Case Studies

Read how Integra helped Data Inflexion strengthen their AI security posture by implementing a robust AWS-native security framework

Data Inflexion Technologies Limited (Data Inflexion) is a Dubai-based AI developer  focused on delivering AI solutions for businesses across multiple tech stacks.

Their key offerings include:

  • Custom AI model development: Building bespoke AI models that integrate smoothly with web platforms and enterprise data pipelines.

  • AI-driven business tools: Delivering dynamic dashboards, decision-support systems, and intelligent user experiences powered by AI.

  • Domain‑specific AI applications: Notably, they’ve developed PrptSLM, a small language model tailored for the Middle East real estate sector—trained on 3.7 billion parameters covering property listings, regulations, market data, etc.

  • AWS-native deployment: Data Inflexion deploys scalable, secure, and cloud-native AI infrastructure and services

In essence, Data Inflexion helps enterprises integrate and operationalize AI—whether through custom models, insightful analytics, or vertical‑specific solutions—to drive smarter, data‑enabled decision-making.

The Challenge

Data Inflexion faced several challenges in securing their AI applications, models, and APIs deployed on Amazon Bedrock and SageMaker. The dynamic nature of model deployment and inference exposed them to risks such as API overuse, prompt injection, and model manipulation. Ensuring secure access control across multiple services, preventing data leakage from training and inference pipelines, and maintaining visibility into model behavior posed additional complexity. Misconfigurations in IAM roles, network access, and storage permissions would have threatened the confidentiality and integrity of sensitive data. Moreover, volumetric threats and bot traffic targeting inference endpoints required advanced protection mechanisms. 

The Solution

We helped Data Inflexion to implement a comprehensive security strategy to safeguard their AI models and applications deployed on Amazon Bedrock, addressing key risks such as API overuse, misconfiguration, data leakage, and exposure to volumetric threats. The mitigation approach included:
 

API Overuse & Abuse Protection:

  • Amazon API Gateway with throttling and usage plans was configured to limit the rate and volume of API calls, ensuring fair use and protecting against abuse.
  • AWS WAF was deployed with custom rules to detect and block abnormal traffic patterns and bot activity targeting inference endpoints.

Misconfiguration Prevention:

  • AWS Config continuously monitored Bedrock integrations, IAM roles, and network settings for policy deviations or insecure configurations.
  • AWS Security Hub aggregated alerts across services to ensure prompt detection and resolution of misconfigurations.

Data Leakage Mitigation:

  • AWS Macie was used to scan and classify training and inference data to identify and protect sensitive information (e.g., PII, financial data).
  • Encryption was enforced using AWS KMS for data at rest and in transit across all Bedrock and S3 interactions.

Protection Against Volumetric Attacks:

  • AWS Shield Standard provided always-on DDoS protection, while integration with Amazon CloudFront reduced surface exposure and latency.
  • AWS WAF rate-based rules and geo-blocking helped detect and mitigate volumetric spikes from known bad actors or anomalous regions.

Model & Prompt-Level Observability with Langfuse:

  • Langfuse was integrated to track prompt performance, user interactions, and model behavior. It enabled:

    • Real-time visibility into how models respond to edge cases and malformed inputs.

    • Detection of injection attempts or data exfiltration via prompt manipulation.

    • Feedback-driven tuning to prevent model misuse and reduce hallucination.

This layered security posture ensured that Data Inflexion’s Bedrock-powered AI applications remained resilient, compliant, and secure, enabling safe and scalable innovation for their customers.

The Result

By implementing a comprehensive security framework across their AI workloads on Amazon Bedrock and SageMaker, Data Inflexion significantly enhanced the resilience, reliability, and trustworthiness of their applications and models. The integration of AWS-native tools—such as WAF, Shield, API Gateway, Macie, and Config—ensured that threats like API abuse, DDoS attacks, data leakage, and misconfigurations were proactively mitigated. Role-based access control, encryption at rest and in transit, and continuous configuration monitoring helped maintain strict governance and compliance, particularly for AI models handling sensitive or regulated data.

Furthermore, the use of Langfuse enabled deep observability into model behavior, including prompt-level tracking, user interaction logging, and anomaly detection, empowering Data Inflexion to fine-tune model responses, prevent misuse, and uphold responsible AI practices. With volumetric threat defenses in place and model-layer observability, the company now benefits from lower attack surfaces, fewer operational disruptions, and improved user trust.

As a result, Data Inflexion is able to scale its AI deployments confidently, ensuring high availability and security for its enterprise customers while accelerating innovation across vertical-specific AI use cases. Their secured AI infrastructure has become a strategic differentiator, enabling them to meet the demands of mission-critical environments with confidence.