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Short answer: AI security keeps the traditional application-security foundation, then expands the threat model, lifecycle controls, and testing to cover AI data, models, behavior, and connected capabilities where those components exist. Secure code, dependencies, infrastructure, identities, secrets, and confidentiality, integrity, and availability still matter; they are no longer the whole security boundary.
An AI feature can be vulnerable as ordinary software, through the data and model it uses, through unexpected model behavior, or through tools and services it can reach. The practical change is not replacing AppSec with a separate discipline. It is adding AI-specific questions and evidence to the same engineering and governance process.
What stays the same: the AppSec foundation
AI systems still run on software, hardware, networks, identities, and operational processes. A chatbot, recommendation service, document-search assistant, or autonomous workflow inherits the security weaknesses of its web application, APIs, cloud environment, databases, build pipeline, and staff accounts. Traditional controls therefore remain the first layer:
- Secure design, code review, static and dynamic analysis, dependency management, patching, and vulnerability response.
- Strong authentication, authorization, least privilege, tenant isolation, session protection, and auditable administration.
- Secret storage and rotation, encryption where appropriate, secure configuration, network segmentation, and hardened hosts and containers.
- Protection of confidentiality, integrity, and availability for application data and the systems that process it.
- Backups, recovery procedures, logging, monitoring, incident response, and controlled release processes.
NIST summarizes the relationship directly: “Some cybersecurity risks related to AI systems are common (or identical) to cybersecurity risks across software development and deployment.” The sentence appears in NIST’s AI Research – Security and Resilience material. Treating an AI component as exempt from ordinary software security creates an avoidable gap.
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The distinction is scope. An application-security review may stop at the code, services, and data stores that make up an application. An AI-security review must also ask how data was collected and transformed, how a model was selected or trained, how its behavior is evaluated, what can influence its context, and what it can do after producing an output.
Where the security boundary expands
| Axis | Traditional application security | Additional AI security scope |
|---|---|---|
| Assets | Application code, dependencies, configurations, identities, secrets, infrastructure, and data. | Those assets plus training, test, and retrieval data; models and parameters; embeddings or memory; model-serving and orchestration components. |
| Threat model | Risks to confidentiality, integrity, availability, identity, and application behavior. | The baseline risks plus data poisoning, adversarial manipulation, model extraction or inference, and AI-enabled abuse. Agents can add tool and service interactions. |
| Verification | Secure development, code review, static and dynamic analysis, penetration testing, and conventional vulnerability response. | Baseline verification plus AI-specific requirements and tests across data, model, behavior, orchestration, monitoring, and change control. |
| Lifecycle | Build, release, operate, and update software. | Acquire, curate or train data and models; evaluate; deploy; monitor; change; and safely retire AI components. |
| Framework use | General secure-development and application-security standards. | AI-risk and AI-specific controls applied alongside conventional standards; no single framework is a universal replacement. |
Not every AI deployment has a training pipeline, a retrieval index, persistent memory, or autonomous agents. Scope the review to components that actually exist, while documenting why omitted components are out of scope.
How the AI threat model changes
Data poisoning and integrity failures
AI systems depend on data beyond the records a conventional application stores. Training sets, fine-tuning material, evaluation sets, retrieval documents, labels, feedback, and telemetry can all influence behavior. If an attacker or an untrusted supplier changes that material, the model may learn an unwanted behavior or return corrupted information.
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Controls should establish provenance for each important dataset, restrict who can add or modify records, separate training and evaluation material, validate formats and labels, and retain versioned hashes or equivalent evidence. Review ingestion jobs and annotation tools as carefully as production APIs. A retrieval-augmented system also needs controls for document ownership, indexing, deletion, and re-indexing; otherwise a removed or altered source can continue influencing responses.
Adversarial inputs and prompt injection
AI inputs are not limited to typed user prompts. They can include uploaded files, web pages, retrieved documents, images, tool results, conversation history, and hidden system context. An attacker may craft content that causes a model to ignore an instruction, reveal data, produce an unsafe recommendation, or request a privileged tool action.
Input validation still applies, but it is not sufficient to look only for malicious strings. Test how the system separates instructions from untrusted content, whether retrieved text can override policy, and whether an attacker can smuggle commands through a file or external page. Enforce authorization outside the model: a model should not be the sole decision-maker for access to records, payment actions, administrative functions, or secrets.
Model extraction and membership inference
Models and their outputs can be valuable assets. Repeated, carefully chosen queries may reveal information about model behavior, training examples, or proprietary capabilities. Model extraction attempts to approximate a model by collecting many outputs; membership-inference attempts seek evidence that a particular record was included in training.
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Behavior and output risks
Traditional applications generally execute defined logic. A generative or probabilistic model can produce incorrect, incomplete, biased, or unsafe output even when the surrounding code is functioning as designed. Security work therefore includes defining acceptable behavior, identifying high-impact failure modes, and placing deterministic checks around model output.
Output controls may include schema validation, content and policy filters, citation or provenance requirements, human approval for consequential actions, and safe handling when the model is uncertain or unavailable. Do not treat a fluent answer as proof that a request was authorized or that a fact is correct. The application must enforce permissions and business rules independently.
Model, data, hosting, and supply-chain exposure
An AI feature can depend on a foundation model, an open model file, a hosted inference service, a vector database, a data-labeling provider, evaluation tools, or a model-serving runtime. Each dependency adds questions about origin, integrity, licensing, update practices, access, and incident notification.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsMaintain an inventory that identifies the model version, provider or repository, data sources, runtime, configuration, prompts or policies, and downstream services. Verify artifacts before deployment, restrict who can replace them, scan the surrounding software and container images, and record the approval for each change. A supplier review should cover how data and prompts are handled, not only the provider’s conventional infrastructure controls.
Agent and tool abuse
Some AI systems only return text. Others can call APIs, run code, send messages, modify tickets, query internal databases, or coordinate multiple services. Those capabilities create an additional control plane. A prompt-injection attack becomes more serious when the model can turn untrusted text into an external action.
Give each tool a narrowly scoped identity, explicit input and output contracts, and independent authorization checks. Separate read and write capabilities, require confirmation for irreversible or high-impact actions, constrain destinations, and log the model request, tool call, result, and human approval where applicable. If the deployment has no tools or autonomous orchestration, record that fact instead of applying agent controls that do not fit.
The lifecycle is longer than a software release
Acquire and curate
Before development, identify why the AI component is needed, what decisions it may influence, and which data and model suppliers are involved. Classify sensitive data, establish retention and deletion rules, and define acceptable use. For third-party models or datasets, record provenance, version, license or contractual constraints, security contacts, and the conditions under which updates are accepted.
Train, fine-tune, or configure
Protect training and fine-tuning environments like production systems. Limit access to raw data, separate duties for data preparation and model approval, and keep reproducible records of datasets, code, parameters, prompts, and evaluation results. A configuration-only system still needs change control for system instructions, retrieval sources, safety policies, and tool permissions.
Evaluate before release
Evaluation must cover both ordinary software behavior and AI-specific failure modes. Establish representative, sensitive, adversarial, and out-of-distribution test cases. Test prompt injection, malicious documents, poisoning scenarios, leakage attempts, unsafe tool requests, refusal behavior, and degradation when a dependency or model is unavailable. Record thresholds, known limitations, and the decision to release; do not rely on a single favorable demonstration.
Deploy and operate
Production controls should identify which model and data versions are active, protect serving endpoints, isolate tenants, and limit access to logs because prompts and outputs may contain confidential information. Monitor for unusual query volume, repeated extraction attempts, policy violations, unexpected tool calls, changes in output quality, and drift in incoming data. Alerting should lead to an actionable response, such as throttling, disabling a tool, reverting a model, or routing work to a human.
Update and retire
Model updates, retrieval refreshes, prompt changes, provider substitutions, and library upgrades can alter security behavior without changing application code. Re-run relevant evaluations, obtain approval, and preserve a rollback path. When retiring a model or dataset, revoke credentials, remove indexes and caches where required, update documentation, and confirm that dependent workflows no longer call the retired component.
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Code review, dependency scanning, penetration testing, access-control tests, and infrastructure assessment remain necessary. They should be supplemented rather than replaced by tests that exercise probabilistic behavior and data influence.
- Map the system. Draw data flows from user input and external content through preprocessing, retrieval, model serving, memory, tools, and final outputs. Mark trust boundaries and privileged actions.
- Inventory versions and authorities. Record application releases, model and dataset versions, embeddings or indexes, prompts and policies, serving runtimes, and every identity that can change them.
- Define security requirements. State what information must never be disclosed, which actions require authorization or approval, what output formats are allowed, and how the system should fail when confidence or dependencies are insufficient.
- Run adversarial behavior tests. Include direct and indirect prompt injection, malicious files and retrieved pages, attempts to override system instructions, extraction queries, membership-inference probes, and unsafe tool requests.
- Test data and model integrity. Validate ingestion, labels, provenance, access controls, update signatures or equivalent checks, and separation between training, evaluation, and production data.
- Test the surrounding application. Exercise authentication, authorization, tenant boundaries, rate limits, secrets, logging, error handling, network controls, and dependency vulnerabilities using established AppSec methods.
- Verify monitoring and response. Generate representative alerts, confirm that logs contain enough context without exposing unnecessary sensitive content, and rehearse rollback, throttling, tool disablement, and human escalation.
- Repeat after change. Re-test when models, prompts, retrieval sources, tools, providers, data pipelines, or relevant libraries change. AI behavior can shift even when the API contract appears unchanged.
Testing should produce evidence tied to a release or model version. A checklist can show that a control was considered; it cannot by itself prove that an AI system is secure. NIST notes that existing frameworks and guidance do not comprehensively address certain machine-learning attacks and AI-system attack surfaces, so teams must interpret controls in the context of their architecture.
How to use the main guidance together
NIST AI Risk Management Framework
NIST’s AI Risk Management Framework organizes work through four functions: Govern, Map, Measure, and Manage. Governance is cross-cutting: it establishes accountability, policies, roles, risk tolerance, and documentation. Mapping identifies context, affected parties, intended use, limitations, and potential impacts. Measuring applies evaluation, testing, and monitoring. Managing prioritizes responses, allocates resources, and tracks decisions across the lifecycle.
Use the framework to structure risk management, not as a product-security checklist. NIST describes the process as continuous, so the assessment should be revisited as data, models, users, providers, and operating conditions change. The framework is voluntary guidance rather than a certification that a deployment is safe.
NIST SP 800-218A
NIST SP 800-218A is an AI-focused community profile that supplements the Secure Software Development Framework in SP 800-218. It adds AI-specific practices, tasks, recommendations, and considerations across the software development lifecycle. NIST positions it for AI model producers, AI system producers, and acquirers.
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Use SP 800-218 for the conventional secure-development baseline and the 800-218A profile to add work for data, models, evaluation, provenance, and AI-specific change risks. This pairing is more useful than treating the AI profile as a replacement for general software-development controls.
OWASP AISVS 1.0
OWASP describes the AI Security Verification Standard (AISVS) as a testable set of AI-security requirements intended to accompany general application, infrastructure, and supply-chain verification. Its 1.0 page, reported as released in June 2026, lists 191 requirements across 12 chapters. The page’s chapter areas include training-data integrity, input validation, model lifecycle, infrastructure, access control, model supply chain, output controls, memory and vector databases, agent orchestration, MCP, adversarial robustness, and monitoring.
Use AISVS to turn architecture and policy decisions into verification items, while using the OWASP Application Security Verification Standard and relevant infrastructure or supply-chain standards for the non-AI foundation. Confirm the current AISVS version before adopting a gate because the standard and AI attack surface are evolving.
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A practical operating model for security teams
Assign ownership across disciplines
AI security is shared work. Product and risk owners define intended use and unacceptable outcomes. Data owners document provenance, retention, and access. ML or platform engineers control training, evaluation, serving, and model changes. Application-security engineers review code, identities, APIs, and infrastructure. Privacy, legal, compliance, and procurement teams may be needed for sensitive data and suppliers. A named incident lead coordinates response when model behavior and infrastructure compromise overlap.
Make architecture-dependent controls explicit
During design review, answer these questions:
- Is the model trained, fine-tuned, retrieved from, or merely called as an external service?
- What data can influence a response, and which sources are trusted, user-supplied, or external?
- Does the system retain conversation memory, embeddings, feedback, or other state?
- Can the model call tools, and which actions can change data or affect people?
- Which decisions are automated, which require human approval, and how is a refusal or outage handled?
- Who can change the model, prompt, policy, retrieval index, tool permissions, or provider?
The answers determine which controls and tests belong in scope. They also prevent teams from claiming that every AI threat applies equally to every deployment.
Preserve evidence that supports decisions
Keep model and dataset inventories, supplier assessments, threat models, evaluation cases and results, approval records, change history, monitoring definitions, incident records, and retirement confirmations. Link each requirement to an owner and a verification method. This evidence makes it possible to investigate an unexpected output and to show what configuration was active at the time.
Common mistakes and how to correct them
Replacing AppSec with an AI checklist
Why it fails: a model can be protected from prompt injection while the API still exposes another tenant’s data or the build pipeline ships a vulnerable dependency.
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Correction: require conventional code, infrastructure, identity, and supply-chain verification in parallel with AI-specific checks.
Letting the model enforce authorization
Why it fails: model instructions and output are influenceable and probabilistic; they are not a reliable policy engine.
Correction: enforce permissions in deterministic application services, pass only authorized context, and require explicit approval for sensitive actions.
Testing only the happy path
Why it fails: ordinary examples do not reveal indirect prompt injection, poisoned data, extraction behavior, or unsafe tool use.
Correction: maintain adversarial and out-of-distribution cases, test external content and tool results, and repeat them after model or data changes.
Ignoring non-code changes
Why it fails: a new retrieval source, system prompt, model provider, or index can change behavior without a conventional application release.
Correction: put prompts, policies, datasets, indexes, model artifacts, and provider changes under versioning, approval, evaluation, and rollback controls.
Assuming a framework guarantees security
Why it fails: guidance cannot anticipate every architecture or attack, and AI risks continue to change.
Correction: use NIST AI RMF for continuous risk management, SP 800-218A for AI-aware secure development, and AISVS for testable requirements, then adapt them to actual data flows and capabilities.
A concise decision rule
If an AI feature only adds a model call to an otherwise conventional application, begin with the full existing AppSec baseline and add controls for model-provider risk, prompt and output handling, sensitive-data exposure, and behavior testing. If it also uses retrieval or memory, add source integrity, index access, deletion, and leakage tests. If it can call tools or act autonomously, add a separate authorization and orchestration review with least-privileged identities, approval gates, and detailed action logs. If the organization trains or fine-tunes models, extend the lifecycle further to data provenance, poisoning resistance, reproducibility, evaluation separation, and model-artifact protection.
That is what changes: not the removal of application security, but a larger security boundary that follows the data, model, behavior, and capabilities of the AI system through acquisition, development, operation, change, and retirement.
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