Practical Security at Scale: Cyber Insights from Engineering Leader Hima Arimanda

As part of the Dialectica Executive Community’s deep dive into cybersecurity leadership, Hima Arimanda—an experienced engineering executive who has architected and implemented data-intensive systems across sectors—offers a grounded perspective on evolving cybersecurity needs in large-scale environments. While not a traditional security specialist, Arimanda has been at the frontlines of incident response, AI risk governance, and enterprise security integration, leading initiatives where engineering meets operational resilience.

Leadership Spotlight

Security That Works in the Real World

Arimanda is a strong advocate for practicality in cybersecurity strategy. His core belief? Security must be deeply contextual.

“Information security in my view has to be practical… strategy and implementation has to be specific to an organization.”

He argues that organizations often fall into the trap of applying standardized security playbooks that don’t fit their architecture or culture. Instead, he encourages teams to engineer solutions that are specific to their workflows, risk profiles, and environments—especially when introducing new technologies like generative AI.

A Defining Incident—and the Strategy That Followed

Arimanda recalls a pivotal moment when he was part of an organization that suffered a major cybersecurity incident. While the exposure was significant, what stayed with him was the response.

“It’s the after-the-fact—how we reacted, the tools we brought in… and taking care of the business.”

The response involved full-stack security: application and network protection, data encryption, and governance overlays, implemented under high pressure and scrutiny. The experience continues to inform his approach to scalable, layered defense strategies.

Pushing for Field-Level Data Protection

Arimanda also shared his experience implementing data protection tools that operate at a field-level. The idea initially met with internal resistance requiring proof and evaluation of alternate platforms and tools. This option  is now a critical layer of the company’s data governance strategy.

“We have a requirement to protect data… Data protection requirement is wide. It could be hardware solution, entire data store, row-level or entire blob—but we chose to go with field-level granularity. That’s what we chose.”

The deployment includes API, database, and big data protectors—tailored to meet fine-grained security needs without disrupting system performance.

Advice for Future Cybersecurity Leaders

Arimanda’s guidance to emerging leaders is clear: don’t let tools drive your security strategy.

“Start by identifying your posture. A tool is only as good as how well it fits—and how well people adopt it and the culture of collective alignment when it comes to security”

He stresses the importance of reducing barriers to adoption, making security seamless and automated wherever possible. Security, he believes, only works when it’s embedded and embraced across the organization—not forced top-down.

Cyber Insights

Cybersecurity Spending: A Practical Increase

Arimanda notes that cybersecurity spending is growing by approximately 10%, with the bulk of that increase focused on tooling, rather than headcount. His organization continues to invest strategically in tools that support both security posture and operational scalability.

Cybersecurity for AI-Driven Applications

As AI applications become more integrated into business processes, Arimanda confirms that his team is investing in cybersecurity capabilities specifically to protect against the risks introduced by internal and external-facing AI tools.

Key areas of investment include:

• Data governance and encryption
• Access management for AI tools
• Secure API and system integrations with external AI models

“It’s not just about securing our AI—it’s about securing how AI interacts with everything else.”

Vendors Mitigating AI Development Risk

Arimanda highlights that all 3 major CSPs and other AI pioneers, are speaheading the RAI facet and imcluding service partners and players building guardrails in securing AI application development. He specifically references Azure OpenAI among other CSPs as a leader for its secure deployment infrastructure

Spending on AI-Powered Cybersecurity Tools

Looking at the reverse equation—AI supporting cybersecurity—Arimanda’s team expects a 20% increase in spend. While the split between existing and new vendors is still being determined, they are actively evaluating platforms with advanced AI capabilities for anomaly detection and policy automation.

Evaluating AI-Driven Cybersecurity Vendors

While vendor names are still under evaluation, Arimanda’s selection process is focused on the following criteria:

• Demonstrated capabilities at enterprise scale
• Strong product roadmap
• Customizable security policies for AI governance
• Use of AI within the tool itself
• Auditability and ability to adapt to evolving threat models

“It’s not just about using AI—it’s about using AI securely, and with clear governance controls.”

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