An agent's action surface is your attack surface.
A guardrail that only logs risk isn't a control. Crescent AI engineers the identity, least-privilege authorization, threat modeling, red teaming, and evidence mapped to OWASP LLM Top 10 and AISVS that turn "we added guardrails" into a security posture you can actually defend.
Securing the app around the model isn't the same as securing the model.
AI Security Engineering is the discipline of designing, testing, and operating controls that protect AI applications, agents, models, prompts, tools, memory, retrieval systems, and data flows from misuse, leakage, manipulation, and unsafe action. LLM and agent systems introduce attack surfaces standard application security doesn't cover: prompt injection, indirect prompt injection through retrieved content, tool misuse, excessive agency, and vector store weaknesses among them.
We threat-model the system against OWASP LLM Top 10 and AISVS, enforce least-privilege agent access, test prompt injection and tool misuse directly against the live system, protect secrets and memory, and produce evidence procurement teams and security questionnaires can actually use, not a guardrail installed and left untested.
What an AI security posture needs
Seven ways an AI system becomes an open door.
97% of organizations reporting AI-related security incidents lacked proper AI access controls. That's not a tooling gap, it's a design gap.
Agent permissions
Agents ship with broad tool access and no defined owner or risk tier. Nobody can answer which agents can send email, touch the CRM, or delete data, until one of them does something nobody approved.
Tool access
Read and write tools live side by side with no separation, and destructive actions run without approval. A tool combination that looks convenient in a demo is a privilege-escalation path in production.
Prompt injection
Direct injection tries to override system instructions through user input. Indirect injection hides instructions inside a web page, resume, or email the agent later reads, and treats the model's own retrieved content as an attacker's command channel.
Data exposure
System prompts, secrets, and tenant data leak through outputs, logs, and traces. A RAG system with no tenant boundary on retrieval lets one customer's documents answer another customer's question.
Model/API risks
Unverified models, datasets, plugins, and MCP servers enter the stack with no supply-chain check. A compromised or malicious tool description can misdirect an agent as effectively as a compromised model.
Runtime threats
Unbounded consumption, cost spikes, token exhaustion, and denial-of-service conditions go undetected because nobody set a rate limit, a turn budget, or a tool-call budget before shipping.
AI supply-chain risks
Models, fine-tunes, datasets, prompts, and dependencies get adopted without verification. Poisoned training or retrieval data can shift model behavior in ways that look like normal variance until tested for.
Built for teams whose agents touch real systems and real data.
We work with five kinds of teams, all past the point where an installed guardrail and no testing is a good enough answer.
AI-Native Startups
Pre-revenue to $20M ARR, shipping AI-native product
B2B SaaS
Adding AI to an existing product surface
Enterprise Engineering
Internal platform teams scaling AI org-wide
Digital-First Enterprises
200-3,000 employees integrating AI across a cloud-native product
Global Capability Centers
Captive engineering centers building internal AI tooling and developer platforms
Eight surfaces, engineered as one security posture.
Organized around what actually stops an incident, not which guardrail library installed it.
AI identity
Distinct authentication points for user, agent, and tool, so a session can't be hijacked or an identity spoofed by manipulating prompt text.
Authorization
Access control enforced by trusted runtime context, tenant, user, and session metadata resolved server-side, never taken from user-controlled text.
Least privilege
Every agent scoped to the minimum tools and data access its purpose requires, with a defined owner and risk tier before it reaches production.
Tool permissions
Read and write tools separated, destructive and external-facing tools gated behind approval by default, and every high-impact call logged with arguments, approver, and trace ID.
Threat modeling
A system-specific threat model mapped to OWASP LLM Top 10 and MITRE ATLAS, covering prompt injection, data poisoning, excessive agency, and supply-chain risk for your actual architecture.
Runtime protection
Input, retrieval, execution, and output guardrails that block or escalate rather than logging after the fact, with rate limits and consumption budgets to stop cost and denial-of-service attacks.
Red teaming
Direct and indirect prompt injection, jailbreak, tool-misuse, and RAG-poisoning attacks run against the live system, not a theoretical checklist.
AI security testing
A repeatable, versioned adversarial test suite covering every OWASP LLM Top 10 category, re-run on every prompt, model, tool, or guardrail change.
Eval science, benchmark design, and regression scoring sit with AI Reliability Engineering. Production monitoring and incident runbooks sit with AI Operations & Optimization.
Seven layers, reasoned through on every system.
Every AI system needs the same seven layers designed deliberately, whichever model or framework runs it.
Identity
Separate authentication for user, agent, and tool, so no single compromised layer can impersonate another, and every action traces back to a real actor.
Access
Authorization resolved from trusted runtime context, not from prompt text, with least-privilege scopes on every tool and data source.
Data
Tenant boundaries enforced before and after retrieval, sensitive fields masked or redacted, and memory writes scoped, explicit, and auditable.
Models
Model and provider selection verified against supply-chain risk, with fallback behavior tested so a provider outage doesn't silently downgrade security posture.
Agents
Every agent with a human owner, a business purpose, a risk tier, and a permission scope, reviewed before autonomy expands, not after an incident.
Tools
Read/write separation, approval gates on destructive actions, idempotency keys against retries, and tenant metadata injected by the runtime, not extracted from user input.
Runtime
Sandboxed code and browser execution, network and file restrictions, resource limits, and full audit logging across every model call and tool invocation.
Mapped to OWASP LLM Top 10, not a generic checklist.
We don't claim AI security competence until we can run a repeatable assessment against all ten categories, with evidence: test case, result, control, residual risk, and remediation owner.
Controls that block or escalate, not controls that only log.
A detection control that only records risk after the fact isn't a security control, it's a postmortem waiting to happen.
Least privilege comes first: limit what the agent can do before layering guardrails on top of it. Retrieved content is treated as untrusted data, never as an instruction channel, and every control is tested for bypass before we call it a control.
Tested against the live system, not a theoretical checklist.
A versioned, repeatable adversarial test suite, re-run on every prompt, model, tool, or guardrail change, not a one-time pentest.
Deep eval science, judge calibration, and regression benchmark design across your whole AI estate is AI Reliability Engineering.
The same nine-phase lifecycle, applied to security.
Discovery through knowledge transfer, with a defined gate at every step. For a security engagement, Validate covers red-team testing, guardrail bypass testing, and secrets scanning, well beyond a functional review.
What you receive.
Scoped to the engagement, from a readiness audit to a full red-team and hardening build.
Evidence is more than a scan result. It has exploits, business impact, and owners.
The standard we hold every finding to before it goes in front of a buyer, a procurement questionnaire, or a compliance reviewer.
The other seven pillars.
AI Security Engineering rarely stands alone. These are the disciplines it most often connects to.
Common questions.
Bring us the agent nobody's stress-tested yet.
Whether it's a prototype with broad tool access and no permission model, or a production agent that's never faced a prompt injection test, we'll walk through where it stands before we recommend anything.
No hype · No forced roadmap · Just a clear view of what the system needs next