Detect an AI-assisted cyberattack by validating the request, identity, destination and resulting activity—not by trying to prove that a message was written by artificial intelligence. Treat unusual demands for money, credentials, sensitive data, access or urgent changes as possible incidents; verify them through a known independent channel, then investigate ordinary security evidence such as sign-ins, consent grants, privilege changes, endpoint alerts and data access. If an AI model or service is involved, add model, prompt, output and tool-use telemetry to the investigation. Do not wait for certainty about an attacker’s use of AI before containing credible compromise.
What “AI-assisted cyberattack” means
The term covers two related situations that require different evidence. In the first, an attacker uses generative AI to improve a conventional operation: phishing, business-email compromise, password attacks, impersonation, malware delivery or fraud. Better grammar and more convincing personalization can remove familiar warning signs. Microsoft says AI can improve phishing language and discusses deepfakes and voice cloning as potential social-engineering developments in its Cyber Signals publication. The UK National Cyber Security Centre (NCSC) likewise explains that generative AI can produce convincing content in its AI and cyber-security guidance.
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In the second, the target is an AI system, its model, prompts, retrieved data, tools, training data or supporting services. An attacker may try to make a model reveal information, call a tool, produce unsafe output or behave differently after its data has been tampered with. The response must examine both conventional security controls and AI-specific behavior.
How can I detect an AI-assisted cyberattack?
Start with the requested action and the surrounding activity. Language style is weak evidence. A polished message can be fraudulent, and a poorly written one can be legitimate. Assess whether the sender, destination, timing, authority and requested action fit an established process.
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Check the sender and destination
- Inspect the complete sender address, reply-to address and destination domain rather than relying on a display name.
- Hover over links or examine them in a safe preview; look for look-alike domains, unexpected redirects and destinations unrelated to the claimed organization.
- Treat unexpected attachments, authentication prompts and requests to enable macros or browser extensions as high-risk until independently verified.
- Compare the request with the organization’s normal purchasing, payroll, access-management and change-control procedures.
Look for pressure and unusual authority
- Urgency, secrecy, threats of consequences or a demand to bypass a second approver are behavioral warning signs.
- Requests to change bank details, purchase gift cards, transfer funds, disclose credentials or export sensitive data deserve a second-person or callback control.
- An executive, supplier or colleague’s apparent identity is not proof of authenticity. Verify using a phone number, directory entry or workflow already on file—not contact information supplied in the message.
Correlate reports with security telemetry
One suspicious message may be a harmless mistake; several reports plus technical anomalies may indicate an active campaign. Review sign-in and conditional-access events, unfamiliar devices or locations, password-spray indicators, mailbox rules, newly granted application consent, privilege changes, endpoint detections, unusual downloads and access to sensitive repositories. Microsoft’s incident-response playbooks cover phishing, password spraying, app-consent grants and compromised or malicious applications. Those scenarios remain relevant whether or not an attacker used AI.
| Observable signal | Why it matters | Immediate check |
|---|---|---|
| Unexpected credential or multifactor prompt | Could be phishing or an attempt to capture an approval | Confirm the sign-in record, device, location and application; deny unrecognized prompts |
| New application consent or privilege | May give an attacker durable access without stealing a password again | Identify who granted it, what scopes were granted and whether the app is approved |
| Mailbox forwarding rule or unusual outbound mail | Can support persistence, surveillance or follow-on fraud | Review rules, sign-in history and messages sent during the suspicious period |
| Unusual data access or bulk download | May indicate collection or exfiltration | Identify account, device, repository, files and the time window |
| Multiple employees reporting similar messages | Suggests a coordinated campaign rather than an isolated error | Cluster indicators, preserve samples and search mail and identity logs |
| Endpoint, email or cloud alert near the request | Technical evidence can confirm impact even when the message is persuasive | Follow the relevant containment playbook and preserve alert context |
Can I tell whether a phishing email was written by AI?
Not reliably. AI may remove spelling and grammar mistakes, but clean prose is not an authorship test. The available guidance supports treating language quality as a weaker cue, not using an “AI detector” to settle whether a message is fraudulent. Microsoft and the NCSC both emphasize the potential for convincing generated content, while the practical security question remains whether the request and activity are authorized.
Do not dismiss a suspicious message because it contains a typo, and do not trust it because it is perfectly written. Preserve the original message, headers and attachment metadata for your security team. Avoid forwarding sensitive samples to public scanning services until your organization has considered confidentiality and monitoring risks; Microsoft’s incident-response overview cautions that adversaries may monitor public scanner submissions.
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How to verify an unusual request, including voice and video
Use the same control for email, chat, phone, voicemail, video and collaboration tools: stop the requested action and confirm the person and intent through an independent channel. A convincing voice or video is an identity claim, not an authentication factor. Microsoft’s Cyber Signals publication discusses deepfakes and voice cloning as possible social-engineering developments.
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- Pause the transaction. Do not click, disclose a code, approve a prompt, change bank details or transfer funds while the request is unverified.
- Find a trusted contact route. Call a known number from the corporate directory, start a new message thread, or use the ordinary procurement or change-management workflow. Never use a number, link or reply address supplied by the suspicious request.
- Confirm the exact action. Ask what is being requested, why it is needed, the amount or data involved, and which approvals are required. For payments and bank-detail changes, apply the organization’s existing callback and dual-approval controls.
- Record the result. Note who verified the request, when, by what channel and what evidence was checked. If verification fails or the request was malicious, report it through the incident channel.
What to do when the evidence is credible
Do not wait to identify the attacker’s model or prove that a message was AI-generated. Use the organization’s incident plan and the current NIST baseline, SP 800-61 Revision 3, finalized in April 2025. Rev. 3 places incident response in broader cybersecurity risk management and organizes operational work around Detect, Respond and Recover, with preparation and continuous improvement across the functions.
1. Escalate and assign ownership
Report through the designated security channel, even if the suspected attack appears to target only one employee. Record when and how the alert arrived, assign an incident lead and identify the business owner. Clear ownership prevents duplicated actions, lost evidence and uncoordinated communications. Microsoft’s playbooks and NIST both emphasize defined responsibilities and coordinated investigation.
2. Verify the trigger
If the alert is a request or identity claim, use an independent channel as described above. If it is a technical detection, validate it against the relevant email, identity, endpoint, cloud or AI-system telemetry. Keep the investigation focused on unauthorized or harmful activity; proving AI authorship is optional and often impossible.
3. Scope the incident
Identify affected accounts, devices, applications, data stores and services. Establish the earliest suspicious time, the latest confirmed activity, the attacker’s likely objective and whether access is still active. Search for related messages, sign-ins, consent grants, privilege changes, persistence mechanisms and data movement. Microsoft’s response overview and playbooks provide scenario-based investigation and containment guidance.
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4. Contain with approved actions
Follow documented thresholds rather than improvising. Depending on scope, containment may include disabling or risk-resetting an account, revoking sessions and tokens, removing malicious consent, blocking a sender or domain, isolating an endpoint, suspending a compromised integration, or restricting access to an affected data set. Preserve enough information to support investigation before destructive cleanup when your plan permits; emergency safety actions take priority when active harm is occurring.
5. Preserve evidence and coordinate
Retain original messages and headers, identity and endpoint logs, access history, alert records, user reports, relevant files and a timeline. If an AI system is involved, preserve the model and system version, prompts, retrieved content, outputs, tool calls and resulting actions where policy and law allow. Coordinate security, IT, legal, privacy, communications and business owners. Apply retention, employment, contractual and regulatory requirements to every collection decision.
6. Recover, watch and improve
Restore trusted service, remove persistence, check for repeat access and monitor after remediation. Notify affected parties through approved communications. NIST Rev. 3 treats improvement as continuous: update controls, playbooks, training, approval workflows and exercises using what the incident revealed.
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How to investigate an AI system that was manipulated or leaked data
Standard security logs may show that a service was accessed without explaining why its output changed. Microsoft’s AI incident-response guidance calls out output anomalies, shifts in safety-classifier confidence, spikes in user reports and telemetry gaps as AI-specific concerns.
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Capture the AI event context
- Which application, model, deployment and version handled the request?
- Which user, service account, session and authorization scope were involved?
- What system instructions, user prompts, retrieved documents and external inputs were supplied?
- Which tools, plugins, connectors or APIs were called, with what parameters and results?
- What output was returned, who saw it, and what downstream action followed?
- Were safety filters, classifiers, human approvals or rate limits triggered, bypassed or unavailable?
Design logging with privacy and retention obligations in mind. Detailed prompt or output capture may contain personal, confidential or regulated information, so define access, minimization, retention and deletion rules before an incident. The examples above are operational guidance, not a universal legal mandate.
Use staged remediation
For an AI-system incident, use a three-stage pattern adapted from Microsoft’s guidance: immediate containment, broader mitigations and longer-term source-level fixes. Containment might disable a tool, isolate a retrieval source, roll back a configuration or restrict affected accounts. Broader mitigation can add validation, approval gates, filtering, access controls and monitoring. Source-level work may correct training or retrieval data, code, model configuration or integration logic.
Do not clear a non-deterministic system after one successful test. Validate with varied inputs, monitor during a defined watch period and investigate recurrence. Record which tests were run and what residual risk remains.
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Prompt injection
The NCSC defines prompt injection as input designed to make an AI system behave in an unintended way. Depending on the system’s permissions and data connections, an injection could cause confidential information to be revealed or unintended consequences to be triggered. Treat retrieved documents, web pages, uploaded files and tool responses as untrusted input; enforce authorization and output checks outside the model.
Data poisoning
Data poisoning means tampering with training data so the resulting system produces undesirable outcomes. Investigate the provenance, integrity and change history of training, fine-tuning, retrieval and evaluation data. NCSC describes the risk, while NIST AI 100-2 E2025 provides broader adversarial-machine-learning terminology and mitigation concepts.
Compromised integrations and model services
An AI feature can inherit ordinary identity, application, supply-chain and cloud risks. A stolen service credential, excessive connector permission or malicious application may expose data even when the model itself is functioning as designed. Apply controls that protect, detect and respond to malicious activity against AI systems and their related data and services, consistent with the Joint Guidance on Deploying AI Systems Securely.
Telemetry and detection design
Build two complementary views rather than replacing conventional monitoring with an AI-only dashboard.
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|---|---|---|
| Identity and access | Sign-ins, multifactor events, token use, role changes, consent grants | Who accessed the system, from where, with which authority? |
| Email, endpoint and network | Messages, attachments, process alerts, DNS, proxy and egress activity | How did the request arrive and did a device or connection execute follow-on activity? |
| Application and data | API calls, repository reads, downloads, exports and configuration changes | What data or function was reached, changed or removed? |
| Model and output | Model/version, prompts, retrieved sources, outputs, classifier confidence and report volume | What input or configuration produced the anomalous behavior? |
| Tool and workflow | Connector calls, parameters, approvals, human overrides and downstream actions | Did the system act beyond the user’s intended or authorized request? |
Correlate timestamps across these layers. A sudden output anomaly without an identity or configuration change may require a different hypothesis from an anomaly that follows a new connector permission. Gaps should be recorded as uncertainty, not silently treated as evidence that nothing happened.
Preparation that makes detection and response faster
- Publish one reporting route for suspicious messages, calls, videos, prompts and AI outputs; make it easy for employees to preserve the original evidence.
- Define independent verification and dual-approval rules for payments, bank-detail changes, privileged access and sensitive-data transfers.
- Maintain current contacts for security, IT, legal, privacy, communications and business owners, including an incident lead and backup.
- Test playbooks for phishing, password spraying, malicious application consent, account takeover, data exposure and AI-system misuse.
- Inventory AI models, versions, data sources, connectors, service accounts, owners and dependencies so responders know what “scope” includes.
- Set logging, access, retention and privacy rules before a crisis; confirm that logs can answer what model ran, what it received and what it did.
- Exercise deepfake and voice-cloning scenarios so staff practice calling a known number instead of trusting the apparent speaker.
- Review lessons after incidents and near misses, then update controls and training rather than treating awareness as a one-time project.
Choosing an investigation approach
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| Decision axis | Use when | Trade-off to manage |
|---|---|---|
| Conventional identity, email, endpoint and network evidence | The suspected attack is phishing, account compromise, malware or unauthorized data access | May not explain anomalous model behavior without AI telemetry |
| AI-specific output and model telemetry | A model, retrieval system, classifier or tool integration may be manipulated | Prompt and output logs can create privacy, retention and access risks |
| Immediate containment | There is active unauthorized access, harmful output or ongoing data exposure | Fast action can disrupt business processes and reduce later evidence if not recorded |
| Durable remediation | The cause involves code, training data, permissions, configuration or workflow design | Takes longer; requires validation and a watch period before closure |
| Internal response | The organization has trained staff, usable logs and a tested plan | Capacity may be exceeded by a widespread or technically novel incident |
| External specialist escalation | Scope, legal exposure, forensics or recovery exceeds internal capability | Requires controlled evidence sharing and clear authority |
Common mistakes that delay a safe response
- Hunting for an AI writing signature: authorship is usually unprovable and distracts from verifying the requested action.
- Trusting a familiar voice or face: deepfake and voice-cloning capabilities make independent callback controls essential.
- Waiting for complete certainty: credible evidence of unauthorized access is enough to invoke the incident plan.
- Deleting the message or resetting everything without a record: containment matters, but preserve the timeline and relevant evidence when safe and permitted.
- Sending confidential samples to public scanners: consider that adversaries may monitor those submissions.
- Logging only traditional security events: without model, prompt, retrieval and tool context, an AI investigation may not explain the failure.
- Declaring an AI incident fixed after one test: varied inputs and a watch period are needed for non-deterministic systems.
A practical decision rule for employees and leaders
If a request is unusual and consequential, pause and verify independently. If verification fails, report it. If technical evidence shows unauthorized access, contain it under the incident plan. If an AI system is implicated, preserve its execution context and add model-specific telemetry to the same investigation. The decisive facts are authorization, impact, scope and persistence—not whether a human or a model composed the first message.
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