AI Security Framework

Threat Landscape

AI-Specific Threats Demand AI-Specific Defenses

Prompt Injection

Malicious instructions embedded in external content that hijack agent behavior. Indirect prompt injection through documents, websites, and APIs is particularly dangerous for autonomous agents with tool access.

Data Exfiltration

Agents with access to sensitive data stores can be manipulated into leaking confidential information through carefully crafted prompts or multi-turn attack sequences.

Model Poisoning

Adversarial manipulation of training data or fine-tuning datasets that embeds backdoors or systematic biases into deployed models — creating vulnerabilities that are nearly invisible to standard testing.

Insecure Tool Use

Agents with overprivileged access to APIs, databases, or file systems can be weaponized to execute unintended actions — from mass data deletion to unauthorized system modifications.

Lack of Observability

Without full audit trails for every agent action and tool call, detecting compromised behavior is nearly impossible. Attackers can operate undetected for weeks in undermonitored AI systems.

Identity & Authorization Gaps

AI agents acting on behalf of users without proper authorization scoping. Service-to-service AI calls without cryptographic identity verification. Privilege escalation via chained agent calls.

Our Framework

6-Domain AI Security Model

A comprehensive, defense-in-depth approach that protects every layer of your AI system.

Domain 1: Identity

Agent identity management, OAuth/OIDC for AI service accounts, fine-grained authorization scopes, and cryptographic signing of agent actions. Zero-trust architecture for AI workloads.

Domain 2: Data

Data classification tagging in RAG pipelines, column-level access controls, PII detection and redaction in prompts and outputs, data lineage tracking, and retention policies for training data.

Domain 3: Model

Model provenance verification, supply chain security for fine-tuning datasets, adversarial robustness testing, red-teaming, and model registry governance with signing and approval workflows.

Domain 4: Inference

Prompt injection detection (both direct and indirect), output content filtering, rate limiting and anomaly detection on inference patterns, and guardrails for agentic tool use authorization.

Domain 5: Observability

Complete audit trails for every agent action, tool call, and decision. Behavioral anomaly detection using ML-based baselines. Integration with SIEM systems (Splunk, Datadog, Azure Sentinel).

Domain 6: Compliance

Control mapping to SOC 2 Type II, HIPAA, PCI-DSS, FedRAMP, and GDPR/CCPA. Pre-built compliance reporting dashboards. Evidence collection automation for audit readiness.

Regulatory Readiness

Compliance Built In, Not Bolted On

Framework Coverage

SOC 2 Type II HIPAA PCI-DSS v4 FedRAMP Moderate GDPR / CCPA ISO 27001 NIST AI RMF EU AI Act

Cloud Security Integrations

AWS GuardDuty + Security Hub

Threat detection and centralized findings for AI workloads on AWS.

GCP Security Command Center

Unified security posture management for Vertex AI and GKE deployments.

Azure Defender for AI

Microsoft's native threat protection layer for Azure OpenAI workloads.