BLACK ARROWS

AI systems expand attack surface in ways traditional applications never did. Large language models, agentic AI, autonomous systems, and AI-powered applications can call external tools, write to memory, read internal documents, invoke plugins, and influence downstream logic. These capabilities introduce threats that existing security controls do not address. We assess your AI security posture through Security Brutalism principles, identifying gaps in fundamentals that leave your systems vulnerable.

Why AI Demands Brutalist Security

Threats documented by the security industry are already visible in production environments. Prompt injection, model poisoning, retrieval abuse, system prompt disclosure, unintended tool invocation, insecure plugin integration, embedding attacks, excessive autonomy, denial of wallet, and malicious output handling all present real risks. Security theater provides no meaningful protection in this space.

Complex guardrails and alignment techniques cannot compensate for weak identity controls, poor logging, or unrestricted agent permissions. No amount of downstream filtering prevents damage when a compromised component has implicit trust. The fundamentals determine whether AI systems remain secure under pressure. Everything else follows from that foundation.

The Core Assessment Areas

We evaluate whether your AI foundation is built on disciplined identity controls, real isolation, trusted components, and reliable observability. The assessment looks at how access is granted, how components are separated, how actions are recorded, how inputs and outputs are handled, and how model and data provenance is maintained. The goal is to reveal where assumptions create risk, where boundaries are unclear, and where the system needs stronger containment or reduction to reach a secure and predictable state.

What We Examine

We start by mapping how your AI systems actually behave. That means understanding what models you run, what tools they can call, what data they touch, and which internal systems they influence. It also includes how users interact with them. This baseline shows where you are exposed and what protections matter most. From there, we look at whether you challenge your systems with real adversarial pressure or rely on hope. Prompt injection, system prompt exposure, retrieval manipulation, memory abuse, and tool misuse all require deliberate testing, not assumptions.

We then evaluate whether your systems stay within safe operational boundaries. Resource limits, plugin controls, and external request caps stop runaway loops and prevent attackers from draining compute or causing instability. We review how well you separate configuration from user content so no model can leak or alter system instructions, credentials, or internal logic. Finally, we examine how quickly you detect and contain compromise. Effective monitoring, anomaly detection, and isolation paths define how much damage an attacker can do before you shut them down.

The Report

You receive a clear assessment of your AI security fundamentals and an honest evaluation of your vulnerability to identified threats. We focus on what matters: the core capabilities that determine whether your AI systems remain secure under pressure. We do not generate exhaustive compliance checklists. We identify gaps in fundamentals that leave you exposed.

The assessment includes specific findings about where AI security fundamentals are missing or inadequate. We explain what each gap means for your actual risk. We identify AI investments that provide minimal security value. We show you where complexity obscures rather than improves protection.

The Roadmap Forward

More importantly, you receive an actionable roadmap grounded in Security Brutalism principles. The roadmap prioritizes establishing core AI security capabilities before adding sophisticated controls. It identifies unnecessary complexity that should be eliminated. It shows you how to transition from your current state to an AI security posture built on solid fundamentals.

The roadmap accounts for your AI systems, threat landscape, and resources. We recommend what will actually work in your environment with your constraints. The goal is AI systems that protect effectively within real-world constraints, not systems that look secure in threat models but remain vulnerable in production.

The Result

Organizations that complete our AI Security Gap Assessment gain honest understanding of their AI security state. They learn whether their fundamentals protect them or leave them vulnerable to attacks. They understand which AI security investments strengthen protection and which waste resources on sophisticated controls that rest on inadequate foundations.

This understanding enables better decisions about AI security priorities. You establish strict identity and access controls, implement immutable logging, enforce input and output validation, and maintain hard segmentation. You deploy monitoring that detects compromise. You eliminate unnecessary complexity that obscures threats. Your AI systems become resilient to adversarial pressure and enable rapid recovery when attacks occur.

Perhaps most importantly, you gain confidence that your AI systems remain defensible. You understand your attack surface. You know what can be compromised and what damage would result. You can respond quickly when threats materialize. Your AI becomes a tool that strengthens your organization rather than introducing uncontrollable risk.