SIEM Speed Without the Sprawl—DataBahn’s Take on Security Data Pipelines

In this Cyber Sentries: AI Insights for Cloud Security episode, host John Richards sits down with Dina Kamal, Chief Revenue Officer at DataBahn, to tackle a familiar cloud security problem: teams can’t get the right data into the SIEM fast enough, and when they do, costs and noise spike. After the introductions, John and Dina dig into why data integration and parsing often consume most of the timeline in SIEM projects—and how a security data pipeline layer can compress onboarding from months to weeks.

They also explore what “doing more with less” looks like in a modern SOC: filtering and routing data based on detection value, preserving what’s needed for compliance, and keeping flexibility for SIEM migrations. Dina’s bigger point is that AI only becomes truly useful when it’s paired with domain expertise and real operational context—otherwise it’s easy to end up with impressive-looking outputs that don’t hold up under investigation pressure.

Questions We Answer in This Episode

• Why do SIEM projects stall on data onboarding, and what speeds it up?
• How can you cut SIEM ingestion costs without weakening detections?
• What does owning your security data change during SIEM migrations?
• Where does AI help most in SOC workflows, and where do guardrails matter?

Key Takeaways

• Data pipelines remove SIEM “plumbing” bottlenecks by automating collection, parsing, and transformation.
• Cost reduction works best when you filter by security value, not just by volume.
• Decoupling data collection from the SIEM reduces lock-in and simplifies vendor changes.
• AI is strongest when guided by security context and experienced practitioners.

The throughline is practical: better detections and faster investigations start upstream with intentional data handling. By treating the SIEM as a high-value analytics destination instead of a dumping ground, teams can regain capacity, reduce noise, and keep options open as tools and vendors change. And when AI is applied to the right parts of the workflow—with clear constraints and real-world context—it can accelerate outcomes without compromising trust.

Links & Notes

• DataBahn
• Connect with Dina Kamal on LinkedIn
• Learn more about Cyberproof
• Got a question? Ask us here!

• (00:04) - Welcome to Cyber Sentries
• (01:02) - Meet Dina Kamal
• (03:14) - Data Pipeline Management
• (05:55) - The Target
• (07:32) - Changing Vendors
• (08:34) - No Storage
• (09:31) - Why People Need It
• (13:09) - Ahead of the Curve
• (19:54) - Capturing the Data
• (23:02) - Useful Data
• (26:02) - More with Less
• (27:03) - Visibility
• (29:40) - When to Start
• (31:04) - Wrap Up

Bridging the AI Security Gap—Inside the Rise of Non‑Human Identities

In this episode of Cyber Sentries from CyberProof, host John Richards sits down with Idan Gour, co-founder and president of Astrix Security, to unpack one of today’s fastest-emerging challenges: securing AI agents and non-human identities (NHIs) in the modern enterprise. As companies rush to adopt generative-AI tools and deploy Model Context Protocol (MCP) servers, they’re unlocking incredible automation—and a brand-new attack surface. Together, John and Idan explore how credential leakage, hard-coded secrets, and rapid “shadow-AI” experimentation are exposing organizations to unseen risks, and what leaders can do to stay ahead.

From Non‑Human Chaos to Secure‑by‑Design AI

Idan shares the origin story of Astrix Security—built to close the identity-security gap left behind by traditional IAM tools. He explains how enterprises can safely navigate their AI journey using the Discover → Secure → Deploy framework for managing non-human access. The conversation moves from early automation risk to today’s complex landscape of MCP deployments, secret-management pitfalls, and just-in-time credentialing. John and Idan also discuss Astrix’s open-source MCP wrapper, designed to prevent hard‑coded credentials from leaking during model integration—a practical step organizations can adopt immediately.

Questions We Answer in This Episode

• How can companies prevent AI‑agent credentials from leaking across cloud and development environments?
• What’s driving the explosion of non‑human identities—and how can security teams regain control?
• When should organizations begin securing AI agents in their adoption cycle?
• What frameworks or first principles best guide safe AI‑agent deployment?

Key Takeaways

• Start securing AI agents early—waiting until “maturity” means you’re already behind.
• Visibility is everything: you can’t protect what you don’t know exists.
• Automate secret management and avoid static credentials through just‑in‑time access.
• Treat AI agents and NHIs as first‑class citizens in your identity‑security program.

As AI adoption accelerates within every department—from R&D to customer operations—Idan emphasizes that non‑human identity management is the new frontier of cybersecurity. Getting that balance right means enterprises can innovate fearlessly while maintaining the integrity of their data, systems, and brand.

Links & Notes

• Learn more about Paladin Cloud
• Learn more about Astrix Security
• Open Source MCP Secret Wrapper
• Idan Gour on LinkedIn
• Got a question? Ask us here!

• (00:04) - Welcome to Cyber Sentries
• (01:21) - Meet Idan Gour
• (03:36) - As the Vertical Started to Grow
• (06:37) - The Journey
• (09:24) - Struggling
• (13:18) - Risk
• (16:15) - Targeting
• (17:54) - Framework
• (20:18) - Implementing Early
• (21:52) - Back End Risks
• (24:04) - Bridging the Gap
• (26:13) - When to Engage Astrix
• (29:54) - Wrap Up

AI-Powered Compliance: Transforming Enterprise Security

In this episode of Cyber Sentries, John Richards speaks with Richa Kaul, CEO and founder of Complyance. Richa shares insights on using modular AI systems for enterprise security compliance and discusses the critical balance between automation and human oversight in cybersecurity.

Why Enterprise Security Compliance Matters Now

The conversation explores how enterprises struggle with increasing cyber threats and complex third-party vendor networks. Richa explains how moving from reactive to proactive compliance monitoring can transform security posture, sharing real examples from Fortune 100 companies and major sports organizations.

AI Implementation That Prioritizes Security

Richa details their approach to implementing AI in compliance, emphasizing their commitment to data privacy and security. The company uses a modular AI infrastructure with opt-in features and minimal data access principles, demonstrating how AI can enhance security without compromising privacy.

Questions We Answer:

• How can enterprises shift from reactive to proactive compliance monitoring?
• What are the key considerations for implementing AI in security compliance?
• How should companies manage third-party vendor risks in the AI era?
• What role does employee education play in maintaining security compliance?

Key Takeaways:

• Continuous monitoring beats point-in-time compliance checks
• Modular AI systems offer better security control than all-in-one solutions
• Third-party vendor risk requires automated, continuous assessment
• Human elements like training and culture can't be fully automated

Looking Ahead: Security Challenges

The discussion concludes with insights into future challenges, including quantum computing's impact on security and the growing complexity of AI-related risks. Richa emphasizes the importance of building nimble, configurable systems to address emerging threats.

Links & Notes

• More About Richa Kaul
• Complyance on LinkedIn and the Web
• Learn more about Paladin Cloud
• Learn more about Cyberproof
• Got a question? Ask us here!

• (00:04) - Welcome to Cyber Sentries
• (01:13) - Meet Richa Kaul from Complyance
• (02:32) - Areas Needing Security
• (04:19) - Reactive vs. Proactive
• (06:17) - Integrating AI
• (07:59) - AI Compliance Challenges
• (10:48) - Training Their Models
• (12:16) - Evaluating Third Parties
• (15:49) - The Team
• (19:04) - Looking to the Future
• (20:44) - How Others Are Implementing AI
• (24:04) - Creating Capacity
• (25:44) - Companies Doing It Well
• (27:25) - When They Don’t Have the Resources
• (28:50) - Wrap Up