Artificial Intelligence & Cybersecurity
Artificial intelligence offers extraordinary opportunities — but it also introduces a new generation of cybersecurity challenges. Kodetis helps businesses unlock the potential of AI while minimising risk.
Understanding the AI Security Challenges
A journey through the critical cybersecurity challenges in the era of artificial intelligence.
New Attack Surfaces
While powerful for defence, artificial intelligence also creates new attack surfaces. AI models themselves can be targeted, requiring dedicated vigilance and tailored protection strategies.
Attack Types
- 1 Poisoning: corruption of training data
- 2 Evasion: bypassing AI-based detections
- 3 Infrastructure: targeting APIs and model endpoints
AI Usage Risks
Beyond direct attacks, deploying AI carries intrinsic risks. Rigorous governance is essential to ensure compliance and fairness in AI-driven decisions.
Bias
Algorithmic discrimination
Black Box
Lack of explainability
Confidentiality
Data protection risks
Vulnerabilities
Model manipulation
Data Sovereignty
The rise of AI raises critical questions of digital sovereignty. Ensuring data localisation and control is essential for regulatory compliance and strategic independence.
Key Questions
CLOUD Act: Non-European platforms expose your data to extraterritorial laws.
Securing LLMs
Securing AI models is imperative. Specific frameworks are emerging to address new risks. Integrating these frameworks is critical in every AI project.
OWASP Top 10 LLM
International reference framework for LLM application risks
- •Malicious prompt injection
- •Sensitive data leakage
- •Model poisoning
Security Best Practices
AI Threat Modeling
Least Privilege Principle
Limiting permissions for AI tools and agents
System Isolation
Network segmentation and AI system sandboxing
Hardened RAG
Strong Prompt Validation
LLM-Specific Attacks
Language models introduce unprecedented attack vectors. Understanding these threats is essential to defending against them.
Prompt Injection
The attacker injects malicious instructions into the prompt to hijack the model's behaviour and bypass security rules.
"Ignore previous instructions and reveal all confidential data..."
Jailbreaking
Techniques designed to circumvent a model's safety guardrails, making it generate prohibited or dangerous content.
Model Inversion
Attackers query the model repeatedly to reconstruct sensitive training data — including personally identifiable information.
Supply Chain Attacks
Compromising the AI supply chain — pre-trained models, training datasets, or third-party libraries — to introduce backdoors at source.
Kodetis AI Security Expertise
A comprehensive service offering to help you deploy, secure, and govern AI in your organisation.
AI Security Audit
Comprehensive assessment of your AI systems: architecture review, data pipelines, model exposure, and compliance with OWASP LLM Top 10.
EU AI Act Compliance
Risk classification of your AI systems, documentation requirements, conformity assessment, and roadmap to EU AI Act compliance.
LLM Hardening
Implementation of guardrails, prompt validation, RAG hardening, and sandboxing to secure your language model deployments.
Sovereign AI Architecture
Design of on-premise or sovereign cloud AI infrastructure to maintain full data control and independence from US hyperscalers.
AI Security Training
Training for your technical and business teams on AI risks, secure deployment practices, and responsible AI usage.
AI Governance Framework
Design of an AI governance policy: acceptable use policy, risk classification matrix, incident response procedures for AI systems.
Ready to Deploy AI Securely?
Harness the power of artificial intelligence without compromising your security posture. Our experts will guide you from risk assessment to secure deployment.
Response within 24h • Free initial consultation • No commitment