovery, and auditability and proof of control.
If you can govern access, constrain actions, verify outputs, and ensure recovery, you can scale GenAI safely, without slowing the business.
References and Further Reading
NIST AI 100-1: Artificial Intelligence Risk Management Framework (AI RMF 1.0), January 2023
NIST AI 600-1: Generative Artificial Intelligence Profile, July 2024
Veeam Completes Acquisition of Securiti AI, December 11, 2025
Ali Salman, “Securing GenAI Beyond the Model: 10 LLM Attacks and the Case for Governance and Recovery,” Veeam Blog, February 2026
Ali Salman, “From Packets to Prompts: How Security is Changing with AI and Why LLM Firewalls Matter,” Veeam Blog, January 2026
