Articles

AI Risk Assessment Strategies, Best Practices and Tools

As AI adoption accelerates, so do the risks. This article explores crucial AI risk assessment strategies, from identifying threats to implementing best practices and leveraging essential tools for secure and responsible AI.

Understanding AISPM: Securing the AI Lifecycle

Learn what AISPM is, why it matters, and how it helps organizations secure AI systems, reduce risk, and support safe, scalable innovation.

Stop Data Exfiltration Before It Starts: 9 Proven Strategies

Learn 9 strategies to detect and prevent data exfiltration from insider threats to AI-powered attacks before sensitive data leaves your environment.

Top 12 AI Security Risks You Can’t Ignore

Discover the most pressing 12 AI security risks and learn how to safeguard your business with best practices, threat detection, and secure software strategies.

What Is Shadow AI? Preventing and Managing AI Risks

Explore the growing risks of shadow AI in software development. Learn about the common AI tools used in shadow AI, the associated risks, and how to implement the necessary security measures.

Responsible AI Usage: Key Principles, Best Practices & Challenges

Key principles of responsible AI usage include fairness, transparency, and accountability. Best practices when deploying AI are crucial to ensuring ethical and meaningful implementation.

RAG vs CAG: Key Differences in AI Generation Strategies

Compare RAG vs CAG AI generation strategies. Learn key differences, trade-offs in accuracy & latency, and choose the best approach for enhancing LLMs with external data.

What is RAG, and How to Secure It

Learn how Retrieval-Augmented Generation improves LLMs with your data. Understand critical RAG security risks & discover best practices to protect your AI.

What is MCP in AI? Everything you wanted to ask

MCP (Model Context Protocol) is Anthropic’s specification for how LLMs (large language models) would communicate, share data, and leverage external resources beyond the model’s data.

Agentic AI vs Generative AI

Discover the key differences between agentic and generative AI, and why those distinctions matter for innovation, automation, and security planning.

AI Attacks and Adversarial AI in Machine Learning

Bad actors can alter machine learning systems through adversarial AI attacks. Learn about common attack types and how to safeguard your systems here.

AI Bill of Materials (AIBOM) for Python Developers: Mapping Your AI Dependencies with Snyk

Snyk's new experimental AIBOM tool helps Python developers by providing automated discovery & cataloging of AI dependencies across your projects.

Protecting Financial APIs: Strategies for Preventing Data Breaches

Explore the key challenges and essential strategies for securing financial APIs, from data privacy and compliance to reducing risks in complex environments.

What is Bias in AI? Challenges, Prevention, & Examples

Learn what bias in AI is, why it matters, and how to detect and prevent it with real-world examples and best practices.

Human in the Loop: Leveraging Human Expertise in AI Systems

Human-in-the-Loop (HITL) is a design approach where humans are actively involved in the training & curating the output of AI systems. Learn more about HITL automation in AI and ML.

Developer-First Security: Building Fast and Secure in CI/CD Pipelines

Learn how integrating security into CI/CD pipelines empowers developers to deliver secure, high-quality applications without compromising speed or agility.

Addressing the Hidden Risks of Single-Page Applications

Single-Page Applications are changing the way we interact online, but at what cost to security? Explore the unique security challenges of SPAs and how to safeguard them effectively.

Decoding SQL Injection: Strategies for Secure Web Applications

Explore the intricacies of SQL Injection: learn its workings, impacts, and robust strategies to shield your web applications from this threat.

Balancing Act: The Six Keys to Successfully Navigating Security and App Development Team Tensions

Tired of the friction between developers and security? Learn six keys to bridge the gap, from better training to developer-first tools. Turn roadblocks into collaboration and build secure applications faster.

From Innovation to Protection: Ensuring Data Security in Healthcare

Secure patient data and Health Tech innovations against evolving cyber threats. Discover strategies for continuous security, from embedding automated testing in your CI/CD pipelines to performing regular risk assessments.