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The practical answer is therefore “both.” Use conventional controls as the foundation, then add AI-specific governance, testing, monitoring and response wherever a model or AI-enabled workflow introduces new ways to manipulate behavior, expose data or lose control of the system.
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What traditional cybersecurity protects
Traditional cybersecurity is concerned with preventing damage to, protecting and restoring computers, electronic communications systems and services, and electronic information. NIST frames its objectives as availability, integrity, authentication, confidentiality and non-repudiation. In everyday security programs, those objectives are applied to endpoints, servers, networks, cloud infrastructure, identities, applications, databases, files and communications.
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- Identity and access management: accounts, authentication, authorization, privileged-access controls and separation of duties.
- Secure configuration and patching: hardened operating systems, applications, containers, cloud resources and network devices, with vulnerabilities remediated according to risk.
- Encryption and key management: protection for data in transit and at rest, together with controlled handling of cryptographic keys.
- Network and endpoint defenses: segmentation, firewalls, endpoint protection, secure remote access and malware prevention.
- Monitoring and detection: logging, alerting, threat detection and investigation of suspicious activity.
- Resilience and recovery: tested backups, incident response, disaster recovery and restoration of trustworthy services.
These controls remain necessary when an application contains a machine-learning model. An AI service still runs on operating systems, containers, APIs, networks, storage, identities and third-party components. A stolen administrator credential or an unpatched library can compromise the service before anyone attempts an AI-specific attack.
What AI security adds
AI security applies the same confidentiality, integrity and availability goals to the AI stack, then addresses assets and failure modes that ordinary software controls do not fully describe. The protected object is not only the server running the application; it is also the model, the data that shaped it, the context supplied at runtime and the behavior produced in response.
Models and model endpoints
Model weights, architectures, adapters, prompts embedded in system instructions and inference endpoints can represent valuable intellectual property. Controls must prevent unauthorized access, tampering, theft, extraction and unsafe deployment. Endpoint permissions should distinguish who may invoke a model, which functions a caller may use, how much data may be submitted and whether the caller can obtain raw scores, embeddings or other details that make replication easier.
Training, fine-tuning, retrieval and evaluation data
AI systems can depend on several data stores rather than one application database. Training and fine-tuning sets influence the model’s learned behavior. Retrieval indexes and document stores shape responses at runtime. Evaluation sets determine whether a release is considered acceptable. Each needs confidentiality and integrity controls, provenance, versioning and an audit trail. Poisoned or unrepresentative data can change behavior without altering application code, so data review and monitoring belong in the security process.
Prompts, context, tools and outputs
Inputs to an AI system are often interpreted rather than executed as fixed program instructions. An attacker may manipulate a user prompt, a retrieved document or other context to override intended instructions, induce disclosure or cause an unsafe tool call. Outputs can contain confidential information, malicious instructions, fabricated claims or content that triggers an action in a connected system. AI security therefore treats prompts, context, tool permissions and outputs as security-relevant data flows.
AI intellectual property
Weights, proprietary architectures, private datasets, labeling guidance, evaluation material and carefully designed system prompts may be commercially sensitive. Ordinary data-loss prevention and access controls help, but AI-specific monitoring is needed to detect repeated probing, unusual query volumes, systematic attempts to reconstruct a model or exfiltration through an apparently legitimate endpoint.
Lifecycle behavior
Traditional code generally behaves according to explicitly written logic. An AI system can change as its data, retrieval corpus, model version, context window, tools or users change. Security must therefore cover design, development, deployment, use and evaluation, with controls for drift, robustness, unsafe outputs and release-to-release changes.
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AI security versus traditional cybersecurity at a glance
| Comparison axis | Traditional cybersecurity | AI security |
|---|---|---|
| Protected assets | Devices, servers, networks, identities, applications, communications and stored information. | All traditional assets, plus models, weights, architectures, training and retrieval data, prompts, context, tools, outputs and evaluation material. |
| Primary threats | Unauthorized access, malware, exploitation, credential theft, data theft, tampering and service disruption. | All traditional threats, plus data poisoning, adversarial examples, model extraction, membership inference, prompt or input manipulation, unsafe tool use and endpoint exfiltration. |
| Testing target | Configuration, vulnerabilities, access paths, code, infrastructure and recovery procedures. | Those targets plus model robustness, data quality and provenance, leakage, behavior under adversarial inputs, drift, output safety and changes caused by updates or context. |
| Runtime assumptions | Rules and program paths are comparatively deterministic and can be checked against known expected behavior. | Outputs are probabilistic and context-dependent; the same model can produce different results as data, prompts, tools or versions change. |
| Governance | Confidentiality, integrity, availability, authentication, accountability and recovery. | Those objectives plus trustworthy, explainable, interpretable, privacy-enhanced and fair behavior, with harmful bias managed where relevant. |
| Response | Contain accounts, hosts, malware, vulnerabilities or data flows; eradicate, recover and learn. | Those actions plus quarantine poisoned data, revoke or rotate models and prompts, disable tools, roll back a model or retrieval index, and re-evaluate behavior before restoration. |
Where the two disciplines overlap
NIST notes that some cybersecurity risks related to AI are common or identical to risks across software development and deployment. The underlying hardware and software still require confidentiality, integrity and availability protections, as do training and output data. This means an AI security program should extend identity, network, application, data and supply-chain security rather than create a separate island.
For example, a retrieval-augmented assistant may use a web application, an API gateway, an identity provider, object storage, a vector database and a hosted model. Access controls, secure coding, dependency management, network segmentation, secrets management, logging and backup are traditional requirements. AI-specific controls then determine which documents may be retrieved, how untrusted text is labeled, what tools the assistant may call and which outputs may reach a user or downstream system.
Where AI changes the risk model
AI systems introduce uncertainty that is not captured by a conventional vulnerability scan. NIST’s AI Risk Management Framework (AI RMF) identifies risks that are new or increased for AI, including data that may not represent the intended context, missing or uncertain ground truth and difficulty deciding what to test because AI systems are not subject to exactly the same controls as traditional code. Data can change over time and unexpectedly affect functionality and trustworthiness.
Data poisoning
Poisoning changes training, fine-tuning, retrieval or evaluation data so that a model learns an attacker-selected behavior or appears safer than it is. A conventional database integrity check may show that files were not altered after ingestion while missing the more important question: whether the content was appropriate, representative and authorized for that purpose. Provenance, review, controlled ingestion, dataset versioning and regression tests are needed.
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Adversarial examples and evasion
An adversarial example is an input designed to cause an incorrect classification or decision while appearing ordinary to a person or to a conventional validation rule. Evasion can also involve carefully constructed prompts, documents or images that bypass a model’s intended safeguards. Testing must include hostile and borderline inputs, not only clean test cases.
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Model extraction
Repeated queries can reveal enough information to approximate a proprietary model or infer its decision behavior. Rate limits, authentication, query monitoring, output minimization and contractual controls reduce exposure. Where detailed scores, logits or embeddings are not required by the application, withholding them limits what an attacker can learn.
Membership inference and sensitive-data leakage
Membership inference attempts to determine whether a particular record appeared in training data. Outputs can also reveal memorized text or sensitive information through direct requests, indirect prompting or retrieval. Data minimization, privacy-preserving training and evaluation for leakage should accompany ordinary encryption and access controls.
Availability attacks
AI services can be exhausted through oversized inputs, expensive tool chains, repeated generation requests or retrieval patterns that consume disproportionate compute. Conventional denial-of-service defenses remain useful, but AI services also need token, cost, concurrency and tool-use limits, along with graceful degradation when a model is unavailable.
How testing and monitoring differ
Traditional security testing asks whether a known weakness, misconfiguration or unauthorized path exists. AI testing must also ask how the system behaves across a range of inputs and contexts, whether its data remains appropriate and whether an update changes security-relevant behavior.
Before deployment
- Map the model, data sources, interfaces, identities, tools and downstream actions.
- Verify dataset provenance, licensing, access rights, retention and approved use.
- Test adversarial inputs, prompt manipulation, sensitive-data leakage, unsafe tool calls and attempts to extract model behavior.
- Measure performance and failure modes against representative and deliberately difficult evaluation sets.
- Confirm that logging captures security-relevant prompts, retrieval events, tool calls, model versions and outcomes without creating a new store of unnecessary personal data.
- Set release criteria, rollback conditions and named owners for security, privacy and operational decisions.
During operation
- Monitor authentication, query volume, token consumption, unusual probing, retrieval anomalies and tool-use patterns.
- Track drift in input data, retrieval content and output behavior; investigate changes rather than assuming they are ordinary traffic variation.
- Re-test after model, prompt, policy, tool, dependency, index or dataset changes.
- Review sampled outputs for leakage, unsafe instructions, policy bypasses and high-impact errors, using privacy-conscious sampling.
After an incident
Containment may require more than blocking an IP address. Depending on the cause, responders may disable a tool, revoke an exposed key, remove poisoned documents, roll back a model or index, rotate system prompts, restrict an endpoint or suspend automated actions. Restoration should include a targeted evaluation proving that the triggering behavior is no longer present.
Governance: using the AI RMF with existing frameworks
NIST describes the AI RMF as voluntary guidance for developers, users and evaluators managing risks to individuals, organizations, society and the environment. Its trustworthiness characteristics include validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness with harmful bias managed.
The AI RMF is complementary, not a substitute for established security and privacy practices. NIST specifically identifies the Cybersecurity Framework, Privacy Framework, Risk Management Framework and Secure Software Development Framework as resources that can be used together. A practical governance model assigns each framework the work it handles best:
| Need | Useful foundation | AI-specific extension |
|---|---|---|
| Enterprise security outcomes and incident readiness | Cybersecurity Framework and security policies. | AI asset inventory, model-threat scenarios, model and data rollback plans. |
| Privacy obligations and data handling | Privacy Framework, data-governance rules and legal requirements. | Training-data consent and provenance, membership-inference testing, prompt and output minimization. |
| System risk decisions | Risk Management Framework and authorization process. | Document intended use, unacceptable use, human oversight, evaluation evidence and residual model risk. |
| Secure engineering | Secure Software Development Framework, code review and supply-chain controls. | Dataset and model lineage, signed artifacts, adversarial evaluation and secure model-serving configuration. |
NIST’s secure-and-resilient guidance describes an AI system as secure when it can maintain confidentiality, integrity and availability through mechanisms that prevent unauthorized access and use. Resilience is related but broader: security includes resilience while also covering protocols to avoid, protect against, respond to and recover from attacks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can existing cybersecurity controls secure an AI application?
They can secure the foundation, but they cannot by themselves establish that the model’s behavior is trustworthy. A firewall can restrict access to an inference endpoint; it cannot determine whether a retrieved document contains an instruction that hijacks the assistant. Encryption can protect a dataset at rest; it cannot reveal that the dataset is poisoned or unrepresentative. An identity system can authenticate a user; it cannot decide whether that user’s request should be allowed to trigger a payment, delete records or expose another person’s information.
The right approach is layered:
- Build an ordinary security baseline. Apply least privilege, strong authentication, secure configuration, patching, encryption, secrets management, segmentation, logging, backup and incident response to every component.
- Inventory AI assets and data flows. Record models, versions, weights, prompts, datasets, retrieval indexes, providers, tools, operators and downstream systems.
- Define intended and prohibited behavior. State what the model may answer, what data it may use, which actions require human approval and which tools are never available to it.
- Secure data ingestion. Authenticate sources, review provenance, scan content, version datasets and indexes, separate trusted instructions from untrusted retrieved text and retain rollback copies.
- Constrain inference. Enforce authorization, rate and cost limits, input-size limits, output filtering where appropriate and narrowly scoped tool permissions.
- Evaluate before release. Use representative, edge-case and adversarial tests for leakage, manipulation, extraction, unsafe actions, robustness and drift sensitivity.
- Monitor continuously. Correlate ordinary security telemetry with model, retrieval, prompt, output and tool-use events; alert on probing, anomalies and policy violations.
- Prepare AI-specific response playbooks. Define when to disable a tool, quarantine data, revoke an endpoint, roll back a model or index, notify affected parties and repeat evaluation before reopening service.
Practical control mapping
| Control question | Traditional answer | Additional AI answer |
|---|---|---|
| Who can use the system? | Identity, authentication, authorization and privileged-access review. | Which model capabilities, data sources, tools and output detail may each role use? |
| What may be trusted? | Signed code, approved dependencies and controlled configuration. | Verified model artifacts, dataset and document provenance, trusted system instructions and approved evaluation results. |
| How is leakage detected? | Data-loss prevention, access logs and network monitoring. | Those controls plus tests and detection for memorization, membership inference, prompt-based disclosure and model extraction. |
| How is misuse contained? | Disable accounts, isolate hosts, block traffic and restore backups. | Also disable tools, restrict prompts, quarantine poisoned data, revoke model endpoints and roll back model or retrieval versions. |
| What proves a release is safe? | Code review, vulnerability testing, configuration checks and operational approval. | Those checks plus behavioral evaluation across adversarial, representative and high-impact scenarios. |
Common mistakes and how to correct them
Treating the model as just another software package
A model artifact needs the same supply-chain discipline as code, but its behavior also depends on data and context. Keep lineage for model, dataset, prompt and evaluation versions, and make the complete combination reproducible enough to investigate.
Relying on a content filter as the whole defense
Output filtering can catch some unsafe responses, but it does not prevent stolen credentials, poisoned retrieval data, unauthorized tools or model extraction. Combine filters with authorization, isolation, monitoring and human approval for consequential actions.
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Giving an assistant broad tool permissions
Connectors turn a text-generation system into an actor. Use least privilege, separate read and write capabilities, constrain parameters, require confirmation for high-impact operations and log every invocation.
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Testing only average-case accuracy
High aggregate accuracy can coexist with a severe failure on a rare but important input. Include edge cases, adversarial examples, sensitive-data requests, ambiguous instructions, multilingual or domain-specific inputs where relevant, and tests for changes after updates.
Ignoring the retrieval corpus
A secure model can still produce an unsafe answer when an index contains malicious or obsolete material. Control who can publish documents, preserve source and version metadata, scan updates and make removal and rollback fast.
Assuming a vendor’s security statement covers your deployment
Provider controls may protect the hosted infrastructure, but your organization remains responsible for identity, prompts, data selection, integrations, user permissions, retention and downstream actions. Document the shared-responsibility boundary and verify it contractually and technically.
When an AI security program is warranted
Every AI deployment should inherit the ordinary security baseline. Additional AI-specific work becomes especially important when a system handles confidential or regulated information, learns from internal data, retrieves changing content, makes or recommends high-impact decisions, connects to write-capable tools, serves many untrusted users or contains valuable proprietary weights and evaluation material.
For a low-risk internal experiment, the minimum may be isolated data, no production credentials, restricted users, logging and a documented deletion plan. For a customer-facing or action-taking system, expect formal threat modeling, dataset governance, adversarial evaluation, human oversight, continuous monitoring, tested rollback and an incident process that names both security and model owners.
The bottom line
Traditional cybersecurity protects the infrastructure and information that make an AI service possible. AI security includes that foundation and extends it to the model, data, prompts, outputs, intellectual property and changing behavior. NIST’s guidance is explicit that shared cybersecurity risks remain, while AI-specific risks must also be governed, mapped, measured and managed. The defensible design is a single layered program: conventional controls for the environment, AI-aware controls for the model lifecycle and explicit governance for what the system is allowed to do.
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