Enterprise-grade security hardening for AI agents and LLM-powered applications.
We help organisations reduce risk across Claude, Bedrock, and other agentic AI deployments by strengthening guardrails, tool permissions, MCP integration controls, identity, logging, rate limits, and production governance.
AI agents can read data, call tools, execute workflows, and interact with internal and external systems. Without robust controls, they create significant operational, security, and compliance risk.
Malicious inputs can hijack your agent's behaviour, bypassing instructions and executing unintended actions.
Without access controls, agents can leak sensitive data to external services, APIs, or malicious actors.
Unbounded API calls and model requests can drain your budget in hours without proper rate limits and cost caps.
Third-party skills and plugins can introduce supply-chain vulnerabilities if not properly vetted and sandboxed.
We offer fixed-scope baseline assessments, targeted hardening programmes, and ongoing advisory retainers. All engagements are scoped according to your risk profile, compliance requirements, and operational maturity.
One-time assessment and hardening engagement. Initial baseline reviews can typically be completed within 1–2 business days. Full engagements are scoped according to complexity.
Continuous monitoring and hardening as threats evolve. Flexible terms, no long-term contracts required.
Our approach reflects the practical controls enterprises need for safe and trustworthy agent deployments: keeping humans in control, making agent behaviour visible, limiting unintended actions, protecting sensitive data, and securing agent interactions with tools and external systems.
Approval gates, scoped permissions, and clear intervention points for high-impact or sensitive actions.
Better visibility into agent plans, tool usage, and decision paths so teams can review and redirect safely.
Controls that keep agents aligned with business intent and reduce the risk of overreach or unintended changes.
Data access controls, connector boundaries, and segregation of sensitive information across tasks and users.
Prompt injection resilience, tool-chain hardening, connector review, and monitoring for misuse or abuse.
Secure enterprise agents need more than model access. They need clear scope, deliberate tool design, observability, testing, and fallback paths that support reliable production operation.
Define what the agent should do, what it should not do, and where human escalation begins.
Use realistic scenarios, edge cases, and expected outcomes to validate behaviour before wider rollout.
Trace agent actions, tool calls, latency, cost, and error patterns from day one rather than after incidents occur.
Design clear tool boundaries, validate parameters, and reduce ambiguity across MCP servers, plugins, and internal integrations.
Plan for failure modes with retry controls, safe fallbacks, and clear escalation routes when confidence is low.
For regulated and high-stakes environments, NVIDIA NemoClaw adds policy-enforced guardrails, identity isolation, privacy routing, and secure local execution on top of OpenClaw. It enables organisations to run autonomous agents with stronger runtime controls, connector governance, and the option to keep sensitive workloads on-premises or in private cloud.
Important note on emerging risk: As enterprise AI agents become more capable and autonomous, robust security and governance become increasingly important. Anthropic’s work in this area is helping customers and partners prepare for the next generation of agentic systems through stronger emphasis on safety, oversight, and responsible deployment practices.
For organisations that prefer a fully managed service, Anthropic’s Claude Managed Agents (currently in public beta) offer a governed, enterprise-ready alternative with built-in safety, oversight, and operational controls.
Policy-based controls, human approval workflows, scoped permissions, and runtime isolation suitable for financial services, energy, and regulated industries.
Routes sensitive tasks to local high-performance models (e.g. Nemotron) while maintaining governed access to cloud frontier models. Supports data segregation and compliance requirements.
Designed for production workloads on workstations, private cloud, or data-center environments with automatic compute detection and one-command installation.
1-hour strategic call to understand your agent, use case, and security requirements.
Remote audit of your configuration, tools, credentials, and access controls.
Implement allowlists, approval gates, logging, rate limits, and cost caps.
Full documentation, team training, and validated security controls.
The same security rigour we apply at major financial institutions — tailored to AI agent and LLM deployments of any scale.
We will review your current AI agent configuration and provide an initial findings summary outlining key risks, control gaps, and recommended next steps.
Book an Initial Review Call →We will review your current architecture, existing controls, security priorities and determine the right scope for assessment or ongoing advisory support.
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