Secure AI deployment, LLM integration security, AI red teaming, and governance frameworks. We help you deploy AI systems that are resilient, compliant, and trustworthy.
AI Security Services
From red teaming and governance to shadow AI discovery and MLOps hardening—we cover the full AI attack surface.
Adversarial testing of AI and LLM systems. We simulate prompt injection attacks, jailbreaking attempts, data exfiltration, and tool abuse to identify weaknesses before attackers do. Findings map to OWASP Top 10 for LLMs and NIST AI RMF.
Align your AI program with ISO 42001, NIST AI RMF, and emerging regulations. We help you build policies for acceptable AI use, data governance, risk ownership, and audit readiness—integrated into your existing compliance and vCISO reporting.
Identify unsanctioned AI tools, plugins, and integrations across your organization. We map shadow AI usage, assess risk exposure, and help you bring unmanaged AI under governance without disrupting productivity.
Secure autonomous AI workflows end to end. We evaluate agent architectures, tool-calling patterns, privilege boundaries, and memory handling to ensure agentic systems operate within safe, auditable boundaries.
Secure your model pipelines from training through deployment. We assess data ingestion, model storage, CI/CD for ML, and inference endpoints—hardening the supply chain that powers your AI systems.
Threat Frameworks
We test and remediate against the most critical risks to large language model applications.
Direct and indirect prompt injection that manipulates LLM behavior, bypasses guardrails, or extracts sensitive data through crafted inputs.
Data leakage through model outputs—exposing PII, credentials, proprietary information, or training data through context windows and responses.
Compromised dependencies, model providers, datasets, or infrastructure that introduce backdoors, biases, or vulnerabilities into deployed AI systems.
Poisoned training, fine-tuning, or RAG corpora that manipulate LLM behavior—creating backdoors, bias, or enabling targeted manipulation of outputs.
Model outputs trusted or executed without validation or policy checks—enabling injection attacks, unauthorized tool invocation, or data corruption in downstream systems.
Agents granted too much autonomy or permission to act safely—allowing unchecked tool use, privilege escalation, or destructive actions without adequate human oversight.
Extraction of hidden prompts, policies, and tool schemas by attackers—revealing internal instructions and enabling targeted bypass and abuse of safeguards.
RAG stores and embeddings that become an attack and data leakage surface—enabling poisoned retrieval, cross-tenant data exposure, and amplified prompt injection.
Confident falsehoods, fabricated citations, and misleading outputs that create operational harm, enable fraud, or drive bad decisions without user awareness.
Runaway costs, latency, or capacity exhaustion via abuse or inadequate controls—denial-of-wallet and denial-of-service dynamics targeting AI infrastructure.
Securing autonomous AI agents against emerging attack vectors unique to agentic architectures.
Manipulating an agent's objectives through adversarial inputs, causing it to pursue unintended goals that serve the attacker's interests instead of the user's.
Exploiting an agent's access to external tools—APIs, databases, file systems—to perform unauthorized actions, escalate access, or exfiltrate data.
Agents inheriting or delegating credentials without proper scoping—creating attribution gaps, privilege escalation, and unauthorized actions beyond intended scope.
Malicious tools, MCP servers, agent cards, and registries in the agentic ecosystem that introduce compromised components into autonomous workflows.
Agent-generated code paths that bypass traditional security controls—enabling remote code execution through autonomous code generation and interpretation.
Persistent corruption of agent memory, embeddings, and shared context—influencing future decisions, creating backdoors, or corrupting ongoing workflows.
Weaknesses in agent-to-agent protocols, discovery, and validation—allowing message tampering, impersonation, or injection between cooperating agents.
Single faults propagating across agents and workflows—where one compromised or malfunctioning agent triggers system-wide failures through trust chains.
Anthropomorphism and authority bias weaponized against human oversight—manipulating users into trusting agent outputs or approving malicious actions.
Behavioral drift, collusion, and self-replication beyond initial compromise—agents operating outside their intended boundaries without detection or control.
Whether you're deploying your first LLM or scaling agentic workflows, we help you build AI systems that are secure, compliant, and trustworthy.