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Cybersecurity training for 2026 should combine one foundation for everyone with role-specific practice for people who use, build, defend, or respond to AI systems. Keep the traditional red-team (attack) and blue-team (defense) skills, but add AI-output verification, AI-enabled phishing and social engineering, adversarial-machine-learning concepts, and exercises that reflect how quickly AI tools and threats change. ENISA and NIST materials establish the threat context and proposed priorities; they do not prove that a particular red/blue course, vendor, or exercise format reduces incidents.
What the 2026 threat evidence actually says
ENISA’s 2026 Threat Landscape, released on 22 September 2026, analyzes incidents and events observed from 1 January through 31 December 2025. It is an EU assessment, not a worldwide prevalence survey.
- Ransomware remained the most short-term impactful type of incident in ENISA’s summary.
- Seventy-three percent of targeted organisations in ENISA’s incident set were classified as essential or important entities under NIS2.
- Public administration accounted for 32% of cases, the most targeted sector in that dataset.
- Within the recorded public-administration events, 82% were ideology-driven distributed-denial-of-service (DDoS) attacks.
- ENISA expects emerging AI models to be used increasingly to support malicious operations.
Those percentages describe ENISA’s collection and period; they should not be presented as global rates or as a forecast for every organisation.
Why a simple red-team versus blue-team split is no longer enough
Red teams traditionally learn to find and exploit weaknesses. Blue teams learn to prevent, detect, contain, and recover from attacks. AI crosses that boundary:
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- An attacker can use an AI model to produce convincing spear-phishing messages, automate reconnaissance, or adapt social engineering.
- An employee or analyst can use an AI assistant whose answer is inaccurate, biased, manipulated, or impossible to audit from the output alone.
- A defender may need to secure a model, its data, its prompts, its tools, and the agents that can take actions on its behalf.
- An incident responder must distinguish an ordinary compromise from an AI-enabled attack or a failure caused by an organisation’s own AI system.
The useful division is therefore not two teams taking one course each. It is a shared baseline followed by tracks based on decision authority, AI use, systems defended, attack and failure modes, and whether the goal is awareness or operational response.
The shared foundation every employee needs
NIST’s Cybersecurity Framework Profile for Artificial Intelligence (initial preliminary draft, December 2025) says personnel should be trained to evaluate AI results and recognise AI-enabled spear phishing and social engineering. It also warns that AI output can be unpredictable, including hallucinations, bias, and manipulated responses.
Verify consequential AI-generated information
Teach staff to pause when an AI-produced answer could trigger a payment, disclosure, access change, safety decision, legal statement, or customer communication. Verification should use an approved source, a second person, or an independent system rather than confidence, fluent wording, or a citation supplied by the model.
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Recognise AI-assisted deception
Exercises should include messages that are unusually well written, personalised at scale, translated into a recipient’s language, or accompanied by synthetic audio or imagery. The expected action is the same as for other high-risk requests: use a known channel to verify, report the message, and avoid opening attachments or disclosing credentials.
Understand the limits of AI tools
Users need a clear rule for what may be entered into an assistant, how sensitive data is handled, and when human approval is mandatory. The lesson is not that AI is always wrong; it is that plausible output is not evidence of correctness.
Role-specific training tracks
The NIST preliminary draft separates general personnel outcomes from specialised training. A practical programme can map those outcomes as follows.
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| Audience | Primary objective | Core content | Practice format |
|---|---|---|---|
| All personnel | Make safe decisions when using or receiving AI-generated content | AI-enabled phishing and social engineering; output verification; data-handling rules; reporting routes | Short scenarios followed by a verify, report, or escalate decision |
| AI users and business owners | Use approved systems without creating new exposure | Permitted data; human-approval points; hallucination and bias checks; logging and escalation | Review a realistic work product and document independent checks |
| AI developers, system owners, and administrators | Protect AI systems and connect AI risks to ordinary security controls | Threat modelling; access and data controls; prompt and tool abuse; monitoring; safe failure and recovery | Design review using the organisation’s architecture and likely attack paths |
| Security analysts and incident responders | Detect, validate, contain, and recover from AI-enabled attacks or AI-system failures | Adversarial-ML terminology; evidence validation; model and data integrity; playbooks for phishing, prompt abuse, and compromised agents | Tabletop or technical simulation with handoffs between detection, response, legal, and business teams |
| Leaders and risk owners | Set decision authority and accept or reject AI-related risk | Critical-use cases; supplier and dependency risk; reporting thresholds; continuity and recovery priorities | Scenario decisions using incomplete information and documented assumptions |
Specialists should learn cybersecurity and AI-specific risks together, with mitigations tied to the systems and authority they actually control. A developer does not need the same lab as a finance approver, and an incident responder needs deeper validation skills than a general user.
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The NIST preliminary draft identifies realistic AI-created attack simulations and phishing scenarios as opportunities for practice, and calls for additional training for specialised incident-response personnel. Treat that as proposed guidance, not evidence that any particular simulation is effective.
Scenario 1: A convincing executive request
- Give participants a personalised email, chat message, or voice transcript requesting a payment, password reset, or sensitive file.
- Require them to identify verification steps that do not rely on the message’s contact details.
- Test whether they report the attempt and preserve the relevant evidence.
- For responders, add mailbox, identity, and endpoint indicators and require a scoped containment decision.
Scenario 2: An unreliable AI assistant
- Provide an assistant response containing a subtle hallucination, biased recommendation, or manipulated instruction.
- Ask the user to approve or use it in a consequential workflow.
- Score whether the user checks an authoritative source, records the review, and escalates uncertainty.
Scenario 3: A compromised AI-enabled workflow
- Assume an agent or integration has excessive permissions and receives an attacker-controlled instruction.
- Have the technical team identify affected identities, tools, data, and logs.
- Require business and security owners to decide whether to disable the workflow, rotate credentials, notify stakeholders, and restore service.
Keep each exercise tied to a real decision, observable evidence, and an approved escalation path. Do not claim that completion proves protection; use the results to find gaps and revise procedures.
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Use a common vocabulary for AI attacks
NIST’s adversarial machine-learning report provides a taxonomy and terminology intended to inform later standards and practice guides. It is not an evaluated training curriculum, but it can prevent red and blue teams from using different names for the same problem.
Organise lessons around four questions:
- Attack method: What was manipulated—data, a prompt, a model, an integration, an identity, or an output?
- Lifecycle stage: Did the action occur during data collection, training, deployment, operation, or response?
- Attacker goal: Was the aim to steal information, change a decision, evade detection, deny service, or gain broader access?
- Mitigation: Which control reduces the risk—access restriction, validation, monitoring, isolation, human approval, recovery, or another measure?
NIST’s AI security and resilience programme also describes planned control overlays for generative-AI assistants and large language models, predictive AI, single- and multi-agent systems, and AI developers. Those overlays are in development; they should not be described as completed standards.
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The December 2025 NIST preliminary draft states: “Personnel should be adequately trained to work with the results of AI systems, which are evolving rapidly and sometimes emit unpredictable output.” It also says: “This training will need to be frequently updated and readministered to match the pace of developments with AI technology.” These are draft recommendations, not final regulatory requirements, and the draft does not prescribe a universal interval, course length, or passing score.
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Organisations can still define review triggers that fit their risk:
- a new model, assistant, agent, plugin, or connected data source is approved;
- a phishing, fraud, data-leak, or model-integrity incident occurs;
- a major vendor or platform changes its capabilities or permissions;
- an exercise reveals a failure in verification, reporting, detection, or recovery; or
- an authoritative threat assessment changes the organisation’s priorities.
Record the trigger, the changed behaviour expected, the audience affected, and the date of the next review. This is more defensible than claiming that one annual awareness module keeps pace with rapidly changing systems.
Measure readiness without overstating evidence
The ENISA and NIST materials cited do not compare training interventions or demonstrate that a specific red/blue format measurably reduces incidents. Use internal measures as management signals, not as proof of causal effectiveness.
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- Can they report an AI-enabled message through the approved channel?
- Can analysts explain what evidence would confirm or refute an AI-related hypothesis?
- Can system owners name the permissions, data, logs, and recovery steps for an AI workflow?
- After an exercise, were procedures, controls, or ownership changed and retested?
Track completion separately from demonstrated behaviour. A high completion rate does not show that people will make the right decision under pressure.
A practical rollout plan
First 30 days: establish the baseline
- Inventory approved AI tools, high-impact use cases, connected data, and responsible owners.
- Set the minimum rules for sensitive data, human approval, verification, and reporting.
- Give all personnel a short scenario-based foundation module.
Days 31–60: add specialist practice
- Run separate workshops for AI owners, developers, defenders, responders, and leaders.
- Map likely attack and failure modes using a shared adversarial-ML vocabulary.
- Update incident, continuity, and supplier playbooks for AI-enabled events.
Days 61–90: exercise and adjust
- Conduct at least one cross-functional scenario involving an AI-generated deception or compromised workflow.
- Capture decisions, evidence gaps, escalation delays, and recovery dependencies.
- Assign corrective actions and define the review triggers that will prompt the next update.
This rollout is an implementation choice, not a schedule mandated by NIST or ENISA. Scale it to the organisation’s exposure, regulatory duties, and available response capability.
The bottom line for 2026 programmes
Keep red-team creativity and blue-team operational discipline, but make AI part of both. Give everyone the ability to question and verify AI-generated content and recognise AI-assisted deception. Give specialists deeper training matched to the systems and decisions they control. Use NIST’s draft recommendations and adversarial-ML taxonomy as guidance, label them accurately as draft or foundational material, and refresh the programme whenever the technology, threat, or a failed exercise changes.
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