Building the Most Secure Environment for AI

"Vibe Hacking" Data Extortion

Attackers used AI coding tools to automate reconnaissance, credential harvesting, and network penetration. Targeted 17+ organisations across healthcare, emergency services, and government. Ransom demands exceeded $500,000 per victim.

Source: Anthropic Threat Intelligence, August 2025

North Korean Remote Worker Fraud

AI eliminated the specialised training bottleneck previously requiring years of preparation. Fraudsters now pass technical interviews at Fortune 500 companies using AI for identity spoofing and professional communication in English.

Source: Anthropic Threat Intelligence, August 2025

No-Code Ransomware Development

Threat actors with minimal technical skills used AI to develop functional ransomware. Ransomware-as-a-Service packages distributed for $400–$1,200. Operators relied entirely on AI for encryption algorithms and evasion techniques.

Source: Anthropic Threat Intelligence, August 2025

Prompt Injection

Attackers manipulate LLM inputs to override instructions, extract sensitive data, or trigger unintended behaviours. Remains the number one critical vulnerability with no complete mitigation available.

Data Poisoning & Model Inversion

Training data integrity attacks that scale across deployments. Adversarial manipulation uses small, invisible changes to cause model misclassification in production.

Supply Chain Attacks

Backdoored models with statistical triggers are nearly invisible to static analysis. Open-source ecosystem compromises increased nearly 4× since 2020.

Agent-Based Risks

Autonomous agents executing tool chains without human oversight. Multi-agent coordination creating cascading failures. Self-preservation behaviours emerging in stress-tested models.

Vector & Embedding Weaknesses

With 53% of companies using RAG instead of fine-tuning, embedding vulnerabilities have become enterprise-scale risks including vector database poisoning and semantic similarity attacks.

Unbounded Consumption

Resource exhaustion through high-volume requests, token-stuffing attacks on inference APIs, and model extraction attacks through systematic querying.

Qwen Family (Alibaba)

Most downloaded open models (cumulative) by end of 2025. Transparent GitHub strategy with detailed documentation. Modular approach from 0.5B to 405B+ parameters.

Llama Family (Meta)

7B to 405B parameter range. Widely used for enterprise RAG pipelines with strong community support. Compatible with Ollama, vLLM, and all major inference engines.

DeepSeek R1

Cost-effective reasoning model validating that open weights deliver high-value reasoning. Popular for on-premise and air-gapped deployments.

IBM Granite 4

ISO 42001 certified for responsible AI development. 3B–8B parameter range optimised for edge deployment. Enterprise governance built in.

1. Govern

Risk management integrated into organisational strategy. Leadership accountability for AI risk.

2. Map

Identify potential AI risks and impacts. Contextualise AI systems within broader organisational risk.

3. Measure

Develop metrics and assess AI performance. Quantify risks with appropriate measurement tools.

4. Manage

Implement risk mitigation strategies. Prioritise and act on identified risks.

5. Monitor

Ongoing oversight and adjustment. Continuous evaluation of AI system performance and compliance.

Training Data Protection

Protects proprietary datasets during training. Enables collaborative AI training across organisations without data exposure.

Model Inference Security

Prevents model extraction attacks (reverse-engineering). Protects against data exfiltration during inference operations.

Data Processing Pipelines

Secure ETL operations. Confidential feature engineering and sensitive data transformation without exposure.

Federated Learning

Privacy-preserving aggregation of model updates from multiple organisations. Encrypted model combination across parties.

Cryptographic Artifact Signing

Sign models at every stage from pre-training checkpoints through production. Version control with attestation. Scan for manipulation before each deployment.

Verification Tools

Structure-aware pickle fuzzer for adversarial model files. Scanners for MCP, A2A, and agentic skill files. Model artifact integrity validation.

Dependency Management

Regular vulnerability scanning of third-party libraries. Patch management processes. Open-source licence compliance. SBOM for all AI components.

Vendor Evaluation

Model provenance verification. Transparency into training data. Third-party security audits. Incident response capabilities assessment.

Prompt Injection

Manipulate inputs to override instructions, extract data, trigger unintended behaviours. No complete mitigation available.

Sensitive Information Disclosure

Models trained on or returning sensitive data. Mitigation: data classification, PII detection, RAG controls.

Supply Chain

Compromised dependencies, models, and plugins. Mitigation: artifact signing, verification, SBOM.

Data & Model Poisoning

Training data integrity attacks. Mitigation: data validation, anomaly detection, continuous monitoring.

Improper Output Handling

Models generate executable code, SQL, or commands. Mitigation: output sanitisation, restricted execution.

Excessive Agency

Agents granted unprecedented autonomy without guardrails. Mitigation: action constraints, escalation protocols, human oversight.

System Prompt Leakage

Extraction of system instructions via injection variants. Focus on minimising sensitive information in prompts.

Vector & Embedding Weaknesses

53% use RAG - vector DB poisoning and semantic similarity attacks are enterprise-scale risks.

Misinformation & Hallucination

Confident false information in medical, financial, legal contexts. Mitigation: fact-checking, verification, education.

Unbounded Consumption

Resource exhaustion, token-stuffing attacks, model extraction through systematic querying. Mitigation: rate limiting, cost monitoring.

Startup Tier

< $50K/yr

• Cloud-only with open-source models
• Llama 7B, Gemma, Ollama
• Standard cloud security defaults
• GDPR, SOC2 from provider

Mid-Market Tier

$50K–$500K/yr

• Hybrid 80/20 on-premise/cloud
• SLMs on-premise; frontier via API
• Zero-trust hybrid architecture
• Sector-specific compliance (HIPAA, PCI-DSS)

Enterprise Tier

$500K+/yr

• Full hybrid with confidential computing
• Multiple model deployments
• Dedicated security team & SOC
• EU AI Act readiness programme

Secure Aggregation

Matured significantly in 2025–2026. Now implementable at scale without significant performance impact. Prevents visibility of individual gradients.

Differential Privacy

Primary empirically validated noise-addition mechanism. Provides formal privacy guarantees with a trade-off between privacy and model accuracy.

Secure Multi-Party Computation

Enables encrypted computation across parties. Theoretically aligned with data minimisation. Computational overhead limits current adoption.

Blockchain Integration

Added transparency and trust layer. Improved auditability with immutable record of contribution history. Emerging in 2026 deployments.

1. Adopt the Hybrid 80/20 Deployment Model

Deploy 80% of workloads on-premise using small language models (7B–13B parameters) for sensitive data processing. Reserve 20% for cloud-based frontier models handling complex reasoning tasks. This achieves 55–65% cost savings versus pure cloud whilst maintaining full data sovereignty.

2. Implement Zero-Trust AI Security from Day One

Apply the four trust layers - data, model supply chain, pipeline, and inference - across all AI deployments. Microsegmentation, continuous identity verification, and immutable audit logs are non-negotiable for regulated environments. This approach yields 76% fewer breaches.

3. Prepare for EU AI Act Compliance Now

With full applicability in August 2026 for high-risk systems, organisations must begin compliance programmes immediately. Document risk management systems, ensure data governance with quality assurance, implement automatic logging, and prepare detailed technical documentation.

4. Secure Your AI Supply Chain

Implement cryptographic signing of model artifacts at every stage. Use SBOM for all AI components. Conduct regular vulnerability scanning. Verify model provenance before deployment. Evaluate emerging verification tools for MCP and agentic frameworks.

5. Evaluate Confidential Computing for Sensitive Workloads

AMD SEV-SNP is the current preferred technology for enterprise AI, offering VM-level protection with minimal code changes and 10–15% overhead. Consider it mandatory for multi-tenant environments processing regulated data.

6. Address OWASP LLM Top 10 Vulnerabilities Systematically

Prioritise prompt injection defences, excessive agency controls (especially for agentic deployments), and vector/embedding security. Implement output filtering, rate limiting, and behavioural monitoring as baseline protections.

7. Build an AI-Specific Incident Response Plan

Traditional IR plans do not cover AI-specific scenarios such as model poisoning, prompt injection breaches, or agent-based cascading failures. Develop and rehearse AI-specific playbooks alongside dedicated red team exercises probing models for adversarial vulnerabilities.