AI and cyberthreats in 2026: how the attack landscape is changing and how to defend

I've spent years working with security and leadership teams that always ask me the same question: does AI help us, or does it make us more vulnerable? My short answer in 2026 is: both, at the same time. In this guide I tell you, without marketing, which AI-driven threats I'm seeing appear most often in reports, which new vectors (prompt injection, data poisoning, attacks on autonomous agents, RAG abuse) are worrying CISOs and European regulators the most, and which concrete measures you can start applying this very week in your organization.

1. 2026 landscape: why AI changes the cybersecurity game

When I talk with CIOs and CISOs across Europe, what they convey to me is a growing sense of asymmetry: attackers have brought their marginal cost of attack close to zero thanks to accessible generative models, while defenders still cope with finite budgets, thin teams and accumulated technical debt. That's the honest snapshot I see in 2026.

AI does not invent radically new threats in most cases, but it industrializes the existing ones: targeted phishing moves from craft to mass production; OSINT reconnaissance takes minutes instead of days; and malware kits adapt to the victim with no human in the loop. At the same time, genuinely new vectors appear tied to the AI systems organizations are now deploying.

In practice, I group AI threats in 2026 into three blocks worth keeping in mind:

• Threats amplified by AI: phishing, deepfakes, polymorphic malware, fraud. Same old, faster and cheaper.
• Threats against AI systems: prompt injection, data and model poisoning, evasion, model theft. New attack surfaces almost no risk register had two years ago.
• Threats through autonomous agents: abuse of copilots, corporate RAGs and agents with permissions to act. This is where I'm seeing most new exposure in 2026.

The European regulatory framework (NIS2, DORA, AI Act, GDPR) already requires explicit treatment of these risks. It's no longer a "nice to have": it's a compliance requirement that affects any relevant company in the supply chain.

2. Phishing and social engineering with generative AI

The most evident shift I've seen since 2024 is that phishing stopped having spelling mistakes. LLMs let any attacker without language skills write perfectly drafted emails, with credible corporate tone and accurate contextual references. That alone is no longer news. What is news in 2026 are three things.

First, hyper-targeted phishing: the attacker combines LinkedIn, GitHub, prior leaks and your company's public web to generate emails that mention your actual boss, a real internal project and a believable deadline. I've seen campaigns personalized down to the individual within the same team.

Second, sustained conversations: it's no longer a single email. The attacker keeps a thread going for several days, replies to questions, attaches plausible documents, and only on the fifth or sixth message asks for the critical action (wire, IBAN change, credentials). Traditional filters look at messages in isolation; this pattern bypasses them.

Third, synchronized multichannel: the email is paired with an SMS, a call and a WhatsApp message from the "same" sender, all generated or assisted by AI. The victim experiences coherence where there used to be friction.

My practical recommendations, ordered by impact:

• Activate DMARC at p=reject on every corporate domain and subdomain. If you're still on p=none, you are not protected, you are measuring.
• Implement mandatory out-of-band verification for any financial operation or bank data change above an agreed threshold. No exceptions for urgency.
• Train your team with AI-generated phishing simulations, not the templates of five years ago. The gap between what you train and what they receive is what kills you.
• Deploy behavior-based detection in email (atypical sending, reply patterns, login geolocation). Static signatures no longer cut it.

3. Voice and video deepfakes: CEO fraud 2.0

Classic CEO fraud used to be an email. In 2026 it's a video call. And that changes the entire control framework. When a finance employee gets a video call from what looks like the CFO, with the right face, voice and verbal tics, asking for an urgent wire, the human reflex to obey is very hard to neutralize through training alone.

Voice deepfakes are now within reach of anyone with 30 seconds of audio of the target (interviews, podcasts, corporate videos). Real-time video deepfakes require more resources, but the cost drops every quarter. I view this as a tier-1 threat for 2026-2027 in mid-sized and large companies.

What I'm recommending to my clients:

• Establish code words known only within finance leadership, rotated quarterly, to validate critical instructions over voice or video. Sounds like a spy movie, works in practice.
• Mandate dual channel verification: any order received over voice/video is confirmed via a different channel (signed Teams, callback to the internal directory number, not the one on screen).
• Train specifically finance, HR and leadership with real recent deepfake examples. Knowing "this is possible" is half the job.
• Consider deepfake detection tooling in corporate videoconferencing platforms. The tech is maturing fast in 2026.

4. Adaptive malware and AI-driven evasion

The next front that concerns me is malware that learns from its environment at runtime. We're talking about payloads that detect whether they're in a sandbox, whether EDR is present, which antivirus version is running, which accounts are active, and adapt their behavior to fly under the radar or to pick the optimal moment to execute.

It's no longer science fiction: in 2026 we're seeing malware families that generate polymorphic variants on the fly, change their indicators of compromise (IOC) on every infection, and consult remote models to decide their next move. Signature-based detection does not scale to this; behavior-based detection does, but it requires SOC maturity.

Three defense actions I always recommend:

• Migrate to EDR/XDR with behavior-based detection on every critical endpoint, not on a sample. Partial coverage is the illusion of security.
• Apply application allowlisting on servers and high-value endpoints (leadership, finance, engineering). It drastically reduces the surface even if the payload lands.
• Assume breach in the design: network segmentation, least privilege, lateral movement monitoring. If malware gets in, it shouldn't be able to move.

5. Prompt injection: the new XSS of LLMs

If there's one new vector consuming every security committee I attend, it's prompt injection. It is to AI applications what XSS was to the web twenty years ago: a structural design flaw, not a one-off bug, affecting practically any application that combines an LLM with untrusted content.

The mechanism is simple: an attacker injects malicious instructions into any text the LLM will process (an email, a document, a web page, a database entry). The model does not distinguish between "legitimate developer instruction" and "instruction injected by the attacker", because to the model it's all just text. Result: the LLM ends up executing what the attacker wrote in the customer email your copilot just summarized.

The two variants I'm seeing most:

• Direct: the attacker interacts with the LLM and tries to bypass its guardrails ("ignore your previous instructions and..."). The most known and easiest to mitigate.
• Indirect: the attacker places the instruction in a document, page or email that another user will feed the LLM. This is the dangerous one, because the victim doesn't know they are pushing hostile content through the model.

Mitigations that work in 2026:

• Separate contexts: the system prompt and external content must travel through clearly distinct channels, and the model must be explicitly instructed to treat external content as data, never as instructions.
• Validate outputs: if the LLM can take actions (send email, call API, write to DB), filter and validate each output before it reaches the executor.
• Least privilege for the agent: the LLM only has access to what is strictly necessary. If your copilot can read all of Drive, it can also exfiltrate all of Drive.
• Continuous auditing: logs of every prompt, response and executed action. No telemetry, no forensics.

6. Data and model poisoning

Poisoning is one of the AI threats I find hardest to explain outside the technical committee, because it doesn't show up in a log. It consists of contaminating the data used to train or fine-tune a model (or the corpus it queries in production) so that the resulting model makes wrong, biased or attacker-controlled decisions.

Three realistic scenarios that worry me in 2026:

• Training poisoning: the attacker injects manipulated samples into public datasets or scraping pipelines. A model trained on that data inherits the bias or the backdoor.
• Fine-tuning poisoning: the attacker manages to insert samples into a fine-tuning process (sometimes by compromising a labeling vendor). The model passes evaluations but responds predictably to a specific trigger.
• RAG poisoning: the attacker manages to push hostile documents into the corpus your RAG system consults in real time. From then on, the model responds with false data or executes instructions hidden in those documents.

How we tackle it in organizations seriously deploying AI:

• Provenance and signing of every dataset and corpus document. If you don't know where it came from, it doesn't go in.
• Adversarial tests before promoting a model to production: trap prompts, backdoor detection, output comparison against a baseline.
• Drift monitoring: if the model starts answering differently without you changing anything, something smells off in the data.
• Pipeline isolation between sensitive and public data flows. Mixing them out of convenience is the recipe for disaster.

7. Attacks on autonomous and agentic AI

This, in my view, is the most interesting (and most risky) frontier in 2026: autonomous agents. Systems that don't just respond, they act: read email, manage calendars, run code, buy things, send invoices, call APIs, open tickets. The productivity promise is huge; so is the attack surface.

What I see when I audit agent deployments in enterprises:

• Excessive default permissions: the agent has access to everything "so it doesn't fail", which multiplies damage if a prompt injection manages to redirect it.
• Opaque agent chains: agent A calls agent B which calls an external service. If one link is compromised, the rest trust without verifying.
• No "human in the loop" on critical actions: the agent sends money or deletes data without human confirmation, because "that's the point of automating it".
• Incomplete logs: the security team cannot reconstruct what the agent did, with which data, or why.

The defense pattern I recommend is what I call "agents with belt and suspenders":

• Strict least privilege, with short-lived tokens and per-task scopes.
• Mandatory human confirmation on any irreversible action (payments, deletions, external communications).
• Execution sandboxes for any generated code or invoked tool.
• Full traceability: every step of the agent, its prompt, its context and its output, signed and stored.
• Periodic review of actual permissions vs. needed ones. Inertia drifts toward over-privilege.

8. RAG abuse and information leakage

RAG (Retrieval Augmented Generation) is the most popular architecture for bringing AI into the enterprise without retraining models: the LLM consults your corporate knowledge base before answering. Done well, it's effective and affordable. Done badly, it's an open source of leaks.

The most common problems I see:

• Mis-mapped corpus permissions: the RAG indexes documents the consulting user shouldn't see, and the model ends up returning them.
• No identity filtering: the system doesn't apply the user's ACL when retrieving passages. Any employee can end up reading payroll or confidential clauses.
• Instruction injection in indexed documents (indirect prompt injection variant): a manipulated document tells the model what to answer to future queries on a given topic.
• Logs with sensitive data: queries and retrieved passages are stored in clear in logs accessible to broad teams.

What I apply in corporate RAG projects:

• Mandatory identity filtering: the retriever applies the user's ACL before passing anything to the model. No exceptions.
• Prior classification of the corpus: public, internal, confidential and restricted data flow through separate pipelines, with different models and endpoints if needed.
• Document sanitation before indexing (strip metadata, embedded instructions, hidden content).
• Encrypted logs with minimal retention, accessible only to a small group.
• Periodic leakage testing: red teams trying to extract restricted information through creative queries.

9. AI for defense: SOC, detection and response

If the whole conversation up to here has been about how AI helps the attacker, this section is the balance: AI is also, in 2026, the best defensive lever we have, especially in the SOC. I cover this in depth in my 2026 AI-driven SOC guide, but here's the useful summary.

Where I see the most impact:

• Alert triage: well-applied AI cuts noise by 60-80%, leaving the analyst the alerts that truly matter.
• Cross-source correlation: joining EDR, identity, network and SaaS logs in seconds instead of hours.
• Behavioral anomaly detection: users, devices and agents that drift from their pattern. For AI threats, this is vital.
• Analyst assistance: copilots that suggest queries, contextualize alerts and speed up response. Properly configured, they don't replace, they multiply.
• Response automation on standard runbooks: endpoint isolation, account lockout, token revocation, with human-in-the-loop for the critical steps.

The key point: defensive AI is not a product you buy and turn on. It's a program that needs quality data, real integration, prioritized use cases and a team that understands both security and ML.

10. Action plan: what to do this week, this quarter, this year

Here's the plan I recommend to teams asking me where to start, ordered by horizon and by effort/impact ratio.

This week

• Quick inventory of where AI is used in your organization (official and "shadow AI"). Without inventory, no management.
• Review the state of DMARC, SPF and DKIM. If you're not on p=reject, schedule the path to get there.
• 60-minute session with finance leadership to explain deepfakes and agree on a dual-channel protocol.
• Activate phishing-resistant MFA (FIDO2/passkeys) at least for admins and leadership.

This quarter

• Phishing simulation program with AI-generated templates, not 2020 ones.
• Acceptable AI use policy aligned with AI Act, NIS2 and GDPR.
• Permission review for corporate copilots and agents: apply least privilege.
• Incident response plan that includes AI scenarios: prompt injection, deepfake, RAG leakage.
• Start behavior-based detection capabilities in the SOC.

This year

• Zero Trust architecture as a baseline, with real segmentation and continuous verification. I cover this in my Zero Trust guide.
• Security program for your own AI systems: red teaming, poisoning tests, model governance.
• SOC maturity with integrated copilots and clear metrics on MTTR, noise and coverage.
• Integrated compliance NIS2 + DORA + AI Act + GDPR under a single control framework, not four silos.
• Culture: continuous training, not annual, with real cases and behavioral metrics.

If after reading this you feel your organization is further behind than you'd like, don't panic: most are. What matters is starting this week with the highest-impact items and building from there, honestly and without shortcuts.

Frequently asked questions on AI and cyberthreats

Does generative AI make attacks more dangerous?

Yes, especially in scale and personalization. AI does not invent many new threats, but it industrializes existing ones (phishing, fraud, malware) and opens vectors specific to AI systems (prompt injection, poisoning, agent abuse). The aggregate risk goes up.

What is prompt injection and why does it worry everyone in 2026?

It's the injection of malicious instructions into content an LLM will process (emails, documents, web pages). The model does not separate data from instructions, so it can execute what the attacker wrote inside apparently harmless content. It is the equivalent of XSS for AI-powered applications.

How do I protect against CEO deepfakes?

A combination of rotating code words in finance leadership, mandatory dual channel for critical orders, specific training with real cases, and, in larger companies, deepfake detection in videoconferencing tools. Training alone is not enough.

Is a corporate RAG safe by default?

No. If it does not apply the user's ACL when retrieving passages, indexes unclassified documents and does not sanitize content, it's a leakage source. RAG security depends on retriever design, not on the model.

Do I need to comply with the AI Act as an SMB?

It depends on the use case. Many SMB AI systems are limited or minimal risk, with light obligations (transparency, marking). But if you use AI in HR, scoring, biometrics or regulated sectors, the obligations grow significantly. Review it in detail.

How does AI rank against other cybersecurity investments?

Don't treat it as a silo. Well integrated, AI multiplies the impact of your SOC, identity management and incident response. Poorly integrated, it's expensive noise. Prioritize use cases with clear metrics.

How long does an average company take to be reasonably prepared?

With executive backing and a realistic budget, 12 to 18 months for a solid base (DMARC, strong MFA, mature EDR, AI policy, training, AI runbooks). NIS2, DORA and AI Act compliance overlap with many of these measures.