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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteProtect your organization by tightening the controls attackers already target—identity, email, exposed systems, sensitive data, and recovery—while securing AI services and integrations as part of the same environment. AI can help attackers move faster, scale familiar techniques, and make impersonation more convincing; it does not make every attack autonomous or guarantee more successful breaches. Treat it as a reason to improve verification, visibility, and response readiness.
What AI changes about cyber risk
AI can assist with reconnaissance, vulnerability discovery, phishing, malware obfuscation, and coordinating steps across an attack. NIST’s initial preliminary draft of IR 8596, the Cybersecurity Framework Profile for Artificial Intelligence, dated December 2025, describes potential increases in speed and scale, ease of deployment, and dynamic optimization. These are threat patterns and possibilities described in draft guidance, not proof that every capability is widespread or operating autonomously in real incidents.
People remain an important target. Generated messages can be personalized, malicious websites can look convincing, and manipulated audio or video can be used to impersonate a trusted person. The practical response is not to ask employees to spot every fake; it is to make sensitive actions require independent verification and to ensure one convincing message or call cannot bypass core controls.
Attacks assisted by AI versus attacks on AI
These are related but distinct risks. Attackers can use AI to improve familiar techniques against conventional systems. Separately, they can target an organization’s AI systems, their data, and connected tools. NIST AI 100-2 E2025, published March 24, 2025, organizes adversarial machine-learning threats into four broad categories: evasion, poisoning, privacy, and misuse. This is a taxonomy, not an estimate of attack frequency.
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| Risk area | What may be targeted | Practical defensive focus |
|---|---|---|
| AI-assisted attacks | People, accounts, email, endpoints, exposed services, and data | Verify sensitive requests independently; strengthen authentication, email and endpoint defenses; patch and monitor exposed systems. |
| Attacks on AI systems | Prompts, retrieved content, training or operational data, models, integrations, and service availability | Limit data and permissions; test for injection and leakage; protect dependencies and provide a way to disable integrations. |
Start by finding what is exposed
Controls cannot protect assets the organization does not know it has. Build or refresh an inventory spanning conventional IT and AI use, then trace the paths between identities, data, services, and third parties.
Inventory systems, identities, and dependencies
- Record internet-facing services, endpoints, software, privileged and workforce identities, and third-party dependencies.
- Identify critical and sensitive data, where it is stored, who can access it, and where it flows—including into external AI services.
- List approved AI tools, model APIs, plugins, retrieval sources, connected applications, and the actions each integration can take.
- Assign an owner and review process to inventories so changes made by business units do not create invisible exposure.
NIST’s AI Risk Management Framework program guidance calls for understanding data dependencies and reevaluating data-asset inventories as AI use expands. An inventory should therefore cover not only which model or service is in use, but also the sources it can retrieve from and the systems it can affect.
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Make identity and sensitive requests harder to spoof
Use multifactor authentication (MFA), favor phishing-resistant methods for privileged and other high-risk accounts, and apply least privilege so a compromised identity has limited reach. CISA’s surfaced excerpt from Risk in Focus: Generative AI in Elections recommends MFA, especially phishing-resistant MFA; that recommendation supports prioritizing stronger authentication, not a claim that one method suits every deployment.
Build an independent verification path
Require a second, trusted channel for payment or bank-detail changes, credential requests, sensitive data transfers, and privileged actions. For example, confirm a request using a known number or an approved internal workflow—not a number, link, or reply address supplied in the request itself. Establish this rule for voice and video requests as well as email and chat.
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Choose authentication that fits your environment
A FIDO2 hardware security key is one possible phishing-resistant MFA implementation. Before deploying any key, confirm compatibility with your identity provider, accounts, devices, enrollment process, and recovery procedures; not every key works with every service. Define how staff report a lost key and how administrators restore access without weakening account security. CISA’s excerpt recommends phishing-resistant MFA, but the underlying PDF was not fully accessible for broader evaluation, so no more detailed claim about its guidance is warranted.
Update workforce practice
Train employees to expect plausible, personalized text as well as voice and video impersonation. Focus practice on the action to take—pause, use the independent verification route, and report suspicious requests—rather than asking people to judge whether audio or video “looks real.”
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Strengthen email, endpoints, and vulnerability management
Keep layered email protections, endpoint detection, centralized logging, and patch processes in place. Maintain visibility into exposed assets so teams can prioritize vulnerabilities according to exposure and organizational risk. The point is not that existing products universally fail; it is that a strategy dependent only on static signatures or manual discovery may be poorly matched to obfuscated malware and faster reconnaissance described in NIST’s December 2025 preliminary AI profile.
- Ensure security alerts from email, identity, endpoints, and key services reach teams responsible for investigation.
- Prioritize patching and mitigation for internet-facing and business-critical assets, with a way to track exceptions to closure.
- Review logging coverage for authentication, administrative changes, data access, and AI integrations so an investigation can reconstruct relevant activity.
- Test whether endpoint and email controls surface suspicious behavior even when a message or file does not match a known signature.
Govern AI tools, data, and integrations
NIST’s Generative Artificial Intelligence Profile (AI 600-1) describes both the possibility that generative AI lowers barriers to offensive capability and the additional attack surface created by AI systems themselves. Prompt injection can arrive directly or indirectly through retrieved material; data poisoning can undermine the integrity of data or model behavior. Protect confidentiality, integrity, and availability across inputs, processing, training, deployment environments, model components, and connected tools.
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Set boundaries before connecting a service
- Require an approval and inventory process for AI services, APIs, plugins, and integrations.
- Set rules for what sensitive data may be entered or sent to external services, and limit access to only the data needed for a task.
- Constrain tool permissions, separate duties where appropriate, and require human approval for high-impact or irreversible actions.
- Track vendor, model, and dependency changes that could affect data handling, behavior, or access.
Test failure modes and retain a shutdown path
Test direct and indirect prompt injection, manipulated retrieval sources, sensitive-data leakage, model and dependency integrity, and service availability. Evaluate what a connected tool can do if its instructions or inputs are manipulated. Keep a practical way to revoke credentials or disable an integration, and rehearse using it. NIST’s AI Risk Management Framework program guidance emphasizes securing AI components and minimizing data leakage; NIST AI 100-2 E2025 provides terminology for adversarial machine-learning threats, not a promise that any single mitigation fully prevents them.
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NIST finalized SP 800-61 Revision 3 on April 3, 2025, superseding Revision 2 and aligning incident-response recommendations with CSF 2.0. It treats incident response as integrated with organizational operations and the framework’s six functions. Adapt that approach to the systems and decision-making structure your organization actually has.
Define response decisions in advance
- Name who can revoke credentials, isolate endpoints or services, disable AI integrations, and approve restoration.
- Preserve relevant logs and evidence while responders contain activity; identify who is responsible for each system and data source.
- Prepare communication routes for employees, customers, partners, and leadership that do not depend on a potentially compromised account or channel.
- Confirm that protected backups can be restored and that restoration steps are known to the people who may need to perform them.
Rehearse realistic scenarios
Exercise response to AI-generated phishing, manipulated executive audio or video, a compromised AI integration, and suspicious activity by an agent or connected tool. Include credential revocation, containment, evidence preservation, internal escalation, and recovery—not just the moment an alert is detected. NIST computer scientist Apostol Vassilev said in NIST’s January 4, 2024 article about its adversarial machine-learning report: “We also describe current mitigation strategies reported in the literature, but these available defenses currently lack robust assurances that they fully mitigate the risks. We are encouraging the community to come up with better defenses.” The implication for incident planning is to assume controls reduce risk without treating them as guarantees.
Use defensive AI only with evidence and oversight
AI-assisted security tools may help analysts with detection, response, or recovery, but their fit and maturity vary. NIST’s preliminary December 2025 profile calls for continuous evaluation of defensive capabilities. Before relying on a tool, test it with representative data and measure false positives and missed detections; understand what information it can access and how that data is retained. Keep an accountable human in the loop for consequential actions such as disabling accounts, isolating critical systems, or changing access.
A practical hardening sequence
- Map exposure: inventory identities, internet-facing services, endpoints, critical data, AI services, integrations, and third-party dependencies.
- Protect access and transactions: enable MFA, prioritize phishing-resistant methods for high-risk accounts, enforce least privilege, and require independent verification for sensitive requests.
- Improve visibility and maintenance: connect email, endpoint, identity, and service logging to investigation; keep asset and vulnerability records current; prioritize exposed systems.
- Constrain AI use: approve services, limit sensitive data and tool permissions, test injection and leakage, monitor dependencies, and retain a shutdown path.
- Practice response and recovery: assign decision owners, rehearse containment and credential revocation, preserve evidence, communicate through trusted channels, and test backup restoration.
- Evaluate defensive AI: assess performance on representative conditions, review data access and retention, and require human oversight for consequential actions.
The reviewed sources do not establish an organization-level prevalence statistic or measured attack-success rate for AI-accelerated cyberattacks. Prioritize controls based on your organization’s exposure and risk rather than an unsupported percentage or forecast.
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