AI SECURITY
What is AI Security? Protect your AI against emerging threats.
AI Security encompasses all measures, processes and technologies that organisations deploy to protect their AI systems against attacks, misuse and unauthorised access. From prompt injection to model theft — the threat landscape for AI requires an entirely new security approach.
Six categories of AI threats
The attack surface of AI systems differs fundamentally from traditional software. Understanding these threats is the first step towards effective defence.
Prompt Injection
Attackers manipulate LLM instructions through crafted input to bypass security measures, exfiltrate data or execute unauthorised actions.
Data Poisoning
Malicious manipulation of training data to subtly influence the behaviour of AI models, with potentially far-reaching consequences for decision-making.
Model Theft & Extraction
Attackers steal or reconstruct proprietary AI models through systematic queries, resulting in the loss of intellectual property and competitive advantage.
Adversarial Attacks
Subtle manipulations of input — invisible to humans — that mislead AI models into making incorrect predictions, classifications or decisions.
Privacy & Data Leakage
AI models can unintentionally reveal sensitive information from training data, from personal details to trade secrets and intellectual property.
Supply Chain Attacks
Vulnerabilities in third-party models, datasets, libraries and APIs that are considered trustworthy but may have been compromised.
Defense in Depth Model
Multiple concentric security layers protect your AI systems from the outside in towards the core.
Six steps to robust AI Security
A pragmatic roadmap to systematically secure your AI systems.
Threat Assessment
Map the threat landscape: inventory all AI systems, identify attack surfaces and classify risks per system and use case.
Security Architecture
Design a defense-in-depth architecture with multiple security layers: perimeter, application, model and data security controls.
Implement Controls
Implement technical controls: input validation, output filtering, guardrails, sandboxing, encryption and role-based access control.
Monitoring & Detection
Set up 24/7 monitoring: anomaly detection, prompt injection detection, data leakage prevention and real-time alerting on suspicious behaviour.
Incident Response
Develop an AI-specific incident response plan: escalation paths, containment procedures, forensic analysis and communication protocols.
Continuous Hardening
AI Security is an ongoing process. Conduct regular red team exercises, update controls for new threats and improve based on incidents.
All about AI Security
AI Security is the discipline focused on protecting AI systems against attacks, misuse and vulnerabilities. It includes technical measures such as input validation, output filtering, model hardening and monitoring, supplemented by policies and processes specific to AI threats.
Prompt injection is an attack technique where malicious actors manipulate an LLM's instructions through crafted input. It is extremely dangerous because it can lead to data exfiltration, execution of unauthorised actions and the complete bypassing of security measures. Research shows that over 90% of LLM systems are vulnerable to some form of injection.
Protection against data poisoning requires strict control over training data: validation of data sources, anomaly detection in datasets, version control of training data and regular audits on data integrity. Additionally, use techniques such as data sanitisation and robust training methods.
Traditional cybersecurity protects systems against known attack vectors such as malware, phishing and network intrusions. AI Security adds protection against AI-specific threats: prompt injection, adversarial attacks, model theft, data poisoning and the unique risks of probabilistic systems. Both are complementary and necessary.
Implement output filtering with PII detection, limit the knowledge base to strictly necessary data, use role-based access control for different user groups, systematically monitor outputs for data leakage patterns and consider differential privacy techniques in your training pipeline.
Adversarial attacks are subtle manipulations of input that cause an AI model to make incorrect predictions or decisions. Think of invisible perturbations in images that mislead a classification model, or carefully crafted text that reverses sentiment analysis. These attacks are particularly insidious because they are invisible to humans.
Yes. The EU AI Act requires adequate technical security measures for AI systems, particularly for high-risk applications. Additionally, GDPR, the NIS2 directive and sector-specific regulations (such as DORA for financial institutions) impose further security requirements on systems that process personal data.
Use a combination of red teaming (human experts attacking the system), automated adversarial testing, prompt injection fuzzing, model robustness testing and AI-specific penetration tests. Combine this with continuous monitoring in production and regular security assessments by external specialists.
The investment depends on the complexity and number of AI systems. A security assessment starts around €12,000. Full implementation is a larger investment, but far outweighs the average damage of €4.5 million from an AI-related data breach and potential fines under the EU AI Act.
AI Security is an essential component of AI Governance. Integrate security requirements into your governance framework, add security assessments to every phase of the AI lifecycle, ensure security incidents are part of your governance reporting and align security controls with the risk classification from your governance policy.
Need help securing your AI systems?
W69 guides organisations in designing and implementing robust AI Security that neutralises threats and ensures compliance.