Cequence Security protects applications and data in the agentic era. With over a decade of experience in bot defense and API security, it delivers immediate value in securing AI workflows, trusted by top organizations to safeguard billions of API interactions and user accounts.
Cequence Security provides a unified platform combining AI Gateway, Bot Management, and API Security to ensure comprehensive application protection. AI Gateway secures agentic AI interactions, enabling innovation with AI. Bot Management prevents automated attacks without additional agents or SDKs. API Security manages API posture, helping uncover risks, ensuring compliance, and identifying vulnerabilities. The Unified Application Protection platform integrates these services, providing seamless discovery, compliance, and protection against cyber threats.
What are the key features of Cequence Security?
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AI Gateway: Secures agentic AI interactions with features like rate-limiting and least privilege access.
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Bot Management: Comprehensive solution against automated attacks using network-based, agentless detection.
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API Security: Monitors and tests APIs, assessing risks and ensuring compliance.
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Unified Application Protection: Brings together discovery, compliance, and protection in one platform.
What benefits and ROI should users expect?
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Immediate Value: Quickly delivers protection and insights into user and agent behavior.
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Scalability: Handles billions of API interactions, ensuring performance and security at scale.
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Comprehensive Protection: Defends against a wide range of cyber threats and vulnerabilities.
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Ease of Implementation: No-code approach allows faster deployment and reduced technical debt.
In financial services, Cequence Security enables secure AI-driven innovations by protecting sensitive customer data and mitigating fraud risks. In retail, it safeguards transactions and user data from automated bots and unauthorized access, ensuring compliance and enhancing customer trust.
Polyspace Code Prover boosts code reliability by identifying critical issues like memory corruption and null pointer dereferences, adhering to ISO 26262 standards.
Polyspace Code Prover offers advanced static code analysis tailored to detect complex runtime issues, making it a substantial asset in safety-critical software development. With features that facilitate easy integration with minimal tool switching, it effectively examines code segment runtimes for potential faults such as memory overflows. Polyspace Code Prover stands out by providing mathematical proofs of correctness, differentiating it from other static tools. However, improvements in processing speed and large-scale application handling remain necessary. While integration challenges exist with CI environments like AWS and Azure, the tool's efficiency is valued in automotive applications for unit-level verification and requirement-based component development, despite some scalability limitations.
What are Polyspace Code Prover's key features?
- Division by Zero Checks: Ensures code stability and reliability by identifying risky operations.
- Memory Corruption Detection: Safeguards against potential memory leaks and access violations.
- ISO 26262 Compliance: Aligns with critical automotive industry standards.
- Mathematical Correctness Proof: Provides certainty in logic correctness, unique in static analysis tools.
- Minimal Tool Switching: Enhances user efficiency with seamless integration into workflows.
What benefits can be gained from Polyspace Code Prover?
- Enhanced Code Reliability: Reduces faults and improves the robustness of developed code.
- Increased Efficiency: Fast analysis with minimal tool changes streamlines workflow.
- Compliance Assurance: Supports meeting stringent industry standards.
- Risk Reduction: Identifies critical issues early, minimizing potential downtime or defects.
In industries such as automotive, Polyspace Code Prover is crucial for Functional Safety validation. It is applied in diverse projects like vertical control systems and cluster infotainment, with a focus on requirement-based component development. Despite challenges in larger applications, it remains a vital tool for analyzing Simulink models and small-scale implementations.
