Center of Excellence Security - AI Runtime Defense Analysis

Shield Your AI in Real Time!

Detect, analyze, and neutralize threats targeting your AI systems during operation with our advanced runtime defense analysis.

Comprehensive AI Runtime Defense Solutions

At COE Security, we understand that the power of artificial intelligence comes with unique, real-time security challenges. Our AI Runtime Defense Analysis service is engineered to continuously monitor and protect your AI systems while they are in operation. By leveraging cutting-edge analytics and dynamic threat detection techniques, we ensure that your AI remains secure against sophisticated runtime attacks, ensuring integrity, reliability, and performance in your operational environment.

Our Approach

Our methodology integrates real-time monitoring with proactive defense measures to secure your AI systems throughout their lifecycle:

Mapping Your AI Environment: Identify critical AI components, execution frameworks, and data flows that require continuous protection during runtime.
Dynamic Threat Detection: Utilize advanced analytics and machine learning to monitor live system behavior and detect anomalies, such as adversarial attacks and unauthorized manipulations.
Real-Time Vulnerability Analysis: Continuously assess the operational integrity of your AI models to uncover emerging threats and potential exploits as they occur.
Immediate Incident Response: Implement automated response strategies that can neutralize threats in real time, minimizing potential disruptions.
Actionable Intelligence: Deliver detailed insights and prioritized recommendations for enhancing your runtime defense mechanisms, ensuring ongoing resilience.

Live Monitoring & Analytics

Anomaly & Threat Detection

Behavioral Analysis & Forensics

Continuous Improvement & Reporting

AI Runtime Defense Analysis Process

Our established methodology delivers comprehensive testing and actionable recommendations.

Assess

Analyze

Respond

Report

Optimize

Why Choose COE Security’s AI Runtime Defense Analysis?

Rely on our team of cybersecurity professionals specialized in securing AI systems.
Protect your AI systems with solutions tailored to their unique operational landscape and threat profile.
Protect your AI systems with solutions tailored to their unique operational landscape and threat profile.
Benefit from rapid incident response to ensure proactive resilience.
Gain actionable intelligence through detailed insights and strategic recommendations.

Empower your team with the tools to maintain robust AI security.
Ensure operational continuity while safeguarding AI systems during operation.
Focus on innovation while we ensure seamless, secure AI performance.
Defend against runtime threats to maintain continuous, secure AI operation.
Partner with COE Security for AI Runtime Defense Analysis and build a resilient, adaptive defense.

Five areas of AI Runtime Defense Analysis

Adversarial Attack Detection

Adversarial attack detection involves identifying and mitigating attempts to manipulate an AI system during its runtime through adversarial inputs. These attacks subtly alter input data in ways that cause the AI model to produce incorrect or malicious outputs without being easily detected. Runtime defense strategies focus on monitoring AI model behavior in real-time and detecting anomalies in the system’s decision-making process. Techniques like adversarial example detection, real-time input validation, and model ensemble methods can be employed to recognize and counteract adversarial attacks, ensuring that the AI system remains robust and reliable during deployment.

Data Integrity Monitoring

Data integrity monitoring focuses on ensuring that the data used in real-time AI decision-making remains accurate and untampered with during runtime. It involves monitoring incoming data streams for signs of corruption or manipulation, which could impact the AI’s predictions. Methods like hashing, cryptographic signatures, and checksums can be used to verify that the data hasn’t been altered. This is crucial for preventing issues like data poisoning attacks, where malicious actors inject false data into the system to skew results. Real-time detection of such integrity violations helps maintain the AI’s trustworthiness and ensures accurate decision-making.

Model Drift Detection

Model drift detection is the process of monitoring and analyzing an AI model’s performance over time to detect any unintended degradation or shifts in its behavior. This can occur due to changes in input data patterns or external factors that affect the model’s effectiveness. Runtime defense systems implement continuous monitoring of key performance metrics and compare current predictions with historical benchmarks. Early detection of model drift allows for timely retraining or adjustment, helping to ensure that the AI continues to function accurately and in line with its original objectives, despite evolving environments or datasets.

Access Control and Secure APIs

Access control and secure API management are essential for defending an AI system during runtime against unauthorized manipulation or data leakage. Real-time access control mechanisms ensure that only authorized users or services can interact with the AI model, minimizing the risk of malicious input or adversarial influence. Secure API management includes implementing proper authentication, authorization, and encryption methods to prevent unauthorized access to the model or its data. Runtime defenses like rate-limiting, logging, and monitoring API calls help detect abnormal access patterns, which may indicate an attack or misuse of the AI system.

Resource Consumption Anomaly Detection

Resource consumption anomaly detection focuses on identifying unusual patterns in the resource usage (CPU, memory, network bandwidth) of an AI system during runtime, which could be indicative of a cyberattack or malfunction. Attackers may attempt to overload or exhaust system resources to disrupt the AI’s operations, or to initiate denial-of-service attacks. Runtime defense strategies continuously monitor system performance, looking for abnormal spikes or drops in resource consumption. Detecting these anomalies can help quickly identify and mitigate potential security threats, ensuring that the AI system remains stable, responsive, and operational under all conditions.

Advanced Offensive Security Solutions

COE Security empowers your organization with on-demand expertise to uncover vulnerabilities, remediate risks, and strengthen your security posture. Our scalable approach enhances agility, enabling you to address current challenges and adapt to future demands without expanding your workforce.