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A more capable AI system is not a more secure one by default, and it does not move security responsibility to the model, the vendor, or the tool. An AI system still runs on servers, reads data, holds credentials, and calls other services. Each of those parts needs an owner and conventional controls, and AI adds failure modes that those controls only partly cover. The practical gap is between what an AI system can do and whether a named team in IT operations is accountable for how it is configured, monitored, and stopped.
Why capability does not replace operations
Teams often evaluate AI tools on output: accuracy, speed, and how many tasks they can handle. Security questions tend to arrive later, after the tool has been connected to email, a document store, a ticket queue, or a production database. By then it has inherited access that was granted for convenience, and nobody has written down who reviews that access or who can switch the tool off. The gap is organizational as much as technical.
AI systems still need ordinary security controls
NIST’s security and resilience guidance states that AI cybersecurity risks overlap with software and deployment risks. The concerns include confidentiality, integrity, and availability of the system and of its training and output data, along with the security of the underlying software and hardware. The same page puts the point directly: “The trustworthiness of AI technologies depends in part on how secure they are.” The model is one component in a stack that still needs conventional protection.
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Training sets, fine-tuning data, retrieval stores, prompts, and generated outputs all carry the three properties that matter for any system. Leaked records break confidentiality. Altered records break integrity, which is the root of the poisoning risk discussed below. A system that stops answering when a data store is unreachable has failed on availability.
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Software and hardware under the model
Model-serving frameworks, orchestration code, container images, accelerators, and operating systems all have vulnerabilities and patch cycles. An unpatched inference server is an ordinary vulnerability even when the model running on it is current. NIST names the underlying software and hardware explicitly for this reason.
Identities and permissions
Every AI component that calls another system needs an identity. Service accounts, API keys, and agent tool connections are often broader than necessary because they were created for a pilot and never narrowed. Scope each identity to a single task, rotate its secrets on a schedule, and review it on the same cycle as human accounts. Avoid a shared administrator key that many components use.
Configuration and network exposure
System prompts, safety settings, tool definitions, retrieval filters, and endpoint settings are configuration, and changing them can alter behavior as much as a code change. Keep them versioned, reviewed, and reversible. Place AI endpoints behind the same network segmentation and authentication as other internal services, and do not expose administrative or inference interfaces to the internet without authentication.
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Risks that are specific to AI
Conventional controls are necessary but not sufficient. NIST’s security material lists AI-specific attack areas, including evasion, model extraction, membership inference, and availability. It also states that current frameworks do not comprehensively cover the evolving AI attack surface. NIST’s adversarial machine learning taxonomy, NIST AI 100-2e2025, *Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations*, was finalized in March 2025 and gives teams a shared vocabulary for these attacks.
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Attack categories NIST describes
| Risk | What it means for an operator | Where NIST describes it |
|---|---|---|
| Evasion (adversarial examples) | Inputs are crafted so a model misclassifies or misbehaves, for example a manipulated document passed through a screening classifier. | Security and resilience page; AI RMF trustworthiness material |
| Data poisoning | Corrupted training or fine-tuning data changes what the model learns and how it responds. | AI RMF trustworthiness material |
| Model extraction | Repeated queries let an attacker reconstruct or approximate a model’s behavior. | Security and resilience page |
| Membership inference | An attacker infers whether a particular record was in the training data, which can expose sensitive information about individuals. | Security and resilience page |
| Exfiltration of models, training data, or intellectual property | Valuable assets leave the organization through system endpoints. | AI RMF trustworthiness material |
Application risks from the OWASP list
A 2026 NIST presentation reproduces the 2025 OWASP Top 10 for large language model and generative AI applications, published by the OWASP Generative AI Security Project. It is OWASP’s list, not a NIST ranking of severity. The right-hand column turns each entry into a question an operator should be able to answer.
| Risk (OWASP 2025 list) | Question an operator should be able to answer |
|---|---|
| Prompt injection | Can untrusted text in documents, web pages, or messages change the system’s instructions? |
| Sensitive information disclosure | What data can the system see, and could it reveal that data to the wrong user? |
| Supply chain | Where did each model, plugin, dataset, and library come from, and who maintains it? |
| Data and model poisoning | How do we know training and retrieval data have not been altered? |
| Improper output handling | Is model output validated before it reaches a browser, database, shell, or another system? |
| Excessive agency | Which actions can the system take without a human approval step? |
| System prompt leakage | Can a user extract the hidden instructions, and does that expose anything our controls depend on? |
| Vector and embedding weaknesses | Can retrieval data or embeddings be manipulated, or read across access boundaries? |
| Misinformation | What happens when a confident but wrong answer is acted on? |
| Unbounded consumption | Are there limits on requests, tokens, and tool calls so that cost and capacity stay bounded? |
Excessive agency is where AI stops being a content problem and becomes an operations problem. An assistant that only drafts text can be wrong. An agent that can close tickets, change firewall rules, or approve payments can act on a wrong or manipulated instruction. Autonomy level is therefore the first decision to make and the first thing to review.
How the risk changes with the deployment
The table below is an illustrative classification built from the risk categories above. It is not a measured study, and it is meant to show how oversight requirements change with what the system can touch.
| Deployment type | Main security exposure | Oversight that needs to exist | Consequence if it fails |
|---|---|---|---|
| Read-only internal assistant over company documents | Sensitive information disclosure and prompt injection through retrieved content | Access scoped to each user’s permissions; logs of queries and retrieved sources | Confidential material reaches staff who should not see it |
| Customer-facing generative assistant | Prompt injection, system prompt leakage, misinformation, unbounded consumption | Rate limits, output checks, escalation to people, abuse monitoring | Misleading advice, reputational harm, unexpected cost |
| Agent with write access to business systems | Excessive agency, misuse of credentials, third-party tool supply chain | Approval gates for high-impact actions, single-purpose identities, a tested way to pause or reverse | Unauthorized changes that spread across connected systems |
| AI decision support in operational technology | Integrity and availability of control processes; model behavior under changing conditions | Continuous monitoring, validation, and refinement; human authority over control actions | Effects on safety, reliability, and physical processes |
Who is accountable, and what that means in practice
NIST’s AI risk material treats accountability as shared. Its trustworthiness guidance states: “It is the joint responsibility of all AI actors to determine whether AI technology is an appropriate or necessary tool for a given context or purpose, and how to use it responsibly.” NIST also says accountability and transparency concern internal processes and the external setting, not only a model’s outputs.
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“Accountable IT operations” in this article means a concrete duty. For each AI system in production, a named team in IT operations owns the runtime, can see its configuration and access, and can stop or roll back the system. That does not mean NIST assigns all accountability to IT departments. A practical split looks like this:
- Business owner: decides whether the use case is appropriate, what success looks like, and who may use the system.
- Data owner: decides which datasets, documents, and records the system may read, retain, or learn from.
- Security team: sets control requirements, assesses threats, and tests for AI-specific attacks.
- IT operations: handles deployment, identities, configuration, monitoring, change control, incident handling, and the ability to suspend or roll back.
The gap opens when no one holds the runtime. For example, a vendor updates a hosted model, a developer adds a plugin, and no one in operations learns that the system’s behavior has changed.
What the frameworks say, and what they do not
NIST’s AI Risk Management Framework 1.0 was released January 26, 2023. NIST describes it as voluntary and intended to help incorporate trustworthiness into the design, development, use, and evaluation of AI products, services, and systems. It is an organizing reference, not a compliance mandate, and using it does not certify that a system is secure. NIST’s framework overview states that version 1.0 is being revised, so check NIST’s current AI Risk Management Framework pages for the revision status before citing a version.
NIST describes trustworthiness in seven characteristics:
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- Validity and reliability
- Safety
- Security and resilience
- Accountability and transparency
- Explainability and interpretability
- Privacy enhancement
- Fairness, with harmful bias managed
NIST cautions that these characteristics can trade off against one another and should be assessed in context.
Dated milestones and their status
| Date | Publication | Status as stated by the publisher |
|---|---|---|
| January 26, 2023 | NIST AI Risk Management Framework 1.0 | Voluntary; NIST’s overview states it is being revised |
| July 26, 2024 | NIST AI 600-1, Generative AI Profile | Released |
| March 2025 | NIST AI 100-2e2025, adversarial machine learning taxonomy and terminology | Finalized |
| December 3, 2025 | Joint guidance on secure AI integration in operational technology, from CISA and partner agencies | Published guidance |
| April 7, 2026 | Concept note for a Trustworthy AI in Critical Infrastructure profile, from NIST | Concept note; not a finished profile |
| Not dated | Control Overlays for Securing AI Systems (COSAiS), from NIST | In development; no completion date stated. Planned use cases include generative AI assistants, fine-tuned predictive AI, single-agent and multi-agent systems, and AI developers, built on NIST SP 800-53 and related material |
NIST also describes Dioptra as a testbed for measuring metrics, vulnerabilities, and the effectiveness of defenses. It is a tool for evaluating defenses, not a control that an operations team can switch on.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Turning a risk list into an operating program
A list of risks does not run a system. NIST’s lifecycle guidance asks teams to consider trustworthiness from pre-design through design and development, deployment, use, and testing and evaluation. The steps below translate that guidance into operational tasks. They are a practical synthesis, not a verbatim NIST checklist.
- Name the system and its accountable owner. Record the use case, intended users, the people who can approve, operate, monitor, and suspend the system, and the date of its last review.
- Inventory every part. List each model (including its vendor, hosting location, and version), dataset, prompt and system prompt, retrieval store, connected tool, API key, and endpoint. A component that is not on the list cannot be patched, monitored, or revoked.
- Apply conventional controls to the stack. Scope identities to the task, rotate secrets, patch the serving software and hardware, segment the network, and send logs to the same platform used for other production systems.
- Evaluate AI-specific risk before deployment. Test the system against the risks that apply to its use, such as prompt injection and sensitive-data disclosure for assistants, and excessive agency for tools. No single test catches every vulnerability, so record what was tested and what was not.
- Set autonomy limits. Decide which actions are read-only, which need human approval, and which are never allowed. Document how to pause the system and how to roll back to the previous model or configuration.
- Monitor after deployment. Watch for changes to models, data, configurations, and integrations. Log model inputs, outputs, and tool calls where policy allows, and alert on unusual patterns such as sudden request volume or new tool use.
- Connect detection to response. Add AI components to the incident playbook: how to disable a tool connection, revoke a credential, quarantine a dataset, and notify affected parties where obligations apply.
- Reassess on triggers. Review the risk decision when the model is updated, a data source or tool is added, a new use case is proposed, an incident occurs, or on a fixed schedule.
Operational technology and critical infrastructure
Joint guidance on secure AI integration in operational technology (OT), published December 3, 2025 by CISA and partner agencies including ASD’s Australian Cyber Security Centre, says AI in OT can create risks that require careful management to support safety, security, and reliability. It addresses machine learning, LLM-based AI, and agents. Its central operational recommendation is to continuously monitor, validate, and refine AI models.
That guidance is scoped to OT and critical infrastructure. It is not a universal rule for every business AI deployment. Organizations outside OT can adopt the same monitoring and revalidation discipline, but they should treat it as a sound practice of their own rather than a requirement the guidance imposes on them.
Warning signs that the gap is open
- No single named person can say who answers for the AI system’s security outcome.
- AI tools authenticate with shared keys, long-lived tokens, or administrator-level service accounts.
- Nobody can produce a current list of models, prompts, data sources, and connected tools.
- An agent can write to production systems with no approval step and no tested way to stop it.
- Logs cover infrastructure events but not model inputs, outputs, or tool calls.
- Prompt and model changes skip the change control applied to application code.
- Incident playbooks do not mention AI components.
What is not established
The sources behind this article give no measured prevalence of AI security incidents, no breach rates, no cost figures, and no effectiveness percentages for any control. The deployment table is an illustrative reading of the risk categories, not a measured study. The sources also do not rank AI products or vendors against one another, so no product recommendation follows from this article.
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