Protect Your AI Ecosystem with Specialized Adversarial Security Assurance
As companies build LLMs and generative AI into everyday workflows, they are also opening the door to a new category of risk. Threats like prompt injection, model poisoning, and data leakage do not look like traditional vulnerabilities, and most standard security tools simply aren’t built to catch them.
ACL Digital’s AI security team sits at the intersection of data science and cybersecurity, giving us a rare vantage point most security firms do not have. We stress-test your entire AI ecosystem the way real attackers would, probing for weaknesses, simulating adversarial attacks, and benchmarking everything against the OWASP Top 10 for LLMs, so your models are as secure as they are intelligent.
Validate
Security of Training Pipelines
Fortify
LLM & GenAI Model Resilience
Neutralize
Sophisticated Adversarial Attacks
Standardize
AI Safety & Compliance Frameworks
Our AI Security Testing Capabilities
Prompt Injection Testing
Prompt injection remains one of the most significant threats to modern AI applications. We evaluate how effectively models resist malicious prompts, hidden instructions, and attempts to manipulate intended behavior, helping organizations strengthen model reliability and safeguard business logic.
Data and Model Poisoning
Training data integrity directly shapes AI performance. We assess the impact of malicious or biased data introduced during training or fine-tuning, detect abnormal model behavior signaling tampering, and test how poisoned prompts can manipulate outputs in real time.
Sensitive Information Disclosure
AI systems must protect confidential information from unintended exposure. We assess whether models reveal hidden system prompts, sensitive data, configuration details, or other information that should remain protected.
Improper Output Handling
Ungoverned model responses can introduce risk. We evaluate filtering mechanisms and response-handling processes to help prevent harmful outputs, protect users, and reduce unintended actions within connected applications.
Supply Chain Vulnerabilities
Modern AI solutions rely on multiple libraries, plugins, frameworks, and model dependencies. We assess the components to identify vulnerabilities, exposed configurations, and security weaknesses that could affect the overall AI environment.
Excessive Agency
AI systems should operate within clearly defined boundaries. Our assessments evaluate whether models have unnecessary control over workflows, external tools, or APIs and identify opportunities to strengthen governance and access controls.
System Prompt Leakage
System prompts often contain critical business instructions and operational logic. We use adversarial techniques to test whether attackers can extract hidden prompts, API keys, configurations, or internal workflows, and we recommend ways to strengthen their protection.
Misinformation and Output Integrity
AI-generated misinformation can damage customer trust and business outcomes. We test how models respond to attempts to generate inaccurate, biased, or misleading content, including efforts to exploit AI trust or influence decisions through fabricated information.
Vector & Embedding Weaknesses
Retrieval-Augmented Generation (RAG) systems depend on secure vector databases. We evaluate embedding storage, retrieval processes, and access controls to identify weaknesses that could expose sensitive information or influence AI responses.
Unbounded Consumption
Uncontrolled AI usage can affect application performance and operational costs. We assess how AI systems respond to oversized, recursive, or repeated prompts that could increase latency, consume excessive resources, or disrupt service availability.
Accelerators/Frameworks
Why Choose ACL Digital for AI Model and Application Security?
Specialized Adversarial Expertise
AI introduces security challenges that extend beyond traditional applications. Our specialists combine cybersecurity knowledge with AI expertise to evaluate risks unique to LLMs, Generative AI, and intelligent applications.
End-to-End Pipeline Integrity
From training data and model development to APIs, vector databases, applications, and supporting infrastructure, we assess every component that contributes to the security of your AI ecosystem.
Framework-Driven Assurance
Our testing methodology aligns with the OWASP Top 10 for LLMs, providing a structured approach to identifying vulnerabilities, prioritizing risks, and strengthening AI security.
Client Impact
SIEM and Security Automation Orchestration for an Investment Bank
The client is a prominent French multinational investment bank faced challenges managing a high volume of security alerts and incident responses. ACL Digital partnered with them to enhance their security posture, streamline incident management, and ensure compliance.
The Challenges
- Large volume of security alerts hindered prompt addressing of critical threats.
- False positives led to critical issues being overlooked or unaddressed timely.
- Lengthy processes and manual incident management caused delays in incident resolution.
The Outcomes
- 70% reduction in response time was achieved through the automation of incident response and workflow management, allowing quicker threat mitigation.
- 50% fewer escalations to Tier 2/3 were realized due to automation and integrated threat intelligence, empowering the initial response team to handle incidents more effectively.
Technical Risk Assessment for a $100mn US-based Software Service Provider
A US-based multinational software service provider, specializing in commodity management platforms, experienced a critical security breach on a Linux machine hosting source code. ACL Digital partnered with them to conduct a comprehensive technical risk assessment across their on-premise and public cloud infrastructure assets.
The Challenges
- Applications hosted in a SaaS-based model on AWS cloud, lacked effective monitoring for security incidents
- Vulnerability of web-facing applications due to lack of a robust security posture
- Limited visibility into critical security incidents
The Outcomes
- 100% reduction in critical vulnerabilities, significantly enhancing defense against potential exploits
- 50% improvement in overall security posture with stronger threat detection, response, and mitigation capabilities.
Transformed Privileged Access Security and Implemented CyberArk Solution for $6bn Telecommunications Provider
A $6B telecommunications company in Belgium partnered with ACL Digital to design and deploy a tailored Privileged Access Security (PAS) solution and Identity Access Management system, providing secure, automated monitoring, control, and protection of privileged access to reduce security risks and bolster compliance.
The Challenges
- Privileged accounts have elevated permissions to access and modify sensitive systems and data.
- If compromised, they can gain unauthorized access to IT infrastructure and confidential information.
- Breaching privileged accounts can expose customer data and intellectual property.
- Compromised accounts can disrupt operations, disable security, and cause financial loss.
The Outcomes
- 70% reduction in manual, error-prone administrative processes, saving time and resources
- 60% improvement in regulatory compliance through the strengthened security of privileged access
