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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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In the rapidly evolving landscape of cybersecurity, organizations are increasingly turning to AI-powered solutions to enhance their security operations. As agentic AI becomes more prevalent in security operations, a critical question emerges for both vendors and customers: what's the most effective pricing model for these intelligent systems?
The debate typically centers around two primary approaches: billing based on tool usage (how often the AI agent performs actions) versus billing based on outcomes (successful threat detection or incident resolution). Let's explore the implications of each model and determine which might serve security teams better in the long run.
The Rise of AI Agents in Security Operations
Security operations centers (SOCs) face unprecedented challenges today - alert fatigue, staff shortages, and increasingly sophisticated threats. This has accelerated the adoption of security operations automation powered by AI agents.
Unlike traditional security tools that follow rigid rules, agentic AI systems can:
- Autonomously investigate potential threats
- Orchestrate responses across multiple security tools
- Learn from past incidents to improve future performance
- Make contextual decisions similar to human analysts
According to Gartner, by 2025, more than 50% of enterprises will be utilizing AI agents in their security operations, up from less than 5% in 2021.
The Tool Usage Pricing Model
Under a tool usage pricing model, customers pay based on how frequently their AI agents engage with security systems or perform specific actions.
Advantages
- Predictable costs: Organizations can forecast expenses based on expected usage patterns.
- Alignment with resource consumption: This model reflects the actual computing resources and API calls the vendor provides.
- Simplicity: Usage-based pricing is straightforward to track and bill.
Disadvantages
- Misaligned incentives: Customers may hesitate to fully utilize the AI agent even when needed, fearing higher costs.
- Penalizes necessary activity: Some security environments naturally require more investigation, potentially making costs unpredictable during security incidents.
- Discourages exploration: Teams might avoid letting AI agents perform comprehensive investigations to save costs.
The Outcome-Based Pricing Model
With outcome-based pricing, customers pay for successful results - such as correctly identified threats, resolved incidents, or prevented breaches.
Advantages
- Aligned incentives: Vendors are rewarded only when their AI agents deliver value.
- Predictable ROI: Organizations can directly link security spending to successful outcomes.
- Shared risk: The vendor shares responsibility for the AI agent's effectiveness.
- Encourages comprehensive protection: No financial penalty for thorough investigation.
Disadvantages
- Defining "success" is challenging: What constitutes a successful outcome isn't always clear-cut in cybersecurity.
- Attribution problems: Was it the AI agent that prevented the breach, or another control?
- Difficulty handling false positives/negatives: How are cases handled where the AI made the wrong call?
Hybrid Models: Combining the Best of Both Worlds
Many security vendors are exploring hybrid pricing approaches that incorporate elements of both models:
Credit-Based Pricing
Some vendors offer a credit-based pricing system where organizations purchase credits that are consumed at different rates depending on the complexity and value of actions performed. This approach provides flexibility while maintaining some outcome orientation.
Tiered Outcome-Usage Models
Another approach combines a base usage fee with outcome-based bonuses or incentives. This ensures vendors receive compensation for providing the service while aligning incentives toward successful outcomes.
Implementation Considerations for Any Pricing Model
Regardless of the pricing model chosen, several factors should be considered when implementing AI agents in security operations:
Guardrails and LLM Ops
Effective guardrails around AI agent behavior are essential to ensure that costs don't spiral unexpectedly. LLM ops frameworks help manage AI agent behavior within appropriate boundaries while optimizing for successful outcomes.
Orchestration Capabilities
The ability of AI agents to orchestrate actions across multiple security tools significantly impacts their value proposition. Pricing models should account for the complexity and breadth of orchestration capabilities.
Transparency and Metrics
Any pricing model requires clear visibility into how the AI agent is performing. Robust metrics and reporting capabilities are essential for validating both usage and outcomes.
What Leading Security Vendors Are Doing
Research from Forrester indicates that 63% of security vendors offering AI agent capabilities are moving toward outcome-based pricing models, recognizing that this approach better aligns with customer value expectations.
Microsoft's Security Copilot, for instance, initially launched with a per-seat licensing model but has introduced outcome-based components for enterprise customers based on feedback.
Similarly, Palo Alto Networks prices its AI-powered Cortex XSIAM platform with a hybrid model that includes both baseline capacity and outcome-oriented components.
Conclusion: Finding the Right Balance
The ideal pricing model for AI agents in security operations ultimately depends on organizational needs, maturity, and risk profile. However, the industry appears to be moving toward models that emphasize successful outcomes over mere tool usage.
For security leaders, the key considerations should be:
- Does the pricing model encourage comprehensive security rather than constraint-based investigation?
- Are incentives aligned between the vendor and your security goals?
- Can you clearly measure return on investment?
- Does the model scale appropriately during security incidents?
As agentic AI continues to transform security operations, pricing models will likely continue to evolve. Organizations that carefully evaluate pricing approaches against their security objectives will be better positioned to maximize the value of these powerful new tools.
By focusing on outcomes rather than activity, security teams can ensure their AI agents serve as true force multipliers, enhancing human analysts rather than generating additional cost concerns during critical security events.