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AI changes cybersecurity in two directions: it can help defenders analyze activity and prioritize response, while also creating new systems and components that must be protected and giving attackers new techniques to consider. The practical answer is not to treat AI as inherently safe or dangerous. Assess each use against its security and privacy benefits, threat model the complete system, and keep accountable people and rollback options in place—especially where failures could affect essential services or physical safety.
How is AI changing cybersecurity?
AI is changing both the work defenders do and the systems they have to defend. Security teams may use AI-supported analysis to sort large volumes of information, surface anomalies, prioritize investigations, or support operational decisions. At the same time, an AI-enabled application brings models, data, APIs, connected tools, and dependencies into the environment; each can create exposure if it is not secured and monitored.
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AI also affects the threat landscape. Organizations need to account for AI-enabled offensive techniques without neglecting ordinary cyber hygiene and resilience. That does not mean every attacker will use AI, or that AI automatically makes an attack successful. It means security planning should consider how AI changes a particular system’s capabilities and risks, rather than assuming existing controls alone are sufficient.
The balance is captured by NSA Cybersecurity Director Dave Luber: “AI brings unprecedented opportunity, but also can present opportunities for malicious activity.” The useful question for an organization is therefore not whether AI is good or bad for cybersecurity in general, but whether a specific use produces a net benefit after its risks and safeguards are considered.
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What cybersecurity benefits can AI provide?
AI may augment defensive capabilities by helping teams analyze information at scale and support anomaly triage, prioritization, and security-operations decisions. It can also support privacy assistants and broader privacy management. These are opportunities, not guarantees: an AI feature should be evaluated for its results and its effect on the data it processes.
Analysis and prioritization
Where a security operation has more signals or cases than people can review immediately, analytic assistance may help staff decide what to examine first. Its value depends on whether it improves the organization’s actual outcomes—not on the fact that a model produced an alert, summary, or recommendation. Establish a baseline before deployment and measure matters such as false positives, missed detections, and response time afterward.
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AI’s ability to analyze information may support privacy-related assistance, but that same analytical scope can increase risks of re-identification, tracking, or surveillance. A privacy use case must therefore be assessed for both its intended benefit and the additional inferences or exposure it could create. Keep privacy impact and data handling within the evaluation rather than treating them as separate from cybersecurity.
Operational technology and essential services
In operational technology (OT) and critical infrastructure, AI may improve efficiency, productivity, decision-making, or customer experiences. Those potential gains must be weighed against safety and security risks. A deployment that can affect essential services or physical processes needs a stronger justification and safeguards proportionate to the consequences of failure than a low-impact internal assistant.
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What new cybersecurity risks does AI create?
AI creates risks in two related ways: an AI system can itself be attacked, and AI can change the capabilities available to an attacker. The first calls for securing the system and its supply chain; the second calls for updating threat assumptions while retaining basic cyber defenses.
Attacks on AI systems and their components
Protect more than the model file. Threats can involve the data used for training or retrieval, model artifacts, identities, APIs, connected tools, logs, and dependencies. A compromise can affect confidentiality, integrity, or availability: sensitive information may be exposed, outputs or data may be manipulated, or a service may become unavailable. Vulnerability management and incident-response arrangements should cover AI-related assets and services as part of the wider system.
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Threat models for AI systems should consider attack types that may not be captured by a conventional application review alone:
- Data poisoning: malicious or manipulated data can undermine the intended behavior of a model.
- Indirect prompt injection: instructions embedded in material an AI system processes may influence its behavior, including when it interacts with other tools.
- Model inversion and membership inference: attacks can attempt to infer information about the data associated with a model.
- Model obfuscation: techniques that obscure or manipulate a model may affect how it is assessed or used.
- Supply-chain compromise: dependencies, model sources, or data sources can introduce risk before a system is deployed.
The UK Code of Practice for the Cyber Security of AI recommends addressing these classes in threat modeling and revisiting the assessment when settings or configurations change. Singapore’s Cyber Security Agency also identifies supply-chain attacks and adversarial machine learning as concerns for AI security.
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Privacy, misuse, and overbroad functionality
A system’s analytic power can create exposure beyond direct attacks: re-identification, tracking, or surveillance may arise from how information is combined or used. Threat modeling should also examine data leakage and misuse of excessive model functionality. The risk is shaped by what data the system can access, what actions it can take, and which people or services are able to invoke those capabilities.
Safety and critical infrastructure
In essential services, AI deployment can add exposure to critical failures, physical attacks, and cyberattacks. The consequences may extend beyond data or service disruption to public safety. DHS frames AI risk mitigation in this setting as a national-security and public-safety imperative. That makes safety assurance, accountable governance, and a safe response to system failure central design questions, not optional finishing touches.
How should an organization use AI safely?
Use a lifecycle process: govern the use, assess its threats, secure the whole system, test it, and keep people accountable for consequential decisions. NIST’s AI Risk Management Framework (AI RMF) 1.0 provides a voluntary, use-case-agnostic structure for design, development, deployment, use, and evaluation. NIST released it on January 26, 2023.
- Define the use and its consequences. Describe what the AI system is meant to do, what data and services it depends on, and what could happen if it is wrong, unavailable, manipulated, or used outside its intended purpose. Identify whether it affects essential services, privacy, or safety.
- Threat-model before deployment. Examine prompt injection, poisoning, model inversion, membership inference, model obfuscation, supply-chain compromise, data leakage, and misuse of excessive functionality. Include the system’s connected tools, data flows, identities, and dependencies, rather than evaluating only the model.
- Secure the complete system. Apply confidentiality, integrity, and availability controls to the model and related data and services. Include vulnerability mitigation, access and identity controls, and incident-response processes. Protect training and retrieval data, artifacts, APIs, tools, logs, and dependencies.
- Build security into procurement and engineering. Make security requirements part of acquisition, development, testing, deployment, monitoring, and retirement. Seek supplier assurance and maintain provenance for models and data so the organization can understand what it is using and where it came from.
- Test and monitor continuously. Use validation, adversarial testing, red teaming, and monitoring appropriate to the system’s criticality. Keep controls current as models, threats, or operating conditions evolve, and provide a coordinated vulnerability-disclosure route appropriate to the system.
- Set human accountability and safe failure behavior. For high-impact or safety-critical decisions, define who reviews an output, when escalation is required, who can override it, and how decisions can be audited. Specify fail-safe behavior before deployment rather than relying on an operator to improvise during an incident.
- Reassess after changes. Repeat threat modeling when configurations change or a connected tool is added or changed. A system’s risk can shift when its permissions, data sources, or operating context shift, even if the model itself has not changed.
- Measure the result against a baseline. Compare false positives, missed detections, response time, privacy exposure, model drift, and incident rates before and after adoption. Stop or roll back a use that does not deliver a net security benefit.
What should change for AI in operational technology?
OT deployments need safety and security controls designed around the consequences of affecting physical operations. If AI can interact with a control environment, consider separating it from control networks, limiting data flows, and ensuring that an AI failure cannot directly produce an unsafe physical outcome.
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Before deployment, determine what the AI can observe and what it can change. Define a human review and escalation path for decisions that could affect safety or service continuity, and make override and fail-safe behavior explicit. The expected benefit should outweigh the added safety and security risk; efficiency or productivity alone is not a sufficient assessment if a failure could cause critical harm.
For a lower-risk advisory role, the system’s output can be evaluated as decision support. If the system can take action in an operational environment, the organization needs to establish how that authority is constrained, monitored, and safely withdrawn. The degree of testing and oversight should match system criticality.
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Which AI security framework should an organization follow?
NIST AI RMF is a voluntary starting structure for managing AI risks across a lifecycle, not a substitute for cybersecurity, privacy, or system-specific safety controls. It can be paired with other NIST resources, while government guidance on secure AI deployment and AI-specific threats can help shape implementation.
| Resource | What the evidence establishes | How to use it |
|---|---|---|
| NIST AI RMF 1.0 | Voluntary and use-case-agnostic; released January 26, 2023; covers design, development, deployment, use, and evaluation. | Use as an organizing structure for AI risk management across the lifecycle. |
| NIST Cybersecurity Framework | Identified as a framework that can be paired with the AI RMF; further details are not stated here. | Pair it with AI risk management where the organization needs a cybersecurity framework alongside the AI lifecycle structure. |
| NIST Secure Software Development Framework | Identified as a complementary NIST resource; further details are not stated here. | Consider it alongside AI risk management for secure development practices. |
| NIST Privacy Framework | Identified as a complementary NIST resource; further details are not stated here. | Include it where AI use raises privacy-management concerns. |
| Joint secure-deployment guidance | Released April 15, 2024, by CISA, NSA, FBI, ASD ACSC, CCCS, NCSC-NZ, and NCSC-UK. | Use its secure-deployment focus to inform protections for AI systems and related services. |
| UK Code of Practice for the Cyber Security of AI | Addresses AI attack classes and advises updating threat models when settings or configurations change. | Use its threat-modeling focus to check AI-specific attacks and reassess after changes. |
| Singapore CSA guidance | Highlights supply-chain attacks, adversarial machine learning, and secure-by-design and secure-by-default practices through the lifecycle. | Use it to consider supply-chain risks and lifecycle security engineering. |
Choose and combine resources based on the use case rather than selecting a framework by name alone. Compare how an approach covers lifecycle stages, threat classes, system criticality, accountability, human oversight, testing and monitoring, supplier and data-chain controls, and fail-safe or rollback capability. A framework label does not by itself demonstrate that a particular deployment is safe.
How can an organization tell whether AI improved security?
Set the baseline before introducing the system, then evaluate the same operational outcomes afterward. At minimum, compare false positives, missed detections, response time, privacy exposure, model drift, and incident rates. The relevant measures depend on the system’s purpose: an alert-triage assistant and an AI feature used near a safety-critical process should not be judged by identical consequences.
Interpret the measurements together. A faster response is not a net gain if important detections are missed or privacy exposure rises beyond what the organization accepts. Define in advance who reviews results, how often the system is evaluated, what level of performance or risk triggers intervention, and how to stop or roll back the use. This makes an AI deployment an accountable security decision rather than an open-ended experiment.
Frequently Asked Questions
Does AI replace cybersecurity analysts?
The available guidance describes AI as an opportunity to augment defensive capabilities, not as a replacement for accountable security staff. Organizations still need people who can review consequential decisions, escalate concerns, override outputs, and audit how a system was used.
Is generative AI itself a cybersecurity framework?
No. Generative AI is a class of technology, while a framework such as NIST AI RMF is a structure for managing risk. An organization can use AI applications and separately apply risk-management and cybersecurity practices to them.
What does secure by default mean for an AI system?
It means security should be built into the system’s normal configuration and lifecycle rather than left for users to add after deployment. Singapore CSA guidance specifically emphasizes secure-by-design and secure-by-default practices throughout the AI lifecycle.
Can AI make cybersecurity worse even when no attacker compromises the model?
Yes. For example, broader analytical capability can increase re-identification, tracking, or surveillance risks. A system can also create privacy or safety concerns through its legitimate use, so assessment should cover intended operation as well as attack scenarios.
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