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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →The answer is an operating program, not an AI product. Protect an organization by keeping the full cybersecurity baseline in place, then adding explicit controls for the AI systems and data the organization builds, buys, or permits employees to use. Assign an accountable risk owner, inventory internet-facing assets and AI services, restrict unnecessary exposure, patch known exploited vulnerabilities, test phishing and ransomware response, and make reporting and recovery part of executive operations.
“AI-assisted cyberattack” covers three different problems that require different decisions:
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- Attackers may use AI to accelerate familiar attacks such as phishing, credential theft, vulnerability exploitation, or ransomware.
- Attackers may target an organization’s AI systems, models, prompts, training data, integrations, or outputs.
- An AI system may fail because of design, implementation, configuration, or operating weaknesses, even without a conventional attacker using AI.
CISA’s summary of the NIST AI Risk Management Framework uses this broad view of AI risk. Treat it as an extension of enterprise security and risk management, not a replacement for patching, identity controls, backups, monitoring, and incident response.
Start by defining the AI risk you actually need to manage
Before buying controls or writing a policy, list the organization’s critical business functions and the ways AI touches them. A customer-service assistant, a developer coding tool, an internal search system, and a model embedded in a medical or industrial product have different consequences when they fail. The same control cannot be applied at the same strength to every use.
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Separate the three risk classes
| Risk class | What happens | Examples of decisions |
|---|---|---|
| AI used by attackers | A threat actor uses automation or generative systems to make a conventional campaign faster, more adaptive, or more convincing. | Strengthen identity, email, endpoint, vulnerability, and response controls; train staff to verify unusual requests. |
| Attacks against AI systems | An adversary targets a model, prompt, data pipeline, plug-in, API, or connected service. | Control data access, isolate components, authenticate integrations, log activity, and test abuse cases. |
| AI design or implementation failure | The system produces unsafe results or exposes information because of flawed requirements, data, code, configuration, or oversight. | Define intended use, prohibited use, human review, testing, release gates, monitoring, and rollback. |
This distinction prevents two common mistakes: assuming a traditional security program has become irrelevant because an attacker uses AI, or treating every AI failure as a criminal intrusion. Both ordinary cyber hygiene and AI-specific engineering controls are necessary.
Assign ownership before deployment
Name a senior decision owner for security risk and a business owner for each AI use. Security leadership should have authority to delay a deployment, require safeguards, or accept a documented residual risk. The business owner remains responsible for the process that depends on the system; the technical owner is responsible for configuration, access, updates, logging, and supplier coordination.
For each system or service, record its purpose, users, data types, connected applications, provider, deployment location, critical business function, recovery requirement, and approval status. Include unofficial employee use discovered through expense records, browser telemetry, procurement data, or interviews. Do not assume that an externally hosted service is outside your security boundary: your organization still controls the information submitted, the identities that access it, and the business process that relies on its output.
Use a practical AI risk cycle
CISA’s summary of the NIST AI Risk Management Framework describes four activities: govern, map, measure, and manage. Turn them into an operating cycle:
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- Govern: set policies, roles, approval thresholds, legal and regulatory requirements, supplier expectations, and escalation paths.
- Map: document the intended context, affected people, data flows, dependencies, failure modes, and consequences for each use.
- Measure: test security, privacy, reliability, abuse resistance, access control, and output quality against the risks identified.
- Manage: prioritize mitigations, assign due dates, track exceptions, monitor residual risk, and retire or redesign systems that cannot be operated safely.
Apply proportionate oversight. A low-impact drafting assistant may need data-loss controls and clear user guidance; an AI component that can authorize payments, change production systems, or make safety-related decisions needs stronger isolation, human approval, testing, and recovery arrangements.
Keep the six-function cybersecurity baseline
Use CISA’s Cross-Sector Cybersecurity Performance Goals as a readable baseline organized around Govern, Identify, Protect, Detect, Respond, and Recover. CISA’s current goals do not yet explicitly address generative-AI-based cyber threats, so they should be paired with an AI-system review rather than treated as a complete AI framework.
| Function | Organization-wide baseline | AI-specific addition |
|---|---|---|
| Govern | Set risk appetite, ownership, policies, supplier requirements, and executive accountability. | Approve AI use cases, define prohibited data and actions, and require a named owner for every model or service. |
| Identify | Maintain inventories of assets, identities, software, vulnerabilities, suppliers, and critical processes. | Inventory models, prompts, datasets, APIs, plug-ins, training pipelines, and employee-approved or unapproved AI services. |
| Protect | Use strong authentication, least privilege, secure configuration, segmentation, encryption, backups, and workforce training. | Restrict model and data access, isolate tools from high-impact actions, protect secrets in prompts and code, and require human approval for consequential operations. |
| Detect | Collect useful logs, monitor endpoints and networks, and investigate anomalies. | Log prompts, tool calls, data access, model changes, policy overrides, and unusual output or usage patterns while respecting privacy obligations. |
| Respond | Maintain an incident plan, communications tree, evidence process, and decision authority. | Include model abuse, data poisoning, prompt manipulation, unsafe output, provider outages, and compromised integrations in scenarios. |
| Recover | Restore systems and data, validate integrity, communicate status, and improve after incidents. | Keep known-good model configurations, data and policy versions; provide a safe fallback process if an AI service must be disabled. |
Reduce preventable internet exposure
CISA’s Internet Exposure Reduction Guidance (June 4, 2025) recommends a simple sequence. First establish which assets can be reached from the public internet. Then decide whether each exposure is operationally necessary, remove or restrict what is not, and mitigate what must remain exposed.
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Follow the exposure-reduction sequence
- Discover: combine asset records with external scanning, cloud inventories, DNS data, certificate records, firewall rules, and business-owner confirmation. Record the owner and purpose of every public address, portal, API, remote-access service, and administrative interface.
- Decide: classify each exposed service as required, conditionally required, or unnecessary. A service with no current owner should be treated as unnecessary until someone accepts responsibility.
- Remove or restrict: shut down abandoned systems, close unused ports, remove direct administrative access, place management interfaces behind a private access path, and restrict APIs to known identities or networks.
- Mitigate what remains: patch promptly, enforce strong authentication, limit privileges, segment dependencies, apply secure configuration, monitor logs, and maintain a tested recovery path.
AI does not change the basic need to know what is exposed. It can increase the consequences of an overlooked API key, an unprotected model endpoint, or a public development console because those interfaces may provide access to sensitive data or automated actions.
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Patch known exploited vulnerabilities first
The FBI and CISA Androxgh0st advisory (January 16, 2024) provides a concrete hygiene lesson: prioritize known exploited vulnerabilities in internet-facing systems and reassess whether exposed services are necessary. The advisory concerns a malware campaign; it is not evidence that Androxgh0st was AI-assisted. Apply the operational lesson broadly: maintain an authoritative vulnerability list, map findings to internet exposure and business criticality, and verify that fixes are installed rather than merely scheduled.
When a patch cannot be applied immediately, document the reason, isolate the system, remove unnecessary exposure, restrict access, increase monitoring, and set a short review date. Compensating controls are temporary risk treatment, not permission to leave an exposed service indefinitely.
Secure the AI systems and services your organization builds or operates
The Secure AI System Development Guidelines, co-announced by CISA and the UK National Cyber Security Centre in 2023, are intended for AI providers and other stakeholders involved in design, development, deployment, and operation. Use them as a lifecycle reference. The organization remains accountable for decisions even when a supplier hosts the model.
Design and data
- Write an intended-use statement and a list of prohibited or high-risk uses.
- Identify sensitive, regulated, confidential, and proprietary data before it enters prompts, training sets, evaluation sets, logs, or support channels.
- Define retention, deletion, residency, and secondary-use requirements contractually and technically.
- Use data provenance and integrity checks. Limit who can add training or retrieval content, and preserve version history so a corrupted dataset can be identified and removed.
- Model abuse cases such as prompt injection, malicious documents, data exfiltration, unauthorized tool calls, model extraction, and denial of service.
Development and testing
- Protect source code, model artifacts, credentials, signing keys, and build pipelines with least privilege and separate environments.
- Review dependencies and suppliers, including model libraries, hosted APIs, plug-ins, vector stores, and data providers.
- Test normal behavior and adversarial behavior. Include attempts to override instructions, retrieve restricted information, trigger unauthorized actions, and cause unsafe or misleading outputs.
- Set release gates for security findings, privacy issues, performance limits, and human-review requirements. Keep a rollback version and document who can authorize release.
Deployment and operation
- Authenticate every user and service; do not rely on a model’s apparent intelligence as an access-control mechanism.
- Give an AI tool only the minimum data and tools required for its task. Separate read access from write or transaction authority.
- Place high-impact actions behind explicit human approval, transaction limits, dual control, or an independent policy engine.
- Log identity, prompt or request metadata, retrieved sources, tool calls, policy decisions, model and configuration versions, and administrator changes. Minimize sensitive content in logs and protect the logs from alteration.
- Monitor for unusual volume, repeated probing, attempts to bypass controls, unexpected data access, and changes in output quality or safety behavior.
- Maintain a kill switch or isolation procedure and a manual fallback for critical work.
The cited guidelines provide the lifecycle scope, not a universal control checklist. Adapt the depth of testing and documentation to the system’s data, autonomy, exposure, and impact.
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How to secure the AI tools employees use
Unapproved use is often a governance failure rather than employee misconduct. Publish a short approved-use standard that answers four questions: which tools are allowed, what data may be entered, what actions require human verification, and how to report a problem.
Control data and identities
- Use centralized identity, multifactor authentication, and rapid offboarding for every approved service.
- Connect services through managed enterprise accounts where possible so access, retention, and audit settings are enforceable.
- Prohibit secrets, credentials, personal information, regulated records, unreleased financial information, and confidential source code in services that have not been approved for that data.
- Use data-loss prevention, browser or endpoint controls, and network restrictions to reduce accidental submission. Explain the reason for each restriction so staff can follow it.
- Review third-party plug-ins, connectors, and extensions. An assistant with access to email, files, code repositories, or ticketing systems can become a path to broad data exposure.
Make output verification mandatory where it matters
Require a person to verify recipients, financial instructions, code changes, access requests, legal text, safety decisions, and other consequential outputs. Treat generated content as untrusted until checked against authoritative records. Keep an independent approval channel for unusual requests; an attacker can use a convincing generated message to pressure an employee into bypassing normal procedure.
How to stop AI-assisted phishing attacks
AI can help an attacker produce polished, personalized messages, but the defensive answer is not to search for awkward grammar. Build controls around identity, process, and verification.
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- Configure email authentication and anti-spoofing controls, filter malicious links and attachments, and sandbox risky content.
- Require out-of-band confirmation for payment changes, credential resets, supplier-bank changes, and urgent executive requests. Use a known telephone number or an established internal channel, not contact details in the message.
- Train staff to report suspicious messages without punishment. Include realistic exercises that test the verification process rather than rewarding employees for spotting a particular writing style.
- Monitor sign-in anomalies, impossible travel, new forwarding rules, mailbox delegation, mass downloads, and unusual OAuth or API grants.
- Prepare account-takeover playbooks: revoke sessions and tokens, reset credentials, preserve evidence, check mailbox rules, notify affected parties, and review related transactions.
How to prepare for AI-assisted ransomware
Ransomware remains an availability, integrity, and business-continuity problem regardless of how an attacker gains access. AI-specific review should identify systems whose automated actions could accelerate encryption or propagation, but it must not displace ordinary resilience work.
- Segment critical systems and administrative networks; restrict east-west movement and privileged access.
- Maintain offline or otherwise isolated backups, protect backup administration separately, and test restoration of the business functions that matter most.
- Use endpoint detection, centralized logging, and alerts for credential abuse, mass file changes, unusual process execution, and deletion of recovery resources.
- Apply least privilege and separate service accounts. An AI assistant should never receive broad write access merely to perform a convenience task.
- Define decisions in advance: who can isolate systems, invoke continuity procedures, contact law enforcement, notify customers, and approve restoration.
- Exercise scenarios in which normal communications, identity systems, or the AI provider are unavailable. Keep a manual process for essential operations.
Detect, report, and respond as one organization
Security leadership must be part of business risk decisions, not only a technical escalation point. CISA’s guidance for corporate leaders and CEOs recommends empowering the CISO, making reporting expectations clear, involving executives and board members in exercises, and testing continuity for critical business functions.
Make reporting easy and specific
Publish one reporting route for suspected phishing, data exposure, unsafe model behavior, lost credentials, suspicious AI output, and supplier incidents. State what employees should include: what happened, when, which account or system was involved, what data may be affected, and what actions have already been taken. Tell staff not to delete messages or “investigate” by contacting a suspected attacker.
Include operational decision makers in exercises
Run tabletop exercises with security, IT, legal, privacy, communications, human resources, business owners, executives, and—where appropriate—board representatives. Scenarios should include a compromised AI integration, poisoned retrieval content, a provider outage, sensitive data submitted to an unapproved service, and a conventional ransomware incident accelerated by automation. Measure time to detect, contain, make a business decision, restore a critical function, and communicate accurately.
Use voluntary information sharing appropriately
CISA’s JCDC AI Cybersecurity Collaboration Playbook, announced January 14, 2025, describes voluntary information-sharing processes for AI-system incidents and vulnerabilities. CISA says, “The playbook provides voluntary information-sharing processes that, if adopted, can help protect organizations from emerging AI threats.” Participation is not mandatory, and an organization should confirm eligibility, partners, handling rules, and confidentiality expectations before sharing sensitive information.
Prioritize work by exposure, impact, and lifecycle
When resources are limited, rank work across four dimensions instead of labeling every task “AI security.”
| Priority question | Higher-priority condition | Typical first action |
|---|---|---|
| General cyber hygiene or AI-specific risk? | A basic control is missing, or an AI system has a unique data or autonomy risk. | Close the baseline gap while assigning an owner for the AI control. |
| Exposure and business criticality? | Internet-facing, privileged, sensitive, or essential to a critical function. | Restrict exposure, patch, segment, and test recovery. |
| Build or consume? | The organization develops the model or embeds it in a product, rather than merely using a low-impact hosted service. | Add lifecycle gates, supply-chain review, testing evidence, and release accountability. |
| Prevention, detection, response, or recovery? | A control is absent at the stage where failure would cause the greatest harm. | Fund the missing capability and exercise it under realistic conditions. |
A practical 90-day implementation plan
Days 1–30: establish visibility and authority
- Name the executive risk owner, security decision owner, and business owner for each critical AI use.
- List critical business functions and the systems, suppliers, identities, data, and AI services that support them.
- Identify internet-facing assets and close or restrict abandoned administrative interfaces.
- Publish interim rules for approved AI services, prohibited data, human approval, and incident reporting.
- Check multifactor authentication, privileged access, known exploited vulnerabilities, and backup status for high-impact systems.
Days 31–60: reduce exposure and test controls
- Patch known exploited vulnerabilities in internet-facing systems and document compensating controls for exceptions.
- Review AI data flows, connectors, plug-ins, model permissions, provider contracts, retention, and logging.
- Run adversarial tests against high-impact AI workflows, including prompt manipulation, data retrieval abuse, and unauthorized tool calls.
- Test phishing-resistant authentication and out-of-band verification for payment and account-change requests.
- Restore a representative critical service from protected backups and record the actual recovery time.
Days 61–90: make response repeatable
- Exercise a combined scenario involving an AI service compromise, credential theft, and disruption of a critical business function.
- Validate who can disable an AI integration, isolate a system, approve a fallback process, and communicate externally.
- Review detection coverage for identity abuse, unusual AI activity, data exfiltration, and destructive behavior.
- Present unresolved risks, exceptions, recovery results, and supplier dependencies to executives and the board or equivalent oversight body.
- Set recurring review dates for inventories, exposure decisions, model changes, access rights, patches, exercises, and policy updates.
Common mistakes that leave organizations exposed
- Buying a product instead of assigning ownership: a scanner, filter, or AI gateway cannot decide which business risk the organization is willing to accept.
- Creating an “AI policy” with no technical enforcement: rules must be backed by identity, data-loss, configuration, logging, and review controls.
- Focusing only on prompt attacks: compromised credentials, unpatched public systems, weak backups, and excessive privileges still cause major incidents.
- Allowing broad tool permissions: an assistant that can read every file or execute arbitrary actions turns a small compromise into a large one.
- Ignoring suppliers: require incident notification, access control, data-use limits, logging information, vulnerability handling, and service-continuity commitments.
- Testing only in a lab: exercises must include executives, business owners, communications, legal, and manual operating procedures.
- Treating AI output as evidence: verify generated claims, code, instructions, and decisions against trusted sources before acting.
What good looks like
An organization is in a stronger position when it can answer, quickly and accurately: Which AI systems and services do we use or provide? What data and actions can each access? Who can approve or disable them? Which public assets support them? How would we detect misuse? Which critical functions can continue without them? Where are our known exploited vulnerabilities? Who reports an incident, who decides, and how do we restore operations?
Those answers should be current, tested, and owned by named people. The combination of CISA’s six-function baseline, exposure reduction, the AI risk cycle of govern–map–measure–manage, lifecycle guidance for secure AI development, and practiced response gives leaders a defensible way to reduce both conventional and AI-related risk.
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