Field CISO Insights: How to Navigate Agentic AI in Cybersecurity and Align Security with Business Goals

We had a unique opportunity to sit down with Anant Thangaraju, who's a Field CISO at E+, to get his thoughts on the current landscape of agentic AI in cybersecurity. This blog post is based on the discussion between Neha Garg, Arambh Labs' CEO, and Anand Thangaraju. This blogpost covers Anand's views on modern CISO challenges, board communication, and the future of AI in security operations.

Introduction: The Evolving Role of the Field Chief Information Security Officer (CISO)

In today’s rapidly changing cybersecurity landscape, Chief Information Security Officers (CISOs) (chief information security officer) face unprecedented challenges. From navigating complex threat environments to justifying security investments to the board, the role has evolved far beyond traditional IT security management.

Field CISOs like Anand Thangaraju at EPlus are at the forefront of helping enterprises tackle these challenges. The modern chief information security officer is responsible for developing a comprehensive cyber strategy and robust risk management practices. Unlike traditional consulting roles, Field CISOs work directly with practicing CISOs, drawing from real-world operational experience in regulated environments to provide practical, actionable guidance.

The Business-First Approach to Cybersecurity Strategy

Why Vendor Selection Shouldn't Come First

One of the most critical mistakes organizations make is starting their security strategy with vendor selection. According to Thangaraju, successful cybersecurity programs begin with a fundamental question: What are our business priorities?

This business-first approach involves:

• Breaking down core business objectives before evaluating security solutions
• Aligning security programs with overall organizational goals
• Maximizing ROI on existing investments rather than constantly seeking new tools
• Integrating security into core business processes to ensure alignment with organizational priorities and embed compliance and risk mitigation into daily operations
• Prioritizing improvements to current infrastructure before considering replacements

The Promise of Agentic AI as an Orchestration Layer

The hope for many security leaders is that agentic AI will provide the necessary orchestration layer to tie together existing security infrastructure and optimize security workflows more effectively. This could potentially solve the common problem of security tool sprawl while improving overall program efficiency.

Agentic AI systems represent a new generation of ai systems capable of autonomous decision-making and orchestration. Unlike traditional AI, these agentic ai systems can independently reason, plan, and act, enabling more dynamic and adaptive security operations.

Communicating Cybersecurity Value to the Board

Moving Beyond Traditional KPIs

Traditional cybersecurity metrics often fail to resonate with executive leadership. Metrics like “threats defended” or “automation hours saved” don’t translate effectively to business impact. Instead, successful CISOs are adopting new communication strategies that help business leaders gain insights into how security initiatives support organizational objectives.

The "Vital Signs" Approach to Security Reporting

Thangaraju advocates for a "vital signs" methodology when reporting to the board:

1. Present overall program health in easily digestible terms
2. Use analogies that resonate with business executives
3. Transition from operational metrics to strategic initiatives
4. Connect security investments to long-term business enablement

Aligning Security Narratives with Business Trends

Effective board communication requires connecting cybersecurity initiatives to broader business transformations:

• Remote work adoption and its security implications
• SaaS environment dependencies and associated risks
• Digital transformation initiatives requiring security consideration
• Aligning security initiatives to support innovation and enable business transformation
• Long-term strategic visions like zero trust architecture

The Reality of Agentic AI in Cybersecurity

Beyond the Hype Cycle

The cybersecurity industry has moved from the initial “Gen AI hype” into what Thangaraju calls the “agentic AI hype.” This new phase brings both opportunities and challenges:

Current Assumptions:

• Agents can replace lower-tier security roles
• Automation can handle complex decision-making processes
• AI agents can operate with minimal human oversight

Reality Check:

• Claims remain largely unproven in practice
• Non-deterministic nature of AI models creates reliability concerns
• Organizations need to find the right balance between automation and human involvement
• Agentic AI introduces new cyber threats, including evolving threats and emerging threats that organizations must proactively address

Learning from RPA: The Trial and Error Approach

The adoption pattern for agentic AI mirrors previous automation trends like Robotic Process Automation (RPA). Organizations will likely need to experiment to find the optimal balance, similar to how companies previously struggled to achieve board-mandated productivity gains from RPA implementations.

Strategic Implementation of Agentic AI in Security

Starting with Well-Understood Use Cases

To build trust and demonstrate value, security organizations should begin their agentic AI journey with real world examples that show how agentic AI can analyze log data and enhance threat intelligence:

• Common, repeatable processes with established playbooks
• Deterministic outcomes that can be easily measured
• Well-documented procedures that minimize subjective decision-making
• Low-risk scenarios where errors have minimal impact

The Personal Assistant Model

One of the most promising applications for agentic AI in security is the personal assistant or co-pilot model. This approach focuses on:

• Eliminating grunt work from security professionals’ daily routines, while helping security practitioners manage human risk and monitor for insider threats
• Handling routine tasks that consume valuable analyst time
• Providing intelligent assistance while maintaining human oversight
• Accounting for individual differences in work styles and preferences

Building Trust Through Continuous Improvement

For broad, common use cases, organizations can:

Continuous improvement in data protection and efforts to protect sensitive data are essential for maintaining customer trust and preventing data leakage.

• Train agents in cloud environments with comprehensive oversight
• Implement continuous improvement processes based on human feedback
• Establish clear guardrails to ensure consistent performance
• Aim for performance that meets or exceeds human capabilities

Key Personas for Agentic AI Solutions

When developing agentic AI implementations, successful organizations focus on four key personas:
Successful agentic AI adoption requires a collaborative approach with strategic partners across the organization.

1. Customer Experience Enhancement

• Improving response times to security incidents affecting customers, which helps protect sensitive data and maintain customer trust
• Providing better communication during security events
• Enhancing overall service reliability through proactive security measures

2. Operational Efficiency Improvement

• Streamlining security operations center (SOC) processes
• Automating routine compliance and audit tasks, enabled by advanced security tools and AI tools
• Reducing manual effort in threat detection and response

3. Internal Employee Productivity Boost

• Eliminating repetitive security tasks
• Providing intelligent assistance for complex analysis
• Enabling security teams to focus on strategic initiatives

4. Developer Experience Simplification

• Integrating security seamlessly into development workflows, especially as generative ai becomes more prevalent in software development
• Providing automated security guidance and feedback
• Reducing friction in secure coding practices

Industry Challenges and Economic Drivers

The Platform Play Problem

While industries like autonomous vehicles have made significant progress with complex, high-stakes AI implementations, the cybersecurity sector has been slower to adopt comprehensive platform approaches. This lag stems from:

• Economic incentives favoring quick fixes over fundamental solutions
• Market dynamics that reward rapid exits rather than long-term innovation
• Risk aversion in security organizations
• Complexity of integration across diverse security toolsets, which often leads to large volumes of raw data. If this raw data is not properly managed, it can create opportunities for attackers to gain access to sensitive systems.

The Path Forward

Despite these challenges, the cybersecurity industry is positioned for significant advancement through thoughtful agentic AI adoption. Success requires:

• Patience with iterative improvement rather than expecting immediate transformation, as this is essential for building cyber resilience and ensuring efforts to protect data
• Investment in comprehensive solutions rather than point fixes
• Focus on fundamental capabilities that enable broader innovation
• Collaboration between vendors and practitioners to address real-world needs, support innovation, and maintain cyber resilience

Conclusion: Balancing Innovation with Practicality

The future of cybersecurity lies in the thoughtful integration of agentic AI technologies with human expertise. Field CISOs play a crucial role in this transformation, helping organizations navigate the gap between technological possibility and practical implementation.

Key takeaways for security leaders:

1. Start with business alignment before selecting technology solutions
2. Communicate security value using business-relevant metrics and analogies
3. Approach agentic AI adoption with realistic expectations and careful planning
4. Build trust gradually by starting with well-understood, low-risk use cases
5. Focus on human augmentation rather than wholesale replacement

As the cybersecurity landscape continues to evolve, the organizations that succeed will be those that balance innovation with practicality, leveraging the power of agentic AI while maintaining the strategic thinking and contextual understanding that only human security