AI Security Framework: What Enterprises Need in 2026

Key Takeaways

• An AI security framework maps the threats to your AI systems onto controls, so each one gets an owner, a test and evidence.
• No single framework covers everything, so layer one management standard, one threat catalog and one control catalog.
• NIST AI RMF suits US-led programs, while ISO/IEC 42001 fits when customers or regulators want a certificate.
• OWASP Top 10 lists and MITRE ATLAS turn abstract AI risk into attacks your team can test before release.
• AI agents need their own identities and scoped permissions, as well as human approval for high-impact actions.
• Start with an AI inventory and access enforced at retrieval, since 97% of organizations with AI breaches lacked AI access controls (IBM, 2025).

When an AI Assistant Becomes a Data Exposure Path

Picture a finance analyst who asks the company’s new AI assistant to summarize a quarterly pricing deck. The answer comes back fast, and at first it looks accurate. It also quotes salary bands from an HR folder that the analyst could never open.

Nobody hacked anything, though. The assistant’s search index was built with a service account that could read every folder, so it answered from files its user could never see. In other words, the permission check happened at the app’s front door instead of where the AI retrieved the data.

Real incidents also follow this pattern. Between August 8 and 18, 2025, a threat actor tracked as UNC6395 stole Salesforce data using compromised OAuth tokens, according to Google Threat Intelligence. The tokens belonged to the Salesloft Drift third-party app, so the theft rode on trust the app already had.

That same month, Cybernews researchers showed that a single prompt could make Lenovo’s chatbot, Lena, leak session cookies. A roughly 400-character prompt made Lena reply in HTML that ran in support agents’ browsers. Unvalidated model output thus became a way to steal their sessions, which is why output validation appears in the crosswalk later.

A security framework built for AI exists to catch these gaps before they ship. This guide explains what one is and then compares the main options. It also shows how to combine them into controls your teams own and test.

What Is an AI Security Framework?

An AI security framework is a structured way to find the threats that target your AI systems and answer each one with a control. Every control then gets an owner, a test that proves it works and evidence an auditor can check.

These threats differ from classic IT risks because they run through language and data. For example, a model can be steered through its inputs, leak data through its outputs or overreach when it calls tools. A framework names those threats, and it also decides who fixes each one and how you prove it stayed fixed.

The word “framework” also covers several kinds of documents. Some are management standards, while others are threat lists or control catalogs. Enterprises need at least one of each, so the comparison later in this guide matters more than any single pick.

AI Security vs AI Governance vs Traditional Cybersecurity

These three disciplines overlap, but each answers a different question. AI governance decides whether and how the business should use AI, including its responsible AI principles. AI security protects AI systems from attack and misuse, while traditional cybersecurity protects the networks, endpoints and cloud accounts underneath them.

A gap in any one of them leaves the others exposed. Even a well-governed model on a patched server can leak data when nobody tests its prompts.

Which AI Systems Need Coverage

Scope is where many programs quietly fail, because AI now arrives through many doors. A framework should cover every system that uses a model to read, generate or act on company data. That includes far more than the models your data science team trains.

• Models you train or fine-tune, together with their training data.
• Retrieval-augmented generation (RAG) apps, especially those that answer from internal documents.
• Copilots inside productivity suites, which also raise their own Copilot security concerns.
• AI agents, particularly those that call tools, APIs or other agents.
• AI features inside SaaS products, which often switch on during a routine vendor update.

The last item is the easiest to miss. When a CRM or HR platform adds an AI feature, that feature can reach the platform’s data on day one. It belongs in the inventory as well.

Why AI Security Frameworks Matter More in 2026

AI has moved from pilots into daily work, so attackers have followed it there. As enterprise AI adoption spreads across departments, the number of exposed systems grows faster than security teams can review them.

IBM’s 2025 breach research puts numbers on that gap. In IBM’s July 2025 report, 13% of organizations reported breaches of AI models or applications. Of those, 97% still lacked proper AI access controls.

The same study found that one in five organizations had a breach due to shadow AI. Organizations with high levels of shadow AI also saw $670,000 higher average breach costs. Even so, only 37% of organizations had policies to manage AI or detect shadow AI.

Three shifts widen the gap further in 2026:

• Agents widen the blast radius. An agent with write access can move money, change records or send email. So one manipulated prompt can become a real action.
• AI arrives through vendors. Copilots and SaaS features bring models into the business before any security review takes place.
• Regulation keeps taking shape. The EU AI Act entered into force on August 1, 2024, according to the European Commission. Prohibitions then applied from February 2, 2025, followed by general-purpose AI obligations from August 2, 2025.

The AI Omnibus, Regulation (EU) 2026/1744, entered into force on July 27, 2026. It moved stand-alone (Annex III) high-risk obligations to December 2, 2027 and product-embedded (Annex I) ones to August 2, 2028. The deadlines moved, but the duties did not disappear. Our guide to AI compliance covers that regulatory side, while the security framework turns those duties into working controls.

The Main AI Security Frameworks Compared

Each major framework was written to do one job well, but none of them does every job. So the useful question is not which framework is best. Instead, ask which layer each one covers: managing risk, naming threats or defining controls.

The table below compares the seven you are most likely to meet. Read the last column first, because it shows where each framework stops and another must take over.

The sections below explain each one, starting with the two management standards.

NIST AI RMF and the Generative AI Profile

The NIST AI Risk Management Framework is a natural foundation for US enterprises, because it comes from the federal standards body. NIST released version 1.0 on January 26, 2023, for voluntary use, so no US law requires it on its own.

The AI RMF 1.0 document organizes the work into four functions: Govern, Map, Measure and Manage. Govern sets roles and policy, while the other three find, test and treat each risk. NIST then added the Generative AI Profile, NIST-AI-600-1, on July 26, 2024.

NIST published a concept note for a critical infrastructure profile on April 7, 2026. Its AI RMF page also states that “the AI RMF 1.0 is being revised as part of the White House AI Action Plan.” Whatever the revision changes, the RMF tells you how to run a program rather than which controls to build. It therefore pairs naturally with a control catalog. The Playbook’s suggested actions are explained in NIST AI RMF Playbook, without turning them into a checklist.

ISO/IEC 42001

ISO/IEC 42001:2023, published in December 2023, is the international standard for an AI management system. It plays a role similar to ISO/IEC 27001, which does the same for information security. It defines how an organization sets policy, assesses AI risk, applies controls and improves over time.

Its main advantage is assurance, because organizations can be audited and certified against it by accredited bodies. That gives customers and regulators evidence instead of a promise. For a SaaS vendor selling AI into regulated industries, the certificate can also shorten security reviews. What the Stage 1 and Stage 2 audits involve, and what they cost, is covered in ISO 42001 certification.

OWASP Top 10 for LLM and Agentic Applications

The OWASP lists are where abstract AI risk finally becomes something an engineer can test. The OWASP Top 10 for LLM Applications 2025 edition ranks the most critical risks in apps built on large language models. Several entries map directly to the incidents above:

• LLM01:2025 Prompt Injection, where hostile instructions in user input or retrieved content change what the model does.
• LLM02:2025 Sensitive Information Disclosure, where a model reveals data it should not.
• LLM10:2025 Unbounded Consumption, where runaway usage drives up cost or blocks service.

OWASP then published the Top 10 for Agentic Applications for 2026 on December 9, 2025, as a peer-reviewed list of risks for autonomous AI. Our overview of generative AI risks also covers the business side of the same threats.

Prompt injection deserves special attention because it needs no stolen password. A crafted sentence in an email or shared document is enough when the model treats it as an instruction.

MITRE ATLAS

MITRE ATLAS is a public knowledge base of adversary tactics, techniques and procedures that target AI systems. It does for AI what MITRE ATT&CK does for enterprise IT, since it describes how real attackers work.

Security teams generally use it in two ways. First, threat modelers walk an AI system through the ATLAS tactics to see where an attacker could get in. Red teams then use the same techniques to plan realistic tests, so their findings map to a shared vocabulary.

Google SAIF

Google’s Secure AI Framework (SAIF) is a conceptual framework built on six core elements, such as expanding strong security foundations to the AI ecosystem. The other five extend detection and response, automate defenses and harmonize platform controls. They also adapt controls through faster feedback and place AI risk in its business context.

SAIF’s message is that AI security should grow out of the program you already run, rather than start from scratch. It works well as a frame for leadership, although teams still need a detailed control set to put it into practice.

CSA AI Controls Matrix and MAESTRO

When teams need a control list, the Cloud Security Alliance publishes a detailed one that is free to use. Its AI Controls Matrix (AICM) holds 243 control objectives across 18 security domains. It also maps to ISO 42001, ISO 27001, NIST AI RMF 1.0 and BSI AIC4, so it bridges the management standards and daily engineering.

CSA also published MAESTRO on February 6, 2025, as a threat-modeling framework for agentic AI. It splits an agent system into seven layers, so teams can trace a threat from the model up to the wider agent ecosystem.

Platform Frameworks: AWS and Databricks DASF

Cloud and data platforms also publish their own frameworks, which translate the general guidance into controls for their services. For example, AWS’s own framework, published May 15, 2026 and updated May 26, sorts controls into three phases. Phase 1 covers zero to prototype, Phase 2 covers prototype to production, and Phase 3 covers continuous improvement and scale.

The Databricks AI Security Framework (DASF) comes from a vendor, yet it is written to be platform-neutral. Its own page says teams can follow its four steps no matter which platform they use. Those steps build an AI risk profile and then map each risk to concrete controls. AWS’s framework, by contrast, stops at the AWS boundary, so both work best as one layer inside a wider program.

How to Combine Frameworks Without Duplicating Work

The simplest way to combine frameworks is to give each one a single job. Pick one management standard to run the program and one or two threat catalogs to name the attacks. Then add one control catalog to define what gets built.

For a US enterprise, that might mean NIST AI RMF for management and the CSA AI Controls Matrix for controls. OWASP and MITRE ATLAS would then cover threats. A company selling into Europe might instead swap in ISO/IEC 42001, because it can be certified.

The control catalog stays the same either way, since the AICM already maps to both.

The mistake to avoid is running each framework as its own project. Three teams writing three risk registers produce three versions of the truth, so none of them gets fixed. Instead, one shared control list, tagged with the framework references it satisfies, keeps the work in one place.

One Risk Through Four Frameworks: Prompt Injection

A crosswalk shows how this works in practice for a single risk. The table follows prompt injection through each framework and then ends with one control, one owner and one evidence artifact that satisfy all of them.

One control, written once, now answers four frameworks. When an auditor asks about ISO 42001 risk treatment, the same report also serves a customer who asks about OWASP LLM01.

Core Components of an Enterprise AI Security Framework

Whatever frameworks you choose, the controls tend to fall into the same seven areas. Think of the frameworks as the building code, while these areas are the rooms where the work actually happens.

Where to Focus First: Data and Supply Chain

Data protection deserves the most attention, because many AI incidents are really data security incidents. Labels and access rules from your data governance framework must apply where the AI retrieves data, not only when a person opens a file. When data cannot move at all, federated learning keeps training data where it lives while models still learn from it.

Model and supply chain controls then start with a model registry. It records where each model came from, who approved it and which version runs in production. Third-party models, plugins and datasets also deserve the same scrutiny you give open-source code.

Before buying new AI-specific tools, extend the ones you already own, including your data governance tools. For instance, identity platforms can issue agent identities, and data security posture management tools can find sensitive data in AI indexes. Your SIEM can also ingest prompt and tool-call logs, so AI-specific products only need to fill the remaining gaps.

How to Implement an AI Security Framework Step by Step

Implementation works best as a short loop that you repeat, rather than a one-off project. Each step below names an owner and the evidence that proves the step is done, so progress stays visible.

1. Inventory systems and name owners. List every model, RAG app, copilot, agent and SaaS AI feature, then give each one a business owner. An AI security assessment is a fast way to build this baseline. Evidence: a living AI inventory.
2. Tier each system by risk. Rate systems by the data they touch and the actions they can take. The risk team owns the tiers, so the evidence is a tier on every inventory row.
3. Threat model the high tiers. Security architects walk each high-tier system through OWASP and MITRE ATLAS, or through MAESTRO when it is an agent. Evidence: one threat model per system.
4. Map threats to controls. Control owners choose controls from the catalog and also record which framework references each one satisfies. Evidence: the crosswalk.
5. Verify before release. AI engineering builds prompt injection, data leakage and permission tests into its MLOps pipelines, so no model or prompt change ships untested. Evidence: test results per release.
6. Monitor and reassess. Security operations logs prompts, outputs and tool calls, much as data observability tracks pipelines, then reruns the threat model whenever a system changes. Evidence: dashboards, incident records and review dates.

How Risk Tiers Set the Pace

Risk tiering decides how deep every later step goes. For example, a customer-facing agent with write access sits in a higher tier than an internal drafting tool. It gets a full threat model, red-team testing and human approval before it acts.

The tier also decides how fast a system moves through the AWS framework’s three phases. A high-tier agent should not leave Phase 1 until it has a scoped identity and human approval for risky actions. A low-tier drafting tool, by contrast, can move to production with baseline controls and pick up the rest as it scales.

Keep the loop short and repeatable. Low-tier systems can then follow a fixed review schedule, whereas high-tier systems should be retested whenever they gain a new data source, tool or model version.

Securing AI Agents: What the Framework Must Add

Agents change the risk because they take actions as well as give answers. An autonomous agent that can read a mailbox, query a database and send payments turns a prompt injection into a financial loss.

A framework built for chatbots therefore needs four additions for agents:

• A distinct identity per agent. Each agent gets its own credentials, so its actions can be scoped, logged and revoked.
• Scoped tool permissions. An agent should reach only the tools and records its task needs, rather than a shared admin account.
• Human approval for high-impact actions. Payments, deletions and external messages wait until a person confirms them.
• Agent-specific threat models. The OWASP agentic list and CSA MAESTRO cover risks that only appear once AI can act on its own.

Salesloft Drift shows why identity matters so much. Those stolen OAuth tokens belonged to a connected app, so the theft rode on access the app already had. Scoped, revocable credentials limit what a stolen token can reach.

Multi-agent setups raise the stakes again. When several agents hand work to each other through AI agent orchestration, every hand-off needs the same identity and permission checks.

That covers the framework-level view, while our guide to agentic AI security goes deeper into agent design and runtime controls.

Which AI Security Framework Fits Your Enterprise?

The right mix depends on your sector, your regulators and what your AI actually does. In the table, the anchor framework runs the program, while the add-ons cover its gaps. The first control is where to spend the first month.

Two patterns stand out. Regulated industries anchor on a management standard because auditors ask for one. Product teams, on the other hand, start with OWASP since it produces tests they can run in a sprint.

Either way, the anchor alone is never the whole answer. Every scenario above also pairs it with a threat catalog and at least one control, because a framework without controls is only a policy.

Common Mistakes That Weaken Enterprise AI Security

Frameworks rarely fail on paper. Instead, they fail in the gaps between teams, and these six gaps are worth checking first:

• Logging prompts without tool calls. Teams keep chat logs but not the actions agents took, so an incident cannot be traced from the prompt to what changed.
• Skipping the inventory. Teams jump to tools before they know which AI systems exist, so unlisted systems never get controls.
• Treating one framework as complete. NIST AI RMF without a control catalog leaves half the work undone, and so does OWASP without a management layer.
• No identity for agents. Agents that run under shared service accounts cannot be scoped or traced when something goes wrong.
• No red teaming. Controls that were never attacked are still assumptions, which is why AI red teaming matters. So test prompts, retrieval and tool use before each major release.
• Ignoring shadow AI. Employees reach for public AI tools when approved ones are missing, so offer sanctioned options and monitor for the rest.

Each of these mistakes is cheaper to fix in design than after an incident. A short checklist review at every release gate catches most of them before they reach production.

How Kanerika Secures Enterprise AI

Kanerika treats AI security as part of the data and AI platform, rather than an add-on at the end. Our AI services follow a five-stage method that turns the frameworks above into owned, tested controls.

1. Assess. Review the AI and data security posture, especially the AI inventory and how data reaches each system.
2. Design. Build the framework and control map, together with the crosswalk that ties each control to its framework references.
3. Implement. Put data, identity and AI controls in place, such as access enforced at retrieval, agent identities and guardrails.
4. Test. Run red-team prompts and permission checks before release.
5. Operate. Monitor, respond and reassess as systems and threats change.

One example is a leading investment bank that needed fast answers from unstructured RFI documents as well as its databases. Kanerika deployed KlarityIQ for documents and Karl for database queries behind one chat interface. It also applied secure role-based access control at the retrieval layer.

That design is the opening scenario in reverse, because every answer is filtered by what the user is allowed to see. The bank reported 43% faster information retrieval and 35% higher workforce efficiency. It also reached 100% role-based compliance at the point of retrieval.

Read More: AI Governance Consulting Services for Enterprises

What Should Enterprises Do First?

Start small and concrete, since three moves in the first quarter give any program a base to build on.

First, build the AI inventory, including copilots and SaaS features, and name an owner for each system. Next, choose your anchor framework, NIST AI RMF or ISO/IEC 42001, and adopt OWASP and MITRE ATLAS as the shared threat vocabulary. Finally, enforce access at retrieval and give every agent its own identity.

The third move matters most, because access at retrieval and agent identity close the gaps behind the Salesloft Drift and assistant-leak cases in this guide.

So what does an enterprise need from an AI security framework in 2026? It needs a layered set of standards turned into controls with owners, tests and evidence, starting with the systems that touch its most sensitive data.

Frequently Asked Questions