As AI gets embedded everywhere, securing it becomes its own discipline. Gartner lists AI security platforms among the core 2026 strategic trends. This is not about securing systems with AI — it is about securing the AI itself.
The AI Attack Surface
AI systems introduce attack vectors that traditional security tools do not cover:
| Attack Type | Description | Impact |
|---|---|---|
| Prompt injection | Manipulating model inputs to bypass controls | Data leakage, unauthorized actions |
| Data poisoning | Corrupting training data | Model produces wrong outputs |
| Model extraction | Stealing model weights via API queries | IP theft |
| Membership inference | Determining if specific data was in training set | Privacy violation |
| Adversarial inputs | Crafted inputs that fool the model | Misclassification, safety bypass |
| Supply chain attacks | Compromised model weights or dependencies | Full system compromise |
What an AI Security Platform Does
An AI security platform provides:
Model Security
- Input validation and sanitization
- Output filtering for PII, secrets, and harmful content
- Prompt injection detection and blocking
- Rate limiting and abuse detection
Data Security
- Training data lineage and provenance tracking
- Differential privacy enforcement
- Data access controls and audit trails
- PII detection and redaction in training pipelines
Runtime Security
- Model behavior monitoring (drift, anomaly detection)
- Inference audit logging
- Guardrail enforcement (topic boundaries, safety filters)
- Real-time threat detection
Governance
- Model inventory and risk assessment
- Compliance reporting (EU AI Act, NIST AI RMF)
- Bias and fairness monitoring
- Explainability requirements
Implementation Architecture
# Example: AI gateway with security controls
apiVersion: v1
kind: ConfigMap
metadata:
name: ai-security-policy
data:
policy.yaml: |
input_validation:
max_tokens: 4096
block_patterns:
- "ignore previous instructions"
- "system prompt"
pii_detection: enabled
output_filtering:
redact_pii: true
block_harmful: true
max_output_tokens: 8192
rate_limiting:
requests_per_minute: 60
tokens_per_minute: 100000
audit:
log_all_requests: true
retention_days: 365The Vendor Landscape
| Platform | Focus | Strengths |
|---|---|---|
| Protect AI | ML supply chain | Model scanning, dependency analysis |
| Robust Intelligence | AI firewall | Real-time input/output validation |
| Lakera | LLM security | Prompt injection detection |
| Calypso AI | AI governance | Compliance, risk assessment |
| Arthur AI | Monitoring | Model performance, bias, drift |
My Recommendation
Do not bolt AI security on after deployment. Build it into your AI platform from day one. The minimum viable AI security stack is: input validation, output filtering, audit logging, and rate limiting. Everything else is important but secondary.
Book a consultation to secure your AI infrastructure.
