Artificial intelligence is reshaping cybersecurity from both sides of the battlefield. This guide covers the full landscape: securing AI systems, using AI for defence, the OWASP Top 10 for LLMs, the EU AI Act, and how platforms like KENSAI put AI-powered security scanning into practice.
AI security is a discipline with two distinct but interrelated dimensions: Security of AI — protecting AI systems, models, data pipelines, and inference endpoints from attacks; and AI for security — leveraging machine learning to detect, prevent, and respond to cyber threats more effectively.
Neither dimension exists in isolation. An AI-powered intrusion detection system that is itself vulnerable to adversarial manipulation creates a false sense of security. Conversely, the most hardened AI model provides no value if it cannot meaningfully improve threat detection or response.
For enterprises deploying AI in production — whether customer-facing chatbots, internal automation, or security tooling — understanding both dimensions is no longer optional. It is a business-critical requirement that intersects with compliance obligations under the EU AI Act, NIS2, DORA, and the DSGVO (GDPR).
Traditional cybersecurity protects deterministic software. You patch a known vulnerability, and it stays patched. AI systems introduce probabilistic behaviour. A model that correctly classifies 99.7% of inputs today may misclassify critical inputs tomorrow if an attacker subtly shifts the data distribution.
This fundamental difference means AI security requires new threat models, new testing methodologies, and new monitoring approaches that go beyond conventional vulnerability management.
Three converging forces make AI security urgent in 2026:
AI adoption has reached critical mass — 78% of enterprises now use AI in production. Attackers are using AI too — AI-generated phishing has 60% higher click-through rates. Regulators are paying attention — fines up to €35M or 7% of global turnover under the EU AI Act.
AI-generated phishing emails have a 60% higher click-through rate than manually crafted ones, according to IBM X-Force. Deepfake-enabled CEO fraud cost companies an estimated €2.1 billion globally in 2025. AI is not just a defensive tool — it is an offensive weapon.
The EU AI Act entered full enforcement in 2025. Combined with NIS2 and DORA, European organisations face overlapping compliance requirements. Non-compliance carries fines of up to €35 million or 7% of global turnover under the AI Act, and up to €10 million or 2% of turnover under NIS2.
Prompt injection is the #1 risk for LLM deployments. Attackers craft inputs that override system instructions, causing models to leak data, ignore guardrails, or perform unintended actions. There is no complete technical fix as of 2026.
Direct prompt injection embeds malicious instructions directly in user input. Indirect prompt injection hides instructions in data the model processes — web pages, documents, or emails. It can chain with tool use, causing an LLM with API access to call APIs with attacker-controlled parameters.
Adversarial ML attacks manipulate model behaviour by crafting inputs that exploit learned decision boundaries:
Data poisoning corrupts the training pipeline. An attacker who can influence even a small fraction of training data can implant backdoors:
Models trained on web-scraped data are vulnerable by default. Effects may not manifest until months after deployment. Detection requires statistical analysis that many organisations skip.
AI models represent significant R&D investment. Theft occurs through API-based extraction, side-channel attacks, supply chain compromise, and insider threats. A model that cost €5 million to train can be stolen and deployed by a competitor in hours.
Modern AI systems depend on pre-trained models from Hugging Face, datasets from public repositories, and open-source frameworks. Each dependency is an attack vector — malicious models that execute code during loading, poisoned datasets, and compromised libraries.
ML models trained on historical vulnerability data predict which code patterns are most likely to contain flaws. AI-assisted fuzzing discovers edge cases that traditional fuzzers miss. Modern scanners can analyse code, generate PoC exploits, prioritise by impact, and correlate against 332,000+ known CVEs.
AI augments human penetration testers by automating reconnaissance, suggesting attack paths, adapting in real time to defensive responses, and generating business-relevant reports. AI also generates highly convincing phishing content and deepfake impersonation for red team exercises.
AI-powered SIEM and XDR systems analyse billions of events to identify threats that rule-based systems miss — behavioural analytics, network traffic analysis, and cross-system log correlation.
Risk-based prioritisation — AI assesses exploitability, asset criticality, and threat intelligence. Predictive analysis — ML predicts which CVEs will be exploited before public exploits appear. Automated remediation — AI suggests and implements fixes in approved scenarios.
NLP models analyse email content, sender behaviour, links, and attachments to detect phishing with higher accuracy than signature-based systems, adapting to novel techniques without manual rule updates.
| # | Risk | Key Mitigation |
|---|---|---|
| LLM01 | Prompt Injection | Input validation, output filtering, privilege separation |
| LLM02 | Insecure Output Handling | Treat LLM output as untrusted; sanitise before rendering |
| LLM03 | Training Data Poisoning | Data provenance validation, quality checks |
| LLM04 | Model Denial of Service | Rate limiting, input size constraints |
| LLM05 | Supply Chain Vulnerabilities | Verify model integrity, audit dependencies, SBOM |
| LLM06 | Sensitive Information Disclosure | Differential privacy, output filtering |
| LLM07 | Insecure Plugin Design | Least privilege for all plugins |
| LLM08 | Excessive Agency | Limit actions, require confirmation for high-impact ops |
| LLM09 | Overreliance | Verification workflows, user education |
| LLM10 | Model Theft | Access controls, encryption, monitoring |
Unacceptable risk — Banned (social scoring, real-time biometric surveillance). High risk — Conformity assessments, documentation, human oversight. Limited risk — Transparency obligations. Minimal risk — No specific obligations.
| Requirement | AI Act | NIS2 |
|---|---|---|
| Risk assessment | AI-specific risk assessment | Cybersecurity risk assessment |
| Incident reporting | AI incidents to national authority | Cyber incidents within 24 hours |
| Supply chain security | AI supply chain due diligence | ICT supply chain security |
| Documentation | Technical documentation, data governance | Security policies, procedures |
| Human oversight | Mandatory for high-risk AI | Governance and accountability |
An organisation using AI for network monitoring (NIS2 scope) with a high-risk classification (AI Act) must satisfy both frameworks simultaneously. KENSAI helps navigate this overlap with compliance-aware security scanning.
The Digital Operational Resilience Act adds additional requirements for financial entities. AI systems in financial services must meet DORA's ICT risk management, testing, and third-party oversight on top of AI Act and NIS2.
AI systems processing personal data must comply with GDPR principles. Automated decision-making under Article 22 triggers the right to explanation and human review.
AI-powered security scanning for web apps, APIs, and infrastructure. Compliance mapping for NIS2, DSGVO, and DORA built in.
Start Free Scan →KENSAI's scanning engine uses machine learning to correlate vulnerabilities across your entire attack surface, prioritise by real-world risk using threat intelligence and exploitability scores, and draws on 332,000+ CVEs continuously updated and enriched with exploit intelligence.
Traditional pentesting happens annually. Threats evolve daily. KENSAI provides continuous automated scanning that catches new vulnerabilities as they emerge — in your infrastructure, newly deployed code, and third-party dependencies.
KENSAI maps findings to NIS2, DSGVO (GDPR), and DORA, showing exactly which regulatory requirements are affected by each vulnerability — transforming scanning from a technical exercise into a compliance management tool.
At €990–€2,490 per month, KENSAI makes enterprise-grade AI security scanning accessible to mid-market organisations that cannot afford six-figure annual contracts with traditional vendors.
AI security is the discipline of protecting AI systems from attacks and misuse, while also leveraging AI to improve cybersecurity defences. It encompasses securing models, training data, and inference pipelines, as well as using ML for threat detection, vulnerability management, and incident response.
Prompt injection, data poisoning, adversarial ML, model theft, and supply chain attacks. Each targets different aspects of the AI lifecycle — from training to inference.
AI detects threats faster and more accurately than rule-based systems, prioritises vulnerabilities by real-world risk, automates incident response, and adapts to new attack techniques without manual rule updates.
A framework identifying the 10 most critical security risks in LLM deployments: prompt injection, insecure output handling, training data poisoning, model DoS, supply chain vulnerabilities, sensitive info disclosure, insecure plugin design, excessive agency, overreliance, and model theft.
The EU AI Act regulates AI systems by risk level. NIS2 mandates cybersecurity risk management. When organisations use AI in critical infrastructure, both frameworks apply simultaneously, requiring coordinated compliance.
KENSAI uses ML to correlate vulnerabilities across network, web, API, and cloud surfaces, prioritise by real-world exploitability and business impact, and map results to NIS2, DSGVO, and DORA. Database covers 332,000+ CVEs. Free scan at kensai.app/scan/free.
No. Attackers use AI regardless of your own adoption. AI-generated phishing, deepfake fraud, and AI-assisted exploitation target all organisations. Most modern security tools also incorporate AI internally.
Find vulnerabilities before attackers do. AI-powered scanning for web apps, APIs, and infrastructure.
Start Free Scan →Security is not optional.
🗡️ The KENSAI Team