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Detect an AI-assisted cyberattack by validating the request and the surrounding activity—not by guessing whether the wording was produced by AI. Treat unexpected requests for money, credentials, sensitive data, access, or urgent changes as untrusted until you verify them through a known, independent channel. Then use ordinary security evidence—sign-in records, privilege changes, application consent, endpoint alerts, data-access logs, and reports from other people—to establish what happened.
“AI-assisted cyberattack” has two meanings. An attacker may use generative AI to make phishing, impersonation, voice cloning, or malicious content more convincing. Alternatively, the target may be an AI model, its prompts, retrieved data, training data, tools, or outputs. The response is different in detail, but the governing rule is the same: act on credible evidence of unauthorized or harmful activity without waiting to prove that an attacker used AI.
What counts as an AI-assisted cyberattack?
The label describes the attacker’s method or the system under attack; it is not a forensic conclusion that can usually be made from one email or log entry. Separating the two cases helps you collect the right evidence.
| Category | What the attacker does | Observable consequences |
|---|---|---|
| AI-enabled attack | Uses generative tools to draft convincing phishing, personalize messages, translate content, clone a voice, create a fake video, or accelerate reconnaissance and malware development. | A credible request, unusual sign-in, payment redirection, credential theft, malicious attachment, or other activity that could have been carried out without AI. |
| Attack on an AI system | Supplies malicious prompts, poisons data, abuses connected tools, manipulates retrieval content, or exploits weaknesses in the model’s controls. | Confidential data in an output, unsafe or unauthorized tool calls, changed model behavior, policy bypasses, abnormal refusals, or corrupted training or reference data. |
| Combined incident | Uses AI to obtain access and then targets an organization’s AI service or data. | Identity compromise followed by unusual model usage, data extraction, configuration changes, or suspicious application consent. |
Microsoft describes how AI can improve phishing language and how deepfakes and voice cloning may support social engineering in its Cyber Signals publication. The UK National Cyber Security Centre (NCSC) likewise warns that generative AI can produce convincing content. Neither source says that style alone proves AI authorship.
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How can I detect an AI-assisted cyberattack?
Start with the requested action and the context around it. A polished message is weak evidence; an unusual action, an untrusted destination, or a break from established process is stronger evidence.
Inspect the request, not the grammar
- Sender identity: Check the complete address, reply-to address, telephone number, account identifier, and the organization’s real domain. A display name can be copied.
- Destination: Hover over links and inspect the actual domain before opening them. Be cautious with look-alike domains, shortened links, unexpected sign-in pages, and attachments you were not expecting.
- Action and timing: Urgency, secrecy, pressure to bypass approval, or a request outside normal working patterns raises risk.
- High-impact request: Treat requests for payment, bank-detail changes, credentials, multifactor codes, sensitive files, access grants, or new software as requiring independent verification.
- Process mismatch: Compare the request with the organization’s normal purchasing, payroll, access-management, change-control, and data-sharing procedures.
AI can remove the spelling and grammar mistakes that once made some phishing messages easy to spot. That makes language quality a weaker signal, not a reason to trust a message. A human can write a flawless scam, and an AI system can produce an inaccurate or harmless message.
Verify the identity through a channel you already trust
Use a known telephone number, an existing chat thread, the organization’s directory, or the normal approval workflow. Do not use contact details supplied in the suspicious message. Ask the purported executive, supplier, colleague, or customer to confirm the exact action and destination. For a payment or bank-detail change, require the existing callback and dual-approval controls rather than accepting a new number in an email.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesApply the same rule to voice and video. A familiar voice, face, caller ID, or live-looking video is an identity claim, not proof of identity. Microsoft’s discussion of deepfakes and voice cloning makes independent confirmation particularly important for high-value requests.
Correlate user reports with security telemetry
One report may be an isolated scam; several reports can reveal a campaign. Compare reported messages with:
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- Sign-ins from unfamiliar locations, devices, or impossible travel patterns.
- Password-spray indicators, repeated failed authentication, or a sudden successful login after many failures.
- New privileged roles, mailbox rules, forwarding settings, OAuth or application-consent grants, and API keys.
- Endpoint alerts, unusual process launches, browser credential access, or new persistence.
- Unexpected downloads, bulk reads, external sharing, or transfers from sensitive repositories.
- Similar messages delivered to other employees, customers, or suppliers.
These signals remain useful whether or not AI was involved. Microsoft’s incident-response playbooks cover phishing, password spraying, application consent, compromised accounts, and malicious applications.
For AI services, collect AI-specific signals
Traditional identity and endpoint monitoring may miss an incident that occurs entirely inside a model or retrieval service. Microsoft’s AI incident-response guidance highlights output anomalies, changes in safety-classifier confidence, spikes in user reports, and telemetry gaps. Plan logs that can answer, subject to privacy, legal, and retention requirements:
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- Which AI service, model, configuration, and version produced the result.
- Which user, service account, application, or API key made the request.
- Which prompt, retrieved document, tool, plugin, or external source influenced it.
- Which tools were called, what parameters they received, and what actions followed.
- Whether the output was blocked, edited, reported, approved, or delivered to another system.
Record enough context to investigate without collecting unnecessary personal content. Standard security monitoring may not capture model behavior, so identify those gaps before an incident.
Can I tell whether a phishing email was written by AI?
Not reliably. There is no dependable style test that establishes authorship, and a detector should not settle whether a message is fraudulent. AI-generated and human-written messages can share the same wording, and an attacker may edit AI output before sending it.
Use authorship only as a secondary investigative question. The operational decision is whether the sender is genuine, the destination is safe, the request is authorized, and the action follows established controls. Preserve the original message, headers, links, and attachments for responders; do not forward a suspicious attachment to colleagues merely to ask what they think.
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What should I do if a voice or video message asks for money?
- Stop the transaction. Do not transfer funds, disclose a code, change bank details, or open a remote-access session while the request is unverified.
- End the contact politely. Do not challenge the caller with information that could help an attacker refine the impersonation.
- Use a pre-existing contact path. Call the person’s known number, start a new conversation in the approved collaboration system, or obtain confirmation through the normal finance workflow.
- Require the usual approvals. A voice or video request does not waive segregation of duties, callback, purchase-order, or dual-authorization rules.
- Report the attempt. Preserve the recording or message where policy permits, note the time and requested action, and send it through the organization’s incident channel.
If money or credentials were already sent, report it immediately to security, finance, the relevant bank or payment provider, and law-enforcement or regulatory contacts required by your organization. Speed matters more than proving whether the voice or video was synthetic.
A practical detection and escalation workflow
Use this sequence for an employee report, an automated alert, or an anomalous AI output. Adapt the exact thresholds to your incident plan.
- Record the trigger. Capture when and how the alert arrived, who noticed it, the affected account or service, and the requested or observed action. Keep the original message or event reference intact.
- Make a safe verification. For an identity claim, use an independent known channel. For a technical alert, compare identity, email, endpoint, cloud, network, data-access, and AI-service records. Do not test a suspicious link with production credentials.
- Escalate through the approved channel. Notify the security or IT function and assign an incident owner. The owner should maintain a timeline, decisions, evidence locations, and people responsible for each action.
- Classify the potential impact. Identify whether credentials, funds, privileged access, personal data, intellectual property, customer systems, or an AI model and its connected tools are involved. Use the organization’s severity and notification thresholds.
- Contain under authorization. Depending on the evidence, containment may include disabling a session, resetting credentials, revoking tokens, removing application consent, isolating an endpoint, blocking a domain, pausing an integration, or restricting an AI tool. Preserve evidence before destructive changes when doing so is safe.
- Preserve and coordinate. Retain message headers, authentication records, endpoint and cloud logs, access history, model and prompt context, configuration versions, and user reports according to policy. Involve legal, privacy, communications, finance, business owners, and external responders when appropriate.
- Recover and watch. Restore trusted configurations, check for persistence and repeat access, monitor affected identities and services, and confirm that business processes work safely before closing the incident.
- Improve the controls. Document the root cause, missed signal, decision delay, and successful control. Update training, approvals, detections, playbooks, and exercises, then test the changes.
Microsoft’s incident-response overview emphasizes scoping, containment, evidence, and coordinated recovery. NIST SP 800-61 Revision 3, finalized in April 2025, places incident response within cybersecurity risk management and uses Detect, Respond, Recover, and continuous improvement, with preparation supported by the broader Govern, Identify, and Protect functions. See the current NIST publication and its incident-response project page; Revision 3 supersedes Revision 2.
How to respond when an account or conventional system is compromised
Suspected phishing or credential theft
- Keep the original message and report it; do not delete the only copy.
- Use the organization’s approved process to reset the affected password and revoke active sessions or tokens.
- Check multifactor settings, recovery details, mailbox rules, forwarding, delegated access, and application consent.
- Search for the same sender, links, attachment hashes, or subject in other mailboxes and quarantine matching messages.
- Review sign-ins and data access before and after the reported event to establish scope.
Password spraying or suspicious sign-ins
- Identify all targeted accounts and whether any successful sign-in followed the failures.
- Require credential and token remediation for affected identities according to policy.
- Review conditional-access, device, location, and privilege changes.
- Check for lateral movement, newly created accounts, and access to sensitive applications.
Unexpected application consent or privilege change
- Record the application identifier, permissions, consenting user, time, and tenant or environment.
- Revoke unauthorized consent and tokens only after preserving the relevant audit records.
- Determine what data the application could read or modify and whether it actually did so.
- Review other users and environments for the same application or permission pattern.
These branches are technology-specific applications of the response lifecycle; follow your organization’s approved playbook and escalation thresholds rather than improvising irreversible actions.
How to respond when an AI system is manipulated or leaks data
Prompt injection
The NCSC describes prompt injection as input designed to make an AI system behave in an unintended way, potentially revealing confidential information or triggering unintended consequences when input is not checked. Examples include instructions hidden in a retrieved document, a web page, an uploaded file, or a user message that attempts to override the system’s intended task.
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Contain by disabling the affected workflow or tool permission when necessary, preserving the prompt and retrieved context, identifying exposed data, and checking whether the model called external tools. Do not assume that a single clean response proves the problem is gone.
Data poisoning
Data poisoning means tampering with training or reference data so that the system produces undesirable outcomes. The NIST AI 100-2 E2025 taxonomy provides broader terminology for adversarial machine-learning attacks and mitigations. Investigate who changed the data, which versions consumed it, what outputs or decisions were affected, and whether an untrusted source remains connected.
Output leakage or unsafe tool use
- Pause the affected model, agent, connector, or tool path if continued operation could expose data or cause harm.
- Identify the model and application version, user or service identity, prompts, retrieved records, tools, destinations, and resulting actions.
- Determine whether the output was merely displayed or was written to a database, sent externally, used in a decision, or executed as a command.
- Rotate exposed secrets, revoke unnecessary permissions, and correct the retrieval, filtering, or authorization control.
- Test with varied inputs and monitor for a defined watch period before restoring normal access.
Microsoft recommends staged remediation for AI incidents: immediate containment, broader mitigations, and longer-term source-level fixes. Because model outputs can be non-deterministic, varied-input validation and continued monitoring are more meaningful than one successful test. Treat that sequence as vendor guidance and align it with your approved incident plan.
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Preserve evidence in a way that supports investigation and protects people. Keep timestamps with their time zone, record who collected each item, and restrict access to sensitive messages, prompts, personal data, and recordings. Coordinate public statements and customer notifications through the designated communications and legal functions.
Avoid uploading suspicious files, confidential prompts, or customer data to public scanning services without approval. Microsoft’s response overview cautions that adversaries may monitor public scanner submissions. Use an internal sandbox or an approved specialist service when policy allows.
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Communicate facts and actions, not speculation about AI authorship. A useful internal alert says which account, system, data, or process may be affected; what users must stop doing; where to report similar events; and when the next update is due.
Controls that make detection faster
People and process
- Publish one incident-reporting route and make it easy to use from email, chat, endpoint, and AI applications.
- Require independent verification for payments, bank-detail changes, privileged access, sensitive-data requests, and urgent exceptions.
- Train employees to report suspicious requests without requiring them to prove that AI was used.
- Exercise voice, video, supplier, executive, and AI-output scenarios with finance, legal, privacy, communications, and business owners.
Identity, endpoint, and application controls
- Use phishing-resistant authentication where feasible, least privilege, device assurance, and short-lived tokens.
- Alert on unusual consent, privilege, forwarding, recovery, and API-key changes.
- Centralize sign-in, endpoint, email, cloud, network, and data-access logs with sufficient retention for investigation.
- Maintain tested playbooks for phishing, password spraying, malicious applications, compromised accounts, and data exposure.
AI-system controls
- Inventory models, versions, prompts, retrieval stores, agents, plugins, tools, service accounts, and data flows.
- Separate model output from authorization: require deterministic policy checks before high-impact actions.
- Limit tool permissions and outbound destinations; log calls and enforce rate, scope, and approval boundaries.
- Monitor output anomalies, safety-classifier changes, report-volume spikes, and gaps in model telemetry.
- Protect training and reference data with provenance, access controls, integrity checks, versioning, and rollback.
For externally developed AI systems, the Joint Guidance on Deploying AI Systems Securely recommends applying security controls to protect, detect, and respond to malicious activity against AI systems, their data, and related services.
Choosing the right evidence and response depth
No single monitoring layer is sufficient. Use the following decision framework rather than treating AI-specific telemetry as a replacement for conventional security operations.
| Decision axis | Option A | Option B | When to use each |
|---|---|---|---|
| Detection evidence | Identity, email, endpoint, network, and data-access signals | Model output, classifier, prompt, retrieval, and tool telemetry | Use conventional signals for account and device compromise; add AI signals whenever a model, agent, or AI data path is involved. |
| Remediation timing | Immediate containment | Durable source-level remediation | Contain first when harm may continue; fix permissions, data, prompts, code, and governance after scope is understood. |
| Response capability | Internal security and IT team | External incident-response or specialist support | Use internal owners for coordination; escalate when impact, regulatory exposure, forensic complexity, or 24-hour coverage exceeds capacity. |
| Visibility and privacy | More detailed logging | Minimized collection and retention | Design logs to answer investigative questions while meeting privacy, employment, contractual, and legal obligations. |
Common mistakes to avoid
- Waiting for an AI verdict: Unauthorized access or a harmful transfer warrants action even when authorship is unknown.
- Trusting polished language: Fluency is not authentication, and awkward language is not proof of fraud.
- Calling the number in the message: An attacker controls supplied contact details. Use a directory or an existing record.
- Resetting one password and stopping: Investigate sessions, tokens, consent, forwarding, privilege, and data access as well.
- Collecting no AI context: Without model version, prompt, retrieved content, and tool-call records, an AI incident can be impossible to reproduce.
- Testing only once: Non-deterministic systems require varied inputs and a watch period.
- Over-sharing evidence: Public uploads can disclose confidential data or alert the adversary.
- Closing after containment: Persistence, repeat access, notification duties, and control improvements remain to be checked.
A concise employee checklist
- Pause before paying, signing in, sharing data, granting access, or changing a process.
- Inspect the sender, destination, attachment, timing, and requested action.
- Verify using a known independent channel and the normal approval workflow.
- Report the message, call, video, sign-in, or AI output through the approved route.
- Do not delete originals or upload them to an unapproved public service.
- If you already acted, say exactly what was sent, entered, opened, approved, or transferred and when.
The NCSC cautions that AI “can get things wrong and present incorrect statements as facts.” That warning applies to both defensive automation and attacker-generated content: treat an AI system’s output as evidence to validate, not as an authority that replaces human approval and security controls.
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