The cybersecurity landscape is changing fast. Artificial intelligence is already a force multiplier for defenders and also the Swiss Army knife for attackers. In 2024–2025, we watched scams go from sloppy phishing to emotionally convincing voice clones, malware adapt itself at runtime, and AI tools help both attackers and defenders find zero-days faster than ever.
A case example is the mid-September 2025 Anthropic cyberattack, where hackers “jailbroke” Claude (its AI) and used it to run large-scale cyberattacks. The attackers reportedly used Claude to perform 80–90% of the hacking operations autonomously.
In this article, I break down the top AI-driven cyberattacks to watch in 2026 alongside the practical defense strategies that can keep organizations ahead of the curve.
Let’s explore them together.
1) AI-driven social engineering and phishing
According to KnowBe4’s 2025 Phishing Threat Trends Report, between Sept 15, 2024, and Feb 14, 2025, 82.6% of the phishing emails analyzed exhibited some use of AI. The same report also observed a 17.3% increase in phishing emails in the same period.
Cyber criminals are now using generative models to create highly personalized, context-aware messages across email, SMS, social, and collaboration platforms. Instead of generic grammar-mistake-riddled lures, attackers generate messages that echo tone, project details, calendared events, and even the phrasing of a target’s boss, all at scale.
They scrape public profiles, company pages, and leaked data, feed that context into an LLM or multimodal generator, and produce dozens or thousands of tailored phishing lures within minutes. These lures can be multi-step: an initial benign message, a follow-up that references real projects, and a final request that tricks the recipient into revealing credentials or approving a transfer.
In February 2024, the European retailer disclosed a sophisticated fraudulent phishing attack on its Hungarian business that led to roughly €15.5 million ($16.3 million) in losses. This incident highlights how believable business email compromise scams can be.
AI defense strategies
Here are practical defense strategies to prevent this AI-enabled cyberattack:
- Use behavior and intent detection tools: Deploy email and messaging protection that uses machine learning to detect anomalous sender behaviors, unusual workflows, and subtle social engineering cues. Combine with transaction-monitoring rules for finance approvals.
- Deploy AI-powered simulation for training: Use red-team simulations that mimic AI-generated attacks to train humans, but ensure the simulations are controlled and only used to educate and create awareness.
- Strengthen authentication: Enforce strong multi-factor authentication, step-up approval workflows for financial transactions, and out-of-band verification, for instance, call a known number for any unusual request.
- Clean and minimize data: Reduce the publicly available data footprint for company employees so the raw material attackers use is smaller.
- Deploy adaptive email defenses: Choose solutions that continuously retrain detection models with recent threat telemetry and that can parse deep contextual signals such as attachments, domain age, and SPF/DKIM/DMARC disparities.
2) Deepfake scams and voice impersonation
Imagine receiving a call from someone who sounds exactly like a close family member or your company manager, urgently asking you to send money, only to later discover that the voice was a deepfake created by a scammer trying to trick you.
Deepfakes and voice impersonation use generative audio and video models to impersonate real people convincingly.
In fraud contexts, attackers can clone a CFO’s face and voice for a video call, or synthesize a family member’s voice to coerce money transfers. These are social-engineering attacks amplified by audiovisual authenticity.
To execute this scam, scammers gather audio or video clips of someone from social media or other online sources. They then use AI tools to set up a video call or voicemail mimicking that person’s voice or face.
In early 2024, fraudsters created a fake video conference call in which a digitally cloned version of Arup’s Hong Kong office CFO appeared to ask a finance staff member of the company to transfer funds for a “confidential transaction”. Believing the call was genuine, the employee initiated 15 transfers to five separate bank accounts in Hong Kong, resulting in losses of about US$25 million.
Although the company’s operations and internal systems were reportedly not compromised, the incident underscores how advanced AI-driven deepfake technology can bypass standard fraud controls.
AI defenses
Here is how to prevent deepfakes and voice impersonation:
- Assume audiovisuals can be faked. Create verification playbooks that require additional checks for any high-stakes instruction received over voice/video, such as authenticated calendar invites, verified corporate channels, or a pre-agreed safe phrase that is changed periodically.
- Deploy deepfake detection tools at ingress points (video conferencing gateways, voicemail systems): These tools are imperfect but can add an automated filtering layer.
- Implement process controls around payments. Require multi-party approvals and manual reconciliation for large transfers; ban unilateral fund transfers based solely on verbal approval.
- Conduct employee awareness and scenario drills. Train finance and HR teams on deepfake red flags and scripted response protocols. Make verification steps frictionless so staff will actually use them.
- Implement digital provenance and cryptographic attestations. Where possible, adopt tools that add signed metadata or provenance to executive communications, such as corporate-signed audio/video or secure communication platforms that use end-to-end attestation.
3) AI-enabled malware and botnets
According to SQ Magazine’s 2025 AI cyber-attack statistics, 41% of active ransomware families now use AI modules for adaptive behavior.
AI-enabled malware and botnets use machine learning components to improve stealth, adapt payloads, or automate decision-making inside an infection chain. There are also cases where adversaries use AI tools to write malware or generate exploit code.
Attackers can use AI to craft obfuscated payloads that evade static signatures, generate polymorphic code at runtime, or instruct bots to vary behavior based on environment telemetry, making detection harder. Separately, threat actors increasingly use LLMs and code generation assistants to prototype malware or scripting quickly.
Case example, in September 2024, HP threat researchers observed an email campaign where parts of a malware dropper were generated with the help of generative AI, a concrete sign that attackers are using AI to accelerate malware creation.
AI-powered defenses
Here are actionable AI-enabled strategies to prevent these attacks:
- Implement runtime behavior monitoring. Rely less on signatures and more on behavioral telemetry, EDR that looks at process lineage, telemetry correlation, and anomalous lateral movement. AI can help defenders identify previously unseen behavior patterns.
- Harden runtime environments. Use application allow-listing, strong endpoint configuration baselines, and memory protection techniques such as DEP/ASLR. Segment networks so botnets can’t easily pivot as well.
- Implement supply-chain and development controls. Monitor developer environments and CI/CD pipelines. AI-assisted code injection can happen during development, so secure the build and artifact signing process.
- Use deception and honeypots. Use honeynets and deception to observe adaptive behaviors safely and feed that intelligence into detection models.
4) AI-powered ransomware
According to a 2023-2024 threat-actor study, out of the 2,811 ransomware incidents recorded, 2,272 (80.83%) were directly tied to actors using AI.
Ransomware actors now use AI techniques to scale reconnaissance, automate lateral movement planning, craft bespoke payloads, or improve extortion strategies such as analyzing stolen data to identify high-value targets for public shaming. Even when the encryption routine is classical, AI often improves the campaign’s targeting and stealth.
Practically, threat actors use AI in multiple campaign stages. They run automated scanning to find high-value hosts, generate social engineering lures for privileged users, optimize timing and scope of encryption to evade backups, and produce more convincing extortion messages. AI can also assist during post-breach negotiation by composing realistic legal or PR threats.
A case example is the ransomware group FunkSec that emerged in late 2024 and is believed to use generative AI to assist with their malware development.
They’ve carried out double-extortion attacks, encrypting data and stealing it, and have claimed many organizations, including those in government and defense, technology, finance, and education.
AI defense strategies
Here is how to prevent these ransomware groups:
- Create immutable backups and air-gapped recovery copies. Ensure rapid recovery regardless of attack sophistication; test restores frequently.
- Implement attack surface reduction and least privilege. Harden RDP and privileged accounts; enforce just-in-time and just-enough-access principles.
- Use tools to detect pre-ransomware behaviors. Use AI models to detect pre-ransomware indicators: unusual data aggregation, discovery scans, and mass file access patterns. Successful AI defenses spot these pre-encryption patterns and isolate systems automatically.
- Utilize rapid isolation playbooks. Automate network segmentation and host containment when early ransomware indicators are seen.
- Deploy legal/PR playbook and extortion mitigation. Have processes for negotiating, forensic containment, and coordinated disclosure — AI will make exfiltrated data more readable, so your legal and response plans must adapt.
5) AI-accelerated zero-day exploits
Zero-day exploits are previously unknown vulnerabilities that attackers exploit before vendors can patch. AI accelerates vulnerability discovery by automatically scanning code, fuzzing with intelligent mutation strategies, or ranking the most promising bug candidates.
Ideally, AI reduces the time from “possible bug” to “exploit” by automating code analysis, triage, and exploit generation. That means both attackers and defenders can uncover zero-days faster — which raises the stakes. Importantly, defenders also use AI to find and patch bugs faster; it’s an arms race.
AI defense tactics
Here are strategies to prevent AI-accelerated zero-day exploits:
- Use proactive vulnerability discovery. Use AI-assisted static analysis, fuzzing, and runtime instrumentation to surface bugs before attackers do. If you ship software, bake automated AI-enabled testing into CI/CD.
- Implement rapid patch management and risk-based prioritization. With AI triage, prioritize fixes for high-impact paths such as authentication, deserialization, memory safety, and accelerate patch rollouts to exposed hosts.
- Exploit mitigations in runtime. Adopt memory-safety tools, enhanced sandboxing, control-flow integrity, and observability that make exploitation harder even if a zero-day exists.
- Use threat hunting with AI telemetry. Feed threat telemetry and telemetry-driven detection into models that look for signs of exploitation attempts (odd kernel calls, unusual memory patterns).
- Use responsible disclosure partnerships. Work with vendors, bug-bounty programs, and CERTs so discovered issues are triaged and patched quickly.
Stay Proactive, Not Reactive
As AI becomes more accessible, these threats will likely scale. The good news is that the same AI innovations also empower defenders — organizations that can proactively adapt can turn the tables. Implement layered defenses, strong governance, and continuous threat modeling in building resilient security postures for your organization.
Accept that attackers will leverage generative models, harden systems and processes so human trust cannot be hijacked, and deploy AI where it multiplies human judgement, for detection, triage, and automated containment.
Above all, practice the basics, including network segmentation, least privilege, immutable backups, and robust authentication, because those fundamentals blunt even the smartest AI-assisted attacks.