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AI is changing cybersecurity as a capability multiplier, not as a fully autonomous replacement for criminals or analysts. Attackers can research targets, write and troubleshoot code, translate messages, produce convincing phishing and synthetic media, and repeat those tasks at much greater scale. Security teams can apply the same technology to analyze telemetry, discover vulnerabilities, improve secure code, filter enormous email volumes, and triage incidents. The side with the advantage is usually the one that combines AI with better data, faster human decisions, stronger identity checks, patched systems, and practiced response procedures.
What has actually changed
Traditional cyberattacks still provide the foundation: phishing, credential theft, malware, denial-of-service, fraud, and exploitation of exposed systems. AI changes the economics and tempo of those operations. A small group can generate more variations of a lure, localize it for different audiences, investigate a target faster, and automate repetitive analysis. Defenders face the same pressure: alerts, logs, messages, code changes, and vulnerability reports arrive faster than a human team can inspect them unaided.
That does not mean every attack is now novel or self-directed. Google Threat Intelligence Group’s review published on 29 January 2025 found that threat actors using Gemini were mainly pursuing productivity gains in research, code troubleshooting, and content generation. It reported no observed novel offensive capabilities or persistent prompt attacks in that review. Later tracking identified suspected North Korea-, Iran-, and China-linked actors using AI at more stages, including reconnaissance, phishing-lure creation, command-and-control development, and data-exfiltration support. The practical conclusion is acceleration and broader use of familiar techniques, with evidence of autonomy and unprecedented capabilities still mixed.
How criminals are using AI
Research and reconnaissance
AI can summarize public information about an organization, turn scattered technical details into a target profile, and help an operator decide which employees, services, or technologies deserve attention. It can also explain unfamiliar code or documentation. This shortens the time between selecting a target and preparing an operation, even when the underlying attack method is conventional.
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Phishing and social engineering
Generative systems can draft fluent messages, imitate a company’s tone, translate them, and produce many versions for different recipients. That removes obvious spelling and grammar clues that once made mass phishing easier to spot. AI can also help create fake professional profiles and realistic supporting content, making a campaign appear to come from a colleague, supplier, or executive.
ENISA’s 2025 analysis of 4,875 incidents from 1 July 2024 through 30 June 2025 is EU-focused. It reported that more than 80% of observed social-engineering activity was described as AI-supported phishing by early 2025. The figure describes ENISA’s reporting and observation scope; it is not a measurement of every phishing attempt worldwide.
Malware and attack tooling
CISA warns that generative AI can lower the cost and increase the scale of malware development, including code intended to evade defenses. In practice, an operator may ask for help understanding an error, adapting an existing component, or generating variants rather than receiving a complete, autonomous attack. The speed of troubleshooting and modification is often the important advantage.
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Command, control, and exfiltration support
Later Google tracking found suspected state-linked actors using AI to support command-and-control development and data exfiltration. Assistance at these stages can reduce friction between an initial foothold and the movement of information, but it still depends on access, infrastructure, credentials, and operational decisions outside the model.
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DDoS and volume operations
AI can help plan, script, and vary denial-of-service activity. ENISA recorded 4,875 incidents in its stated period and reported that 77% were DDoS. Those numbers describe the EU incident set ENISA analyzed, not a global AI-attributed total. AI may make familiar DDoS work cheaper or easier to coordinate; the statistic does not prove that AI caused each incident.
Voice cloning, fake images, and deepfakes
CISA identifies voice cloning, realistic fake images, counterfeit profiles, and deepfakes as practical abuse cases. Microsoft’s Digital Defense Report 2025 describes synthetic media, including cloned voices and deepfake video, being used against companies and government organizations for sensitive information and financial loss. A convincing voice or video should therefore be treated as a claim that requires verification, not as proof of identity or authorization.
Where the evidence is strong—and where it is not
| Observation | What it supports | Qualification |
|---|---|---|
| Google’s 29 January 2025 review of Gemini misuse | Threat actors used AI for research, code troubleshooting, and content generation. | The review reported no observed novel offensive capabilities or persistent prompt attacks at that time. |
| Later Google Threat Intelligence tracking | Suspected North Korea-, Iran-, and China-linked actors used AI across reconnaissance, phishing lures, command-and-control development, and exfiltration support. | These are observations from Google’s threat-intelligence visibility, not a census of all attackers. |
| ENISA 2025 incident analysis | AI-supported phishing was reported in more than 80% of observed social-engineering activity by early 2025; 77% of 4,875 analyzed incidents were DDoS. | EU-focused analysis covering 1 July 2024–30 June 2025; the percentages are not global rates. |
| Microsoft Digital Defense Report 2025 | AI-assisted phishing, multi-stage attack chains, and synthetic-media fraud require automated filtering and verification. | Microsoft’s figures and observations reflect its own telemetry and products. |
This distinction matters when evaluating headlines about “AI hackers.” The documented pattern is mostly faster, more scalable assistance applied to known attack categories. Claims that AI has independently replaced criminal operators or security analysts go beyond the evidence summarized above.
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Telemetry analysis and detection
Security operations collect endpoint, identity, email, cloud, and network signals. AI can sift that volume, identify relationships, and surface a probable attack chain for an analyst. The value is not simply a faster alert; it is the ability to connect weak signals that look harmless when viewed separately.
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Microsoft says its systems screen about 5 billion emails daily, a 2025 figure that illustrates why automated filtering is necessary at internet scale. Filtering is not a final verdict: suspicious messages still require policy decisions, investigation, and a way to recover when a legitimate message is blocked or a malicious one passes through.
Incident triage and response
An AI assistant can organize alerts by affected identity or asset, summarize known evidence, and suggest investigation steps. Analysts remain responsible for confirming scope, deciding whether to isolate systems, and authorizing disruptive actions. Human review is particularly important when the model’s output is uncertain, manipulated by adversarial input, or based on incomplete telemetry.
Secure coding and vulnerability discovery
Google lists secure coding and vulnerability discovery among defensive uses. AI can inspect code for likely weaknesses, explain why a pattern is risky, and help developers produce a safer alternative. Teams still need code review, testing, and controlled deployment because generated code can introduce defects or misunderstand the application’s security requirements.
Operational streamlining
Routine work—normalizing reports, drafting investigation notes, mapping indicators to affected systems, or preparing a handoff—can be accelerated. Standardized outputs make it easier for a team to compare incidents and maintain continuity across shifts, provided a person verifies the important fields.
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Who has the advantage? Compare the capabilities, not the hype
| Axis | Attacker advantage | Defender requirement |
|---|---|---|
| Speed and scale | Generate and localize many lures, code variants, or profiles quickly. | Automate collection, prioritization, containment, and repeatable checks. |
| Quality and credibility | Produce fluent text and convincing synthetic voice, images, or video. | Validate identity, payment changes, unusual requests, and high-risk instructions through an independent channel. |
| Visibility | Operate across separate accounts and infrastructure to hide connections. | Correlate endpoint, identity, email, cloud, and model telemetry into one timeline. |
| Human oversight | Use a person to choose targets, approve actions, and exploit context the model misses. | Keep people accountable for high-impact decisions and challenge uncertain model output. |
| Fundamental resilience | Exploit exposed services, default credentials, and outdated software regardless of AI sophistication. | Reduce internet exposure, remove default credentials, patch outdated software, and test response procedures. |
| Collective learning | Reuse successful lures and techniques across campaigns. | Share incidents and indicators quickly so other organizations can block related activity. |
AI therefore does not create a permanent winner. A defender with clean telemetry and well-practiced controls can gain more from automation than an attacker gains from generating another message. A poorly maintained environment can lose to a basic intrusion even when its AI tools are advanced.
What security teams should do now
1. Reduce the attack surface before adding automation
- Inventory internet-accessible systems. Identify services, administrative interfaces, cloud resources, and remote access paths that do not need public exposure.
- Remove avoidable weaknesses. Replace default credentials, patch outdated software, and correct misconfigurations. CISA’s exposure-reduction guidance treats these as foundational controls, not optional AI measures.
- Protect the identities that can approve change. Establish clear ownership for privileged accounts and require a second verification path for unusual requests, especially those involving payments, secrets, or data exports.
2. Design for AI-assisted phishing and deepfakes
- Train staff that polished language, a familiar voice, or a live-looking video is not sufficient evidence of identity.
- Use an independently known phone number, secure channel, or established approval workflow to confirm high-risk instructions.
- Make urgent requests prove both who is asking and why the action is authorized; do not let urgency bypass normal controls.
- Record and investigate near misses, not only confirmed compromises, so new lure patterns reach the whole organization.
3. Connect telemetry before asking a model to explain it
Bring relevant email, endpoint, identity, cloud, and network events into a timeline that analysts can inspect. Define which data the AI system may access, how long prompts and results are retained, and who can approve automated actions. An assistant that cannot see the necessary context will produce confident but incomplete conclusions.
4. Keep a human decision gate
Use AI to rank, summarize, and suggest. Require a qualified person to confirm containment, credential resets, public notifications, legal escalation, and deletion or encryption of data. Review a sample of both accepted and rejected recommendations to detect drift, bias, or systematic blind spots.
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5. Exercise the full attack chain
Run scenarios that begin with an AI-written lure or deepfake request and continue through credential use, endpoint activity, cloud access, and attempted exfiltration. Measure time to detect, time to contain, evidence quality, and whether staff followed independent-verification procedures. Update playbooks when an exercise exposes a gap.
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AI introduces risks for defenders, too
CISA’s JCDC AI Cybersecurity Collaboration Playbook states that AI systems’ data-driven, non-deterministic properties create exposure to model poisoning, data manipulation, and adversarial inputs. A malicious document, crafted prompt, or tampered training source can influence what a defensive system highlights or ignores.
- Model poisoning: corrupted training or reference data can teach a system to misclassify activity.
- Data manipulation: altered logs, labels, or context can produce a misleading investigation summary.
- Adversarial inputs: specially designed content can cause an AI detector or assistant to make an incorrect decision.
Mitigations include controlling data sources, logging prompts and outputs, separating recommendation from execution, testing against hostile inputs, and retaining an independent investigation path. Never treat an AI-generated explanation as the sole record of why a high-impact action was taken.
Why information sharing is part of the defense
One organization rarely sees the entire campaign. A phishing lure, malicious domain, poisoned model artifact, or exploitation technique observed by one team may appear elsewhere hours later. CISA’s JCDC AI Cybersecurity Collaboration Playbook provides voluntary information-sharing processes for government, industry, and international partners. It describes sharing AI-related incidents and vulnerabilities so participants can warn one another and improve defenses.
Participation works best when reports contain usable context: what happened, which systems were affected, what indicators were observed, what confidence level applies, and which actions stopped or limited the activity. Sharing should respect legal, privacy, and contractual requirements, but withholding every detail also prevents collective learning.
A practical decision rule for leaders
Fund AI security capabilities when they improve a measurable bottleneck: alert triage, email screening, vulnerability review, investigation time, or secure-code feedback. Pair each deployment with an owner, a data-quality check, a human approval point, and a rollback procedure. If the basic environment still has unnecessary internet exposure, default credentials, unpatched software, or untested response plans, fix those issues first; AI will otherwise accelerate confusion around a preventable compromise.
The strategic shift is straightforward: assume attackers can produce credible content cheaply and repeatedly, while defenders must make trustworthy decisions quickly. AI can help both sides, but resilient systems, verified identities, connected visibility, human judgment, and shared learning determine whether that extra speed becomes protection or damage.
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