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AI Security, Guardrails & Defensive Alignment

Deploy LLMs and AI agents with confidence. We assess, attack and harden AI systems - then build the guardrails and governance that keep them safe in production.

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Overview

Traditional application security tools were not built for systems that generate novel outputs from a prompt. Cystem X specializes in the security problems that are unique to AI: prompt injection, data leakage through model outputs, unsafe tool use by autonomous agents, and the supply-chain risk introduced by third-party models and datasets. We assess systems against the OWASP Top 10 for LLM Applications and the MITRE ATLAS framework, then red-team your actual deployment the way an adversary would, including attempts to manipulate agents into taking unauthorized actions.

Analysts reviewing AI system security alerts and guardrail dashboards
4AI security domains

From there we design and implement guardrails and defensive alignment measures - input filtering, output validation, tool sandboxing and monitoring - calibrated to the risk level of each use case rather than applied as a blanket policy that blocks legitimate use. For organizations formalizing AI governance, we help build the policies, risk registers and documentation aligned with NIST AI RMF and ISO/IEC 42001, including EU AI Act readiness where relevant, so your security and legal teams can sign off on AI with evidence instead of assurance alone.

LLM & agent red teaming

Guardrails & defensive alignment

AI governance & compliance

Model supply-chain security

Capabilities

Core capability areas we deliver within this pillar.

AI security assessment (MITRE ATLAS; OWASP Top 10 for LLM Applications)
LLM & agent red teaming
AI guardrails & defensive alignment
AI governance & compliance (NIST AI RMF, ISO/IEC 42001, EU AI Act readiness)
Model supply-chain security

How we deliver

A consistent four-stage delivery path, from first assessment to ongoing operation.

  1. Assess

    We map your AI attack surface against OWASP LLM Top 10 and MITRE ATLAS, including every agent, tool and data source the model can reach.

  2. Design

    We design layered guardrails - filtering, alignment, output validation and sandboxing - calibrated to each use case's actual risk.

  3. Build

    Guardrails and monitoring are implemented and validated through structured red-team exercises before launch.

  4. Operate

    We track attack-success rates and guardrail performance in production and update defenses as new techniques emerge.

Outcomes

What changes for your organization once this pillar is in place.

Documented risk register

Every identified AI risk is tracked with an owner and a remediation status, not left as a verbal finding.

Attack-success-rate reduction

Guardrail effectiveness is measured against repeatable red-team exercises, not assumed.

Approved guardrail policies

Guardrail logic is documented and signed off by security and product stakeholders before it ships.

Governance evidence

Policies, risk assessments and testing records give legal and compliance teams something concrete to review.

Technology

A representative slice of the platforms and tools we use to deliver this pillar.

NVIDIA NeMo Guardrails Garak Promptfoo Lakera Azure AI Content Safety LangChain Python MITRE ATLAS

Frameworks & standards alignment

Our delivery practices are aligned with these recognized frameworks and standards.

NIST CSF 2.0 ISO/IEC 27001:2022 CIS Controls v8 OWASP MITRE ATT&CK/ATLAS ISO/IEC 42001 NIST AI RMF

Engagement models

Choose the model that matches where you are today, with no fixed pricing until scope is agreed.

Related industries & insights

Where this pillar shows up most, and further reading from our engineering team.

Industry

Banking & Financial Services

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Healthcare & Life Sciences

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Education & Research

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Article

The OWASP Top 10 for LLM Applications (2025): an enterprise defense playbook

A practical walkthrough of each risk, how it fails in production, and how to detect and mitigate it.

Read article

Frequently asked questions

What is the difference between AI security and traditional application security?
AI systems introduce new failure modes like prompt injection, data leakage through generated text, and unsafe autonomous actions by agents. Our assessments cover these alongside standard application security.
Do you test AI agents, not just chatbots?
Yes. Agent red teaming specifically targets tool use, multi-step reasoning and the ways an agent could be manipulated into taking an unauthorized action.
Will guardrails make our AI application unusable or overly restrictive?
Guardrails are calibrated to the risk of each use case. The goal is to block genuinely unsafe behavior while preserving legitimate functionality, validated through testing rather than guesswork.
Can you help us prepare for the EU AI Act or similar AI regulation?
Yes. Our governance work aligns with NIST AI RMF and ISO/IEC 42001 and can be scoped toward specific regulatory readiness, including EU AI Act considerations.
How often should AI red teaming be repeated?
At minimum before major model, prompt or tool changes, and on a recurring schedule since new attack techniques against LLMs continue to emerge.
Is AI security the same engagement as our general cybersecurity program?
They are complementary but distinct. Our cybersecurity pillar covers networks, clouds and applications broadly, while this pillar focuses specifically on the failure modes unique to models and agents. Many clients run both together.

Let’s engineer what’s next.

Tell us about your initiative - a solution architect will respond within one business day.

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