What Is AI Security Framework?
- Aug 2
- 28 min read
Organizations are wiring AI into loan approvals, hiring pipelines, customer service, and internal copilots faster than most security programs can absorb. A firewall does not stop a poisoned training set. A web application scanner does not catch a prompt injected through a PDF. The result is a widening gap between what conventional security controls were built for and what today's AI systems actually need, and an AI security framework is how organizations start closing it.
TL;DR
An AI security framework is a structured set of governance practices, risk processes, technical controls, and lifecycle procedures that protect AI systems, their data, models, and the people who depend on them.
It can mean an external standard an organization adopts (like NIST AI RMF or ISO/IEC 42001) or an internal framework built by combining several standards, threat models, and controls.
Leading resources include NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, the OWASP Top 10 for LLM Applications, MITRE ATLAS, CISA/NCSC's Guidelines for Secure AI System Development, and Google's Secure AI Framework (SAIF).
Protection has to run across the full AI lifecycle, from data collection through decommissioning, not just at the point a model goes live.
No single framework or guardrail is sufficient on its own; system prompts and content filters are not dependable security boundaries.
What is an AI security framework?
An AI security framework is a structured set of governance practices, risk-management processes, technical controls, testing methods, and lifecycle procedures used to protect AI systems, their data, models, infrastructure, and users against manipulation, misuse, failure, and unauthorized access. It can refer to an adopted external standard or an organization's own combined internal program.
Table of Contents
What Is an AI Security Framework?
An AI security framework is a structured set of governance practices, risk-management processes, technical controls, testing methods, roles, and lifecycle procedures used to protect AI systems, their data, models, infrastructure, integrations, users, and business processes against security threats, misuse, failure, manipulation, and unauthorized access.
The term is used in two different ways, and mixing them up is a common source of confusion.
First, "AI security framework" can mean a recognized external framework or standard that an organization adopts wholesale, such as the NIST AI Risk Management Framework or ISO/IEC 42001. Second, it can mean an internal organizational framework that a company assembles from multiple standards, threat models, policies, and technical controls, tailored to its own systems and risk appetite.
Most mature organizations end up doing both: they adopt one or two external frameworks as a backbone, then build an internal framework around them that reflects their specific AI use cases, from a machine learning fraud model to a customer-facing chatbot built on a large language model.
No single framework covers every technical, governance, privacy, compliance, and operational need. NIST AI RMF gives structure to risk decisions but does not specify technical controls in detail. OWASP's guidance covers application-layer LLM risks but says little about enterprise governance. A workable AI security framework in practice is usually a combination, not a single document.
Hypothetical example. A mid-sized insurer using a large language model to draft claims summaries might adopt NIST AI RMF for its risk-management structure, apply the OWASP Top 10 for LLM Applications to harden the chatbot itself, and use its existing cybersecurity program for the underlying cloud infrastructure. Together, these form the insurer's AI security framework, even though no single document defines it.
Why AI Security Frameworks Matter
Conventional application security controls are necessary for AI systems, but they were never designed for probabilistic, data-driven software that changes behavior based on what it is fed after deployment.
A traditional application behaves the same way every time it receives the same input. An AI model, especially a generative one, can produce different outputs from the same prompt, and its behavior depends heavily on training data, fine-tuning, retrieval sources, and runtime context. That probabilistic nature breaks assumptions that firewalls, static code scanners, and traditional access controls rely on.
Several factors make AI-specific security work necessary rather than optional:
Data dependence. A model is only as trustworthy as the data used to train, fine-tune, or ground it. Corrupted or manipulated data becomes part of the model's behavior.
Training and inference pipelines. These pipelines are new attack surfaces with their own credentials, storage, and compute dependencies that traditional application security teams rarely touch.
Third-party and open-source models. Many organizations fine-tune or deploy models they did not build, inheriting whatever weaknesses or backdoors are already present.
Prompt-driven interfaces. Natural-language input blurs the line between "data" and "instructions," which is the root cause behind prompt injection.
RAG and vector databases. Retrieval-augmented generation pulls in external content at runtime, and a vector database with weak access controls can become a poisoning vector.
Tool-using and autonomous agents. An autonomous agent that can call APIs, move money, or edit files turns a language failure into an action failure.
Supply-chain risk. Pretrained weights, datasets, and libraries pulled from public repositories carry the same trust problems as any other third-party software component.
Continuous change. Models drift, get retrained, and get fine-tuned regularly, so a security review performed once at launch does not stay valid.
Dual-use capability. The same model that drafts marketing copy can also be misused to draft phishing emails or malicious code.
Business, legal, safety, and reputational stakes. A compromised AI system does not just leak data; it can make decisions, take actions, and produce content that causes real-world harm.
Article 15 of the EU AI Act makes this explicit for high-risk systems in the European Union, requiring resilience against data poisoning, model poisoning, adversarial examples, and confidentiality attacks as a matter of law, not just best practice.
AI Security vs. AI Safety, Responsible AI, Governance, Privacy, and Cybersecurity
These terms overlap constantly in vendor marketing, but they are not interchangeable. Understanding the distinction helps an organization figure out which team, and which controls, actually own a given risk.
Discipline | Primary Focus | Typical Owner | Example Concern |
AI security | Protecting AI systems, data, and models from attack and misuse | Security engineering, CISO | Prompt injection, model theft, data poisoning |
AI safety | Preventing unintended or harmful model behavior, including at scale | Research, safety teams | Harmful outputs, loss of control in advanced systems |
Responsible AI | Ethical, fair, and accountable AI development and use | Product, ethics, legal | Bias, explainability, appropriate use cases |
AI governance | Policy, oversight, and accountability structures for AI | Governance committee, legal | Approval workflows, roles, documented decisions |
Privacy | Protecting personal data and individual rights | Privacy/legal | Consent, data minimization, subject rights |
Traditional cybersecurity | Protecting IT systems and infrastructure broadly | CISO, IT security | Network security, endpoint protection, identity |
These disciplines overlap deliberately. A prompt injection vulnerability is an AI security issue, but it can also become a privacy issue if it leaks personal data, and a governance issue if no one owns the decision to fix it. AI security is best understood as the technical and operational layer that the other disciplines depend on to be enforceable in practice.
What an AI Security Framework Protects
A common mistake is treating the model file as the entire system. In reality, an AI security framework has to protect a much wider set of assets across the full stack:
Training, validation, testing, and operational data, along with data provenance and labeling records
Foundation and task-specific models, including model weights and other artifacts
Prompts, system instructions, and prompt templates
Embeddings and the vector database that stores them
RAG pipelines that retrieve and inject external content at runtime
APIs, plugins, tools, and connectors that a model or agent can call
Agent memory, session state, and permission scopes
MLOps and LLMOps pipelines, including CI/CD for models
Code repositories and third-party dependencies
Cloud and on-premises infrastructure hosting the AI stack
Secrets, credentials, and service identities used by AI components
Outputs and any downstream actions those outputs trigger
The people who use the system and the people affected by its decisions
The business processes the AI system supports
Logs, monitoring data, and audit evidence
Protecting only the model and ignoring the pipeline, the plugins, or the people who rely on its outputs leaves most of the real attack surface uncovered.
The Core Principles and Control Domains of AI Security
A durable AI security framework rests on a small set of principles applied consistently: security by design, defense in depth, least privilege, zero trust between components, data minimization, separation of duties, human oversight for high-impact decisions, fail-safe behavior when something goes wrong, traceability, reproducibility, transparency about capabilities and limits, continuous monitoring, secure defaults, resilience, and clear accountability for every control.
Those principles translate into control domains that map cleanly onto how most security teams already organize their work:
Governance — policy, ownership, and escalation paths
Asset inventory — knowing which AI systems exist, including shadow deployments
Risk management — structured identification and treatment of AI-specific risk
Data security — protecting training, fine-tuning, and retrieval data
Model security — protecting weights, architecture, and training integrity
Application security — securing the interfaces around the model
Infrastructure and platform security — the cloud, compute, and storage layer
Identity and access management — for both humans and machine identities
Supply-chain security — vetting models, datasets, and dependencies
Privacy — protecting personal data used by or generated through AI
Secure development — building security into the ML/LLM development lifecycle
Testing and evaluation — adversarial testing before and after deployment
Deployment controls — approval gates before a system goes live
Runtime protection — monitoring and blocking abuse in production
Monitoring and detection — logging and anomaly detection tuned to AI behavior
Incident response — AI-specific playbooks for containment and recovery
Business continuity — plans for model failure or unavailability
Third-party risk — vetting external model and API providers
Compliance and assurance — mapping controls to legal and contractual duties
The AI Threat Landscape: Attacks, Vulnerabilities, Misuse, and Failures
AI systems face a distinct threat landscape that traditional threat models rarely capture. MITRE ATLAS and the OWASP Top 10 for LLM Applications are the two most widely used references for cataloging these threats, and together they cover the categories below.
Data and model integrity attacks. Data poisoning corrupts training or fine-tuning data so the model learns the wrong behavior. Model poisoning targets pretrained components directly. Backdoors are hidden triggers planted during training that activate only under specific conditions, making them hard to detect through normal testing.
Evasion and extraction. Evasion attacks, also called adversarial examples, use carefully crafted inputs to cause misclassification without altering the model itself. Model extraction lets an attacker reconstruct a proprietary model through repeated querying. Model inversion and membership inference attacks try to recover sensitive details about the training data itself, including whether specific records were used to train the model. Training-data extraction pulls memorized content, sometimes including personal data, directly out of a deployed model.
Prompt and instruction attacks. Prompt injection manipulates a model's behavior through crafted input, and indirect prompt injection hides those instructions inside documents, web pages, or emails the model later processes. Jailbreaking bypasses a model's built-in restrictions, often through role-play framing or obfuscated language. System prompt leakage exposes internal instructions that were never meant to be a security boundary in the first place.
Output and agency risks. Sensitive-information disclosure happens when a model reveals data it should not. Insecure output handling occurs when an application trusts model output without validation, for example by executing generated code. Excessive agency describes an AI agent given more permissions or autonomy than its task actually requires, and tool or plugin abuse happens when an attacker manipulates an agent into misusing the tools connected to it.
Retrieval and knowledge-base risks. RAG poisoning inserts malicious content into the documents a RAG system retrieves. Retrieval manipulation biases which content gets surfaced. Vector and embedding weaknesses can allow attackers to infer or extract sensitive information stored in embeddings.
Availability and cost risks. Denial-of-service and unbounded consumption attacks flood a model with expensive queries, and cost harvesting deliberately drives up an organization's inference bill.
Supply-chain and operational risks. Supply-chain compromise affects pretrained models, datasets, or libraries obtained from public repositories. Credential theft and insecure APIs expose the infrastructure around a model. Shadow AI refers to AI tools employees adopt without security review. Model drift and unauthorized model changes erode reliability and can be exploited. Deepfake-enabled fraud and social engineering use generative models to impersonate real people, and insider threats and monitoring gaps round out the list; a threat that is never logged is a threat no one ever investigates.
No single guardrail or content filter addresses this whole list. Each category needs its own combination of preventive, detective, and corrective controls mapped to the specific assets it threatens.
Securing the Complete AI Lifecycle
AI security has to run across the entire lifecycle, not just at launch. CISA and the UK's National Cyber Security Centre organize this into four phases in their Guidelines for Secure AI System Development: secure design, secure development, secure deployment, and secure operation and maintenance.
A more granular internal lifecycle typically includes: strategy and business-case approval; risk classification; system design; data acquisition and preparation; model selection or development; training and fine-tuning; testing and evaluation; integration; deployment; runtime operation; monitoring and change management; incident response; and eventually model replacement, decommissioning, and data disposal.
Responsibilities and evidence requirements shift at each stage. During design, threat modeling and risk classification produce the evidence that later gates depend on. During development, supply-chain checks and secure coding practices generate audit trails. During deployment, an approval gate should confirm testing was completed and residual risk was accepted by someone with the authority to accept it. During operation, monitoring data and incident logs become the evidence that the system is still behaving as expected. Decommissioning is often skipped entirely, leaving orphaned models, stale credentials, and undeleted training data as long-term liabilities.
Major AI Security Frameworks, Standards, and Regulations
NIST AI Risk Management Framework (AI RMF 1.0). Published by the U.S. National Institute of Standards and Technology on January 26, 2023, as NIST.AI.100-1, the AI RMF is voluntary guidance organized around four functions: Govern, Map, Measure, and Manage [1]. It is sector-agnostic and does not prescribe specific technical controls; instead it structures how an organization identifies, assesses, and treats AI risk. NIST released a companion Generative AI Profile, NIST-AI-600-1, on July 26, 2024, which adds 12 risk categories specific to generative AI [2]. Because it is voluntary, no organization can be "NIST certified" against AI RMF; it functions as a risk-management operating model, often nested inside a certifiable standard like ISO/IEC 42001.
ISO/IEC 42001:2023. Published in December 2023, this is the world's first international standard for an AI management system (AIMS) [3]. Unlike NIST AI RMF, ISO/IEC 42001 is certifiable through accredited third-party audit. It specifies requirements for governance, AI system impact assessment, lifecycle management, and supplier oversight. Organizations use it to build an auditable management system rather than a specific set of technical security controls.
ISO/IEC 23894:2023. Published in February 2023, this standard provides guidance on AI-specific risk management, built on top of the general enterprise risk-management standard ISO 31000 [4]. It is voluntary and non-certifiable, and it complements both NIST AI RMF and ISO/IEC 42001 by giving more tactical, process-level detail on identifying and treating AI risk.
ISO/IEC 27090. As of August 2026, this document is not yet a published standard. It reached Final Draft International Standard (FDIS) status in early 2026, with a ballot that closes in August 2026 [5]. Once published, it is expected to provide detailed technical guidance on AI-specific cybersecurity threats such as data poisoning, evasion, model extraction, and prompt injection, extending the existing ISO 27000 family. Until formal publication, it should be treated as advanced draft guidance, not a finalized standard.
OWASP Top 10 for LLM Applications. Maintained by the OWASP GenAI Security Project, the current version was published on March 12, 2025, replacing the original 2023 list [6]. It is a prioritized list of the ten most critical security risks in LLM-based applications, including prompt injection, sensitive information disclosure, supply chain risk, and system prompt leakage. It is informational guidance rather than a certifiable standard, and it is one of the few resources written specifically for developers building LLM applications day to day.
MITRE ATLAS. The Adversarial Threat Landscape for Artificial-Intelligence Systems began in 2020 as a MITRE and Microsoft collaboration and is now a living, community-maintained knowledge base of adversary tactics and techniques targeting AI and ML systems, modeled on MITRE ATT&CK [7]. It documents real-world case studies and mitigations rather than prescribing controls directly, and it is most useful for threat modeling and red-team planning.
CISA/NCSC Guidelines for Secure AI System Development. Released jointly on November 26, 2023, by the U.S. Cybersecurity and Infrastructure Security Agency and the UK National Cyber Security Centre, co-signed by 23 international agencies [8]. The guidelines are non-binding and organized around secure design, secure development, secure deployment, and secure operation and maintenance, built on a "secure by design" philosophy.
Google Secure AI Framework (SAIF). A vendor-authored framework built on the same tenets as Google's existing secure-by-default engineering practices, aimed at mitigating risks such as model theft, data poisoning, and prompt injection [9]. It draws on the NIST Cybersecurity Framework and ISO/IEC 27001 and is offered as informal industry guidance rather than a formal standard.
ENISA guidance. The EU's cybersecurity agency published a Multilayer Framework for Good Cybersecurity Practices for AI in June 2023, recommending practices across the AI system lifecycle for EU member states and bodies [8]. It functions as regional advisory guidance rather than binding law.
EU AI Act. Regulation (EU) 2024/1689 is binding law, not voluntary guidance. Article 15 requires high-risk AI systems to achieve an appropriate level of accuracy, robustness, and cybersecurity throughout their lifecycle, with specific measures against data poisoning, model poisoning, and adversarial examples [10]. Obligations for high-risk AI systems apply from August 2, 2026, making Article 15 one of the only entries on this list with direct legal force in the EU.
Comparing the Leading AI Security Frameworks
Framework | Type | Primary Purpose | Best Use | Certification/Legal Status |
NIST AI RMF | Voluntary guidance | Structure AI risk decisions | Risk-management operating model | Not certifiable |
ISO/IEC 42001 | International standard | AI management system | Auditable governance program | Certifiable |
ISO/IEC 23894 | Voluntary guidance | AI-specific risk process | Tactical risk assessment detail | Not certifiable |
OWASP LLM Top 10 | Informational guidance | Application-level LLM risks | Securing LLM/RAG/agent apps | Not certifiable |
MITRE ATLAS | Knowledge base | Adversary tactics and techniques | Threat modeling, red teaming | Not applicable |
CISA/NCSC Guidelines | Government guidance | Secure AI development lifecycle | Secure-by-design engineering | Non-binding |
EU AI Act (Art. 15) | Regulation | Legal cybersecurity obligation | High-risk EU AI systems | Legally binding |
These resources are complementary because each one operates at a different altitude. NIST AI RMF and ISO/IEC 42001 sit at the governance and risk layer. OWASP and MITRE ATLAS sit at the technical and threat-modeling layer. CISA/NCSC and Google SAIF sit at the engineering-practice layer. The EU AI Act sits above all of them as a legal floor that applies regardless of which voluntary frameworks an organization chooses. Treating any single one as sufficient by itself leaves gaps that the others were built to fill.
How to Build an AI Security Framework Step by Step
Define scope and business objectives. Decide which AI systems, business units, and use cases the framework covers. Deliverable: a written scope statement.
Establish executive ownership. Assign a named executive accountable for AI security outcomes. Deliverable: a documented ownership and escalation chart.
Create an inventory of AI systems and dependencies. Include shadow AI, third-party models, and internal tools. Deliverable: a living AI system register.
Classify systems by risk and impact. Consider data sensitivity, autonomy, and potential harm. Deliverable: a risk tier assigned to every inventoried system.
Map applicable laws, standards, and contractual duties. Deliverable: an obligations map tied to each system.
Define the threat model. Use MITRE ATLAS and OWASP as inputs. Deliverable: a documented threat model per high-risk system.
Select control baselines. Choose which frameworks and controls apply at each risk tier. Deliverable: a control baseline document.
Assign control owners. Every control needs a named, accountable owner. Deliverable: a RACI or ownership matrix.
Establish secure lifecycle gates. Build checkpoints into design, development, and deployment. Deliverable: gate criteria and sign-off records.
Create testing and evaluation requirements. Define what adversarial and functional testing every system must pass. Deliverable: a testing standard.
Define deployment approval criteria. Deliverable: a formal go-live checklist.
Implement monitoring and logging. Deliverable: monitoring coverage mapped to each system.
Prepare AI-specific incident-response procedures. Deliverable: a tested incident-response playbook.
Assess third-party providers. Deliverable: vendor security questionnaires and review records.
Collect evidence and audit controls. Deliverable: an audit trail demonstrating each control is operating.
Review and improve the framework continuously. Deliverable: a scheduled review cadence with version history.
Risk Assessment and Threat Modeling for AI Systems
AI threat modeling extends conventional practice rather than replacing it. It still requires understanding system context, intended and reasonably foreseeable use, assets, trust boundaries, data flows, dependencies, and existing controls. What changes is the addition of AI-specific threat actors, attack surfaces, and abuse cases drawn from resources like MITRE ATLAS and the OWASP LLM Top 10.
For each identified threat, the same core questions apply: what is the likelihood, what is the impact, what controls already exist, what residual risk remains, and who has the authority to accept that residual risk. Review triggers should include model retraining, a new data source being added to a RAG pipeline, a new tool being connected to an agent, or a significant shift in usage volume.
Hypothetical example. A retailer builds an internal support chatbot that uses RAG over its policy documents. Threat modeling identifies indirect prompt injection through uploaded documents as the highest-priority risk, since an attacker could embed hidden instructions in a file that an employee later asks the bot to summarize. The mitigation combines treating retrieved content as untrusted, sandboxing any actions the bot can take, and logging every retrieval so an investigator can trace what content influenced a given response.
Security Controls for Generative AI, LLMs, RAG, and AI Agents
Effective controls for generative systems combine input handling, output handling, and permission boundaries, because no single layer can be trusted alone.
On the input side: validate and contextually filter input, treat all external content, including retrieved documents, as untrusted, and keep prompts and instructions structurally separated where the platform supports it. On the output side: validate model output before it is displayed, executed, or acted on, and apply data-loss-prevention checks before sensitive content leaves the system.
For agents and tool use: maintain a tool allowlist rather than open access, validate parameters before a tool call executes, sandbox execution environments, use least-privilege service accounts scoped to only what a task requires, require human approval for high-impact or irreversible actions, and set transaction or spending limits on anything involving money or resources. Isolate agent memory and sessions from one another, and enforce retrieval access controls and document provenance checks on anything a RAG system ingests.
Operationally: manage secrets outside of prompts, isolate models and providers from each other, apply rate limits and cost controls, log comprehensively, build in kill switches and graceful degradation paths, and test specifically for direct and indirect prompt injection, including recursive agent-loop failures where an agent triggers itself repeatedly.
The single most important principle across all of this: a system prompt is not a security control. OWASP's 2025 guidance is explicit that prompts are not deterministic, auditable boundaries, and any control that truly matters, such as authentication, authorization, or spending limits, has to be enforced outside the model, in code the model cannot talk its way around.
AI Security Governance, Roles, and Accountability
AI security only works when accountability is explicit rather than assumed. A simplified responsibility structure looks like this:
Role | Governance | Risk Acceptance | Technical Controls |
Executive leadership / board | Accountable | Accountable (high risk) | Informed |
CISO | Responsible | Consulted | Accountable |
AI governance committee | Responsible | Responsible | Consulted |
Model/product owners | Consulted | Consulted | Responsible |
ML/security engineering | Informed | Informed | Responsible |
Legal, privacy, and compliance | Consulted | Consulted | Informed |
Internal audit provides independent assurance that controls are actually operating, procurement vets AI vendors before contracts are signed, and end users carry a lighter but real responsibility: reporting suspicious model behavior and following acceptable-use policies. Ambiguity about ownership is one of the most common root causes behind unaddressed AI risk, more often than any missing technical control.
Testing, Red Teaming, Monitoring, Metrics, and Incident Response
Security testing for AI systems has to go beyond functional QA. AI red teaming simulates adversarial behavior deliberately, covering abuse-case testing, prompt-injection testing, data-leakage testing, and permission and tool testing for anything with agentic capability. Supply-chain scanning and dependency monitoring extend traditional software composition analysis to models and datasets. Drift monitoring tracks whether a model's behavior or accuracy is degrading over time in ways that could also signal manipulation.
Metrics fall into distinct categories that are often conflated. Key performance indicators track operational health, such as testing coverage across the AI inventory. Key risk indicators flag emerging exposure, such as a rising number of unreviewed shadow AI tools. Control-effectiveness metrics measure whether a specific control is catching what it was built to catch. Outcome metrics track real incidents and their impact. None of these should be presented as an industry benchmark unless it comes from a verified source; internal metrics are for internal comparison over time.
Incident response for AI needs its own playbook elements: triage that can distinguish a model quality issue from an active attack, containment options including rate-limiting or fully disabling a compromised system, model rollback to a known-good version, credential rotation for any exposed service accounts, investigation of what data may have been exposed or manipulated, and a defined notification and escalation path. A post-incident review should feed back into the threat model and control baseline, not just into a report that no one reads again.
AI Security Maturity Model and Implementation Roadmap
Level | Name | Characteristic Evidence |
1 | Ad hoc | No formal inventory; controls applied inconsistently, if at all |
2 | Repeatable | Basic inventory exists; some controls applied per project |
3 | Defined | Documented framework, control baselines, and assigned ownership |
4 | Measured | Metrics tracked; testing and monitoring are consistent and evidenced |
5 | Adaptive | Framework updates continuously based on new threats and incidents |
A realistic rollout prioritizes foundational work before advanced tooling:
First 30 days: Build the AI system inventory, assign executive ownership, and classify existing systems by risk tier.
Days 31–60: Select framework baselines, draft control requirements, and begin threat modeling the highest-risk systems.
Days 61–90: Implement monitoring and logging for priority systems, and run the first round of adversarial testing.
Months 4–6: Formalize deployment gates, complete third-party vendor reviews, and build the incident-response playbook.
Months 7–12: Extend coverage to lower-risk systems, run a full framework review, and pursue certification where relevant, such as ISO/IEC 42001.
Practical AI Security Framework Examples
Hypothetical example — internal employee chatbot using RAG. Main assets: internal policy documents, the vector database, and employee session data. Main threats: indirect prompt injection through uploaded documents and retrieval poisoning. Selected frameworks: NIST AI RMF for governance, OWASP LLM Top 10 for application controls. Key controls: document provenance checks, retrieval access controls, and output logging. Evidence: retrieval logs tied to each response. Residual risk: low, accepted by the IT security lead given limited data sensitivity.
Hypothetical example — customer-facing support LLM. Main assets: customer PII, brand reputation, and the model's system configuration. Main threats: jailbreaking, sensitive-information disclosure, and system prompt leakage. Selected frameworks: OWASP LLM Top 10, plus the organization's existing privacy program. Key controls: output filtering for PII, rate limiting, and a human escalation path for flagged conversations. Evidence: monthly red-team reports. Residual risk: medium, accepted by the product owner and reviewed quarterly.
Hypothetical example — software-development agent with repository access. Main assets: source code, CI/CD credentials, and production deployment permissions. Main threats: excessive agency, tool abuse, and credential exposure. Selected frameworks: MITRE ATLAS for threat modeling, CISA/NCSC guidelines for secure-by-design controls. Key controls: least-privilege service accounts, mandatory human approval for merges and deploys, and sandboxed execution. Evidence: approval logs for every agent-initiated action. Residual risk: elevated, accepted by engineering leadership with a defined review trigger tied to any permission-scope change.
Common AI Security Framework Mistakes
Organizations repeatedly fall into the same set of avoidable errors: treating the model as the entire system rather than one component among many; buying a security product without first establishing governance; copying a framework wholesale without tailoring it to actual systems; focusing only on prompt injection while ignoring data provenance and supply-chain risk; giving agents more permissions than their task requires; treating system prompts as if they were secrets or enforceable security controls; skipping third-party risk reviews for AI vendors; failing to inventory shadow AI; testing only before launch instead of continuously; collecting logs that no one ever reviews; confusing a safety evaluation with a security test; claiming compliance without evidence to back it up; ignoring decommissioning and data deletion at end of life; and creating policies without assigning a named, accountable owner.
How to Choose the Right AI Security Framework
The right combination depends on organization size, industry, jurisdiction, risk tolerance, existing ISO or NIST programs, the mix of internal versus third-party models in use, regulatory exposure, certification goals, technical maturity, and available resources. A startup with a single low-risk internal tool needs far less than a regulated financial institution deploying multiple customer-facing models.
As an example combination, not a universal prescription: an organization might use NIST AI RMF for its risk structure, ISO/IEC 42001 for the management system itself, ISO/IEC 27001 for the information-security foundation underneath everything, the OWASP LLM Top 10 for application-level risks, MITRE ATLAS for adversary behavior and threat modeling, CISA/NCSC guidance for secure lifecycle practices, and whatever laws actually apply, such as the EU AI Act for EU market exposure. Smaller organizations can start with a lighter version of the same logic: one risk framework, one technical checklist, and a documented owner, expanding as AI use grows.
The Future of AI Security Frameworks
Several trends are shaping how this space will keep evolving. Agentic systems with real-world permissions are pushing frameworks toward stricter action-level controls rather than just content-level ones. Multimodal models expand the attack surface beyond text into images, audio, and video. AI supply-chain assurance, including provenance for models and datasets, is becoming a distinct discipline in its own right, mirroring how software supply-chain security matured over the past decade.
Standardized evaluations and shared benchmarks are slowly emerging, though consensus is far from complete. Security evidence and auditability are becoming expected outputs of AI programs, not just internal documentation. Automated control monitoring is starting to replace point-in-time audits with continuous assurance. Regulatory activity is both converging, as frameworks borrow language and structure from each other, and diverging, as jurisdictions set their own binding rules on different timelines. Sector-specific requirements are emerging in healthcare, finance, and critical infrastructure. Static checklists have real limits in a domain where the underlying technology and threat landscape both change every few months; the frameworks that hold up best are the ones built to be revised, not the ones treated as finished.
Conclusion
An AI security framework is not a single document to download and file away. It is the combination of governance, technical controls, testing, and lifecycle discipline that lets an organization use AI without losing control of the data, models, and decisions behind it. NIST AI RMF, ISO/IEC 42001, OWASP's LLM guidance, MITRE ATLAS, and CISA/NCSC's secure development guidelines each cover a different layer of that problem, and the EU AI Act adds a binding legal floor for high-risk systems. Building a working framework means combining the right pieces for a specific organization, assigning real ownership, and revisiting the whole thing as both the technology and the threats keep changing.
Frequently Asked Questions About AI Security Frameworks
What is an AI security framework?
An AI security framework is a structured set of governance practices, risk-management processes, technical controls, and lifecycle procedures that protect AI systems, their data, models, and users. It can be an adopted external standard, such as NIST AI RMF, or an internal program a company builds from multiple standards and controls.
Why is an AI security framework needed?
Traditional application security controls do not address AI-specific risks such as data poisoning, prompt injection, model theft, and agentic tool abuse. AI systems are probabilistic and data-dependent, which breaks assumptions that conventional firewalls and code scanners rely on. A dedicated framework closes that gap.
What is the difference between AI security and AI safety?
AI security focuses on protecting AI systems from attack and misuse, such as prompt injection or model theft. AI safety focuses on preventing unintended or harmful model behavior, including at larger scale. The two overlap but are handled by different teams with different priorities.
Is NIST AI RMF a security framework?
NIST AI RMF is a broader risk-management framework covering trustworthiness characteristics like validity, safety, and fairness, not a security-only framework. It is voluntary guidance, not a certifiable standard, and organizations often pair it with more technical resources like OWASP's LLM guidance for security-specific controls.
Is ISO/IEC 42001 a security standard?
ISO/IEC 42001 is a management-system standard for AI governance, similar in structure to ISO/IEC 27001 for information security. It covers risk management, lifecycle management, and supplier oversight, and it is certifiable through accredited audit, but it is not a technical security control catalog on its own.
Can an organization become certified to NIST AI RMF?
No. NIST AI RMF is voluntary guidance without a certification program. Organizations can align their practices with it and document that alignment, but there is no accredited "NIST AI RMF certified" status, unlike ISO/IEC 42001, which does have a formal certification path.
What is the best AI security framework?
There is no single best framework because each one covers a different layer. Most organizations combine a risk framework like NIST AI RMF or ISO/IEC 42001 with technical guidance like the OWASP LLM Top 10 and threat intelligence from MITRE ATLAS, tailored to their own systems and regulatory exposure.
How does MITRE ATLAS differ from OWASP's LLM guidance?
MITRE ATLAS is a knowledge base of adversary tactics and techniques across all AI and ML systems, useful for threat modeling and red-team planning. OWASP's Top 10 for LLM Applications is a prioritized list of the most critical risks specifically in LLM-based applications, aimed more directly at developers.
What security risks are unique to generative AI?
Generative AI introduces prompt injection, jailbreaking, system prompt leakage, insecure output handling, and hallucination-related security consequences that do not apply to traditional predictive models. RAG systems add retrieval poisoning and vector-database risks on top of these.
How do you secure an AI agent?
Secure an AI agent with least-privilege service accounts, a tool allowlist, human approval for high-impact or irreversible actions, sandboxed execution, spending or transaction limits, and isolated memory between sessions. Never rely on the agent's own instructions as the only safeguard.
What should an AI security policy include?
An AI security policy should define scope, ownership, risk classification criteria, approved frameworks and control baselines, testing and evaluation requirements, deployment approval gates, monitoring expectations, incident-response procedures, and a defined review cadence.
How often should an AI security framework be reviewed?
Most organizations review their framework at least annually, with additional reviews triggered by major model changes, new AI use cases, new regulations, or after a significant incident. Continuous monitoring should feed into these reviews rather than replace them.
Does the EU AI Act require cybersecurity controls?
Yes. Article 15 of the EU AI Act legally requires high-risk AI systems to achieve an appropriate level of accuracy, robustness, and cybersecurity throughout their lifecycle, including specific resilience against data poisoning, model poisoning, and adversarial examples.
Can small businesses implement an AI security framework?
Yes. A small business can start with a lightweight version: an inventory of AI tools in use, a basic risk classification, one technical checklist such as the OWASP LLM Top 10, and a named owner. The framework can scale up as AI use and risk grow.
How do you measure AI security maturity?
AI security maturity is typically measured against a staged model, from ad hoc practices with no formal inventory, through repeatable and defined stages, up to measured and adaptive stages where metrics, testing, and monitoring are consistent and the framework updates continuously.
Who is responsible for AI security?
Responsibility is shared but should be explicit. Executive leadership is accountable for outcomes, the CISO is typically responsible for the security program, an AI governance committee oversees risk decisions, and model or product owners are responsible for the controls on their specific systems.
Key Takeaways
An AI security framework can mean an adopted external standard or an internal program built from multiple standards, and most mature organizations use both.
Protection has to cover the full AI stack, including data, models, prompts, embeddings, agents, pipelines, and infrastructure, not just the model file.
NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, the OWASP LLM Top 10, MITRE ATLAS, and CISA/NCSC guidance each cover a different layer, and they work best combined rather than chosen individually.
The EU AI Act's Article 15 is the only entry on this list with direct legal force, requiring cybersecurity resilience for high-risk systems from August 2, 2026.
Security has to run across the entire AI lifecycle, from data acquisition through decommissioning, not just at the point of deployment.
A system prompt is not a security boundary; enforceable controls have to sit outside the model in code an attacker cannot talk their way around.
Clear ownership, documented evidence, and a recurring review cycle matter as much as any individual technical control.
Actionable Next Steps
Inventory every AI system in use, including shadow AI tools employees have adopted without review.
Assign a named, accountable owner for AI security at the executive level.
Classify each system by risk and impact based on data sensitivity and autonomy.
Map the laws, standards, and contractual duties that apply to each system.
Select complementary frameworks, such as NIST AI RMF for structure and OWASP LLM Top 10 for technical controls.
Threat-model the highest-risk systems first, using MITRE ATLAS as a reference.
Establish minimum control baselines tied to each risk tier.
Run adversarial testing on any system with generative, RAG, or agentic capability.
Define monitoring coverage and a tested incident-response playbook.
Set a recurring review cycle, at minimum annually, with triggers for major changes.
Glossary
Adversarial machine learning — The study of attacks on machine learning systems and the defenses used against them.
AI agent — A software system that uses AI to plan and take actions, often by calling external tools or APIs.
AI governance — Policy, oversight, and accountability structures that guide how an organization develops and uses AI.
AI red teaming — Structured adversarial testing designed to find security and safety weaknesses in an AI system before attackers do.
AI risk — The potential for an AI system to cause harm, financial loss, or unintended outcomes.
AI security — The practice of protecting AI systems, their data, and their models from attack, misuse, and unauthorized access.
AI security framework — A structured set of governance practices, risk processes, technical controls, and lifecycle procedures that protect AI systems.
Backdoor — A hidden trigger planted in a model during training that activates unintended behavior under specific conditions.
Data poisoning — Corrupting training or fine-tuning data so a model learns incorrect or malicious behavior.
Embedding — A numerical representation of text, images, or other data that captures semantic meaning for use in machine learning.
Evasion attack — An adversarial input crafted to cause a model to misclassify or behave incorrectly without altering the model itself.
Excessive agency — Granting an AI agent more permissions or autonomy than its task actually requires.
Foundation model — A large, general-purpose model trained on broad data that can be adapted to many downstream tasks.
Guardrail — A control designed to constrain AI model behavior, though guardrails alone are not a substitute for enforced security controls.
Hallucination — Confident, fabricated model output that is not grounded in fact or source data.
Human-in-the-loop — A design pattern requiring human review or approval before an AI system's decision or action takes effect.
Inference — The process of a trained model producing output in response to new input.
Jailbreak — A technique used to bypass a model's built-in behavioral restrictions.
Large language model (LLM) — A neural network trained on large amounts of text to generate and understand natural language.
MLOps — The practices and tooling used to deploy, monitor, and maintain machine learning models in production.
Model drift — Gradual degradation in a model's accuracy or behavior over time as real-world conditions change.
Model extraction — Reconstructing a proprietary model's behavior or parameters through repeated querying.
Model inversion — An attack that attempts to reconstruct sensitive training data from a model's outputs.
Model provenance — Documented information about a model's origin, training data, and modification history.
Prompt injection — Manipulating an AI model's behavior through crafted input, either directly or through content it later processes.
RAG (retrieval-augmented generation) — An architecture that retrieves external content at runtime to ground a model's response.
Residual risk — The risk that remains after controls have been applied, which must be formally accepted by someone with authority to do so.
Shadow AI — AI tools or systems adopted within an organization without security or governance review.
Supply-chain attack — An attack that compromises a system through a third-party component, such as a pretrained model or dataset.
System prompt — The instructions given to a model to shape its behavior, which should never be treated as a security control.
Threat modeling — A structured process for identifying assets, threats, and mitigations for a system before or during design.
Training-data extraction — Recovering memorized content, sometimes including personal data, directly from a deployed model.
Vector database — A database optimized to store and search embeddings, commonly used to power retrieval-augmented generation.
Zero trust — A security model that requires verification for every access request, regardless of whether it originates inside or outside the network perimeter.
Sources & References
National Institute of Standards and Technology. "Artificial Intelligence Risk Management Framework (AI RMF 1.0)." NIST, January 26, 2023. https://www.nist.gov/itl/ai-risk-management-framework
National Institute of Standards and Technology. "NIST-AI-600-1: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile." NIST, July 26, 2024. https://www.nist.gov/itl/ai-risk-management-framework
International Organization for Standardization. "ISO/IEC 42001:2023 — Information technology — Artificial intelligence — Management system." ISO, December 2023. https://www.iso.org/standard/42001
International Organization for Standardization. "ISO/IEC 23894:2023 — Information technology — Artificial intelligence — Guidance on risk management." ISO, February 2023. https://www.iso.org/standard/77304.html
International Organization for Standardization. "ISO/IEC FDIS 27090 — Cybersecurity — Artificial Intelligence — Addressing security threats and compromises to artificial intelligence systems." ISO, 2026 (draft, FDIS stage). https://www.iso.org/standard/56581.html
OWASP GenAI Security Project. "OWASP Top 10 for LLM Applications 2025." OWASP, March 12, 2025. https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/
MITRE Corporation. "MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems." MITRE, 2024 (living resource). https://atlas.mitre.org/
Cybersecurity and Infrastructure Security Agency and National Cyber Security Centre (UK). "Guidelines for Secure AI System Development." CISA/NCSC, November 26, 2023. https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development
Google. "Introducing Google's Secure AI Framework (SAIF)." Google, 2023. https://blog.google/innovation-and-ai/technology/safety-security/introducing-googles-secure-ai-framework/
European Union. "Regulation (EU) 2024/1689 — Artificial Intelligence Act, Article 15: Accuracy, Robustness and Cybersecurity." Official Journal of the European Union, June 13, 2024. https://artificialintelligenceact.eu/article/15/
European Union Agency for Cybersecurity (ENISA). "Multilayer Framework for Good Cybersecurity Practices for AI." ENISA, June 2023. https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development
National Cyber Security Centre (UK) and Cybersecurity and Infrastructure Security Agency, National Security Agency, and partner agencies. "Deploying AI Systems Securely." NSA/CISA, April 2024. https://media.defense.gov/2024/apr/15/2003439257/-1/-1/0/csi-deploying-ai-systems-securely.pdf
International Organization for Standardization. "ISO/IEC 27001 — Information security management systems." ISO. https://www.iso.org/standard/27001
