Signal vs. Noise: The CIO Scorecard for AI Security Vendor Evaluation for 2025
- Advisor@AegisIntel.ai
- Sep 30, 2025
- 6 min read
The Problem: Your security team just recommended a $2M "AI-driven" platform. Three competitors claim the exact same capability. They're not lying—but they're solving completely different problems. Choosing the wrong architecture could cost 18+ months and $5M+ in sunk costs.
Key Findings:
Identical AI terminology masks 7 fundamentally different architectural approaches
"Agentic AI" means multi-agent systems at CrowdStrike, natural language assistants at Microsoft, and fabric orchestration at Fortinet
Only one vendor (SentinelOne) offers true offline AI capability—critical for air-gapped environments
Bottom Line: Use our Vendor Comparison Matrix to cut through marketing hyperbole and align vendor capabilities with your actual security architecture requirements.
Your security vendor just claimed their platform uses "revolutionary AI-driven detection." So did the previous three vendors in your evaluation. Same terminology. Same confidence. Completely different technologies.
The cybersecurity AI marketplace has become a battlefield of buzzwords, with every major vendor claiming transformative artificial intelligence capabilities. For CIOs and CISOs evaluating solutions, the challenge isn't finding AI-powered security tools—it's distinguishing between genuinely transformative technologies and marketing hyperbole.
Our analysis reveals that identical terminology often masks fundamentally different architectural approaches, creating significant implications for enterprise security strategy and vendor selection.
In our first article we introduced the current market dynamics and latest trends, we performed a high-level scan of the category leaders, and shared a snapshot of the verticalization evolving within the space. Here we continue with the next level of assessment—clarifying the competing vendor claims and terms that are bringing confusion to any executive evaluation of the market. We also include a Vendor Comparison Matrix to better align marketing language to a truer reference context.
Setting Expectations: Two Different Security Challenges
As we approach this market, it is important to clear the air on the claims being conflated when vendors discuss AI cybersecurity.
AI cybersecurity is a broader umbrella term that embodies two components:
AI for Cybersecurity, which uses AI/ML models to bolster security efficacy
Security of AI, which involves protecting LLM models and containing unsanctioned use of AI apps
AI for Security: Leveraging machine learning algorithms, behavioral analytics, and pattern recognition to enhance threat detection, automate incident response, accelerate forensic analysis, and augment human security analysts' capabilities across endpoint, network, and cloud environments.
Security of AI: Implementing governance frameworks, access controls, data privacy protections, model integrity verification, prompt injection defenses, and compliance monitoring to secure generative AI deployments and prevent malicious AI exploitation within enterprise environments.
These two domains represent fundamentally different security challenges requiring distinct expertise, toolsets, and organizational approaches—yet vendors often confuse or merge them in marketing materials, creating additional complexity for executive decision-making.
This series focuses on the former—current market offerings from cybersecurity vendors who are selling solutions that leverage AI/ML as part of their solution set to increase operational efficiency.
Cybersecurity AI Vendor Comparison Matrix
AI Terminology Breakdown: Why the Same Words Mean Completely Different Technologies
"Agentic AI"—A $3M Architecture Mistake Waiting to Happen
The term "agentic AI" appears in marketing materials from CrowdStrike, Microsoft, and Fortinet. But they're describing three fundamentally different implementations.
Choosing based on terminology alone could mean selecting a natural language assistant when you need autonomous multi-agent orchestration.
CrowdStrike's "Agentic AI" (Charlotte AI)
Their Definition: AI agents that can independently perform complex cybersecurity tasks at expert human level
12+ specialized AI agents working in concert (detection, triage, investigation, response)
Expert-level decision making—trained on elite MDR team decisions with 98% agreement rate
Multi-modal AI architecture—combines LLMs, ML models, and domain-specific algorithms
Real Example: Charlotte AI can autonomously triage a complex APT attack, correlate IOCs across global threat intelligence, and provide expert-level analysis in minutes vs. hours
💡 Decision Impact: If your security team is overwhelmed with alert fatigue and false
positives, CrowdStrike's multi-agent expert system directly addresses this pain point. The 98% accuracy rate means your analysts can trust automated triage decisions.
Microsoft's "Agentic AI" (Security Copilot)
Their Definition: AI assistant that acts as a force multiplier for security teams through natural language interaction
Natural language processing—security teams can ask questions in plain English
Unified incident management—aggregates data from Defender, Sentinel, Entra ID into single view
Machine speed operations—processes massive datasets at scale
Real Example: Analyst types "Show me all lateral movement in our network last week" and gets comprehensive analysis across all Microsoft security tools instantly
💡 Decision Impact: If you're already heavily invested in the Microsoft ecosystem, Security Copilot provides immediate value through stack consolidation. However, it's fundamentally an assistant, not an autonomous decision engine.
Fortinet's "Agentic AI" (FortiAI)
Their Definition: AI applications that autonomously secure, assist, and govern AI usage across the security fabric
Three-pillar approach: Protect (threat detection), Assist (analyst augmentation), SecureAI (AI governance)
Autonomous network operations—AI manages security policies across 40+ million sensors
15+ years of AI innovation with 500+ patents
Real Example: FortiAI automatically adjusts firewall policies across global network when new threat patterns emerge
💡 Decision Impact: If you operate at massive scale with distributed network infrastructure, Fortinet's fabric-wide orchestration delivers coordinated response across more sensors than any competitor.
"Autonomous AI Decisioning"—The Only True Offline Solution
SentinelOne's Unique Approach
Their Definition: AI that makes security decisions and takes action in real-time without human approval or cloud connectivity
On-device AI engine—Purple AI runs locally on endpoints
Cloudless detection—works even when disconnected from internet
Behavioral analysis—monitors process behavior patterns
Storyline technology—visualizes attack chains automatically
Real Example: When ransomware attempts to encrypt files, Purple AI immediately kills the process, quarantines the endpoint, and rolls back changes—all without contacting the cloud or waiting for human approval
⚠️ Critical Differentiator: SentinelOne is the ONLY vendor in this comparison offering true offline AI capability. If you have air-gapped environments, OT/ICS systems, or remote locations with unreliable connectivity, this is non-negotiable. Every other solution requires cloud connectivity for AI-driven detection.
"Hyperautomation"—Different Scopes of Workflow Automation
CrowdStrike's Context
Their Definition: Complete automation of SOC workflows from detection through response
Workflow orchestration across Falcon platform modules
Multi-AI coordination—different AI agents handle different workflow stages
75% task completion acceleration
Real Example: Suspicious email triggers automated investigation → threat classification → IOC extraction → global threat intelligence lookup → endpoint isolation → all happening without human intervention
Fortinet's Context
Their Definition: Comprehensive automation of security fabric operations using AI
Network-wide automation across Security Fabric platform
40+ million sensors providing coordinated intelligence
Converged security operations—network, endpoint, cloud unified
Real Example: Single threat detected at network perimeter automatically triggers coordinated response across firewalls, switches, endpoints, and cloud workloads globally
"AI-Driven Detection"—Vastly Different Technical Implementations
Palo Alto's "Precision AI"
Thousands of ML models each specialized for specific attack types
BYOML capability—customers can integrate their own ML models
100% detection rate in MITRE ATT&CK evaluations
Prevention-first approach—stops threats before they execute
💡 Financial Impact: Prevention-first architecture reduces incident response costs by 60-80% compared to detect-and-respond approaches. For a 10,000-employee enterprise, this translates to $2-3M annual savings in IR costs.
VMware Carbon Black's Approach
Cross-data telemetry correlation—network traffic + endpoint behavior
Native network visibility—unique among endpoint vendors
1.5 trillion events daily processed for pattern recognition
Attack chain visualization across multiple attack vectors
Zscaler's Implementation
Inline AI inspection—analyzes traffic in real-time at cloud scale
5 trillion daily signals from global Zero Trust Exchange
AI governance—monitors AI/ML applications for security risks
Policy enforcement—AI automatically applies zero trust policies
"Cross-Data Telemetry"—Vendor-Specific Meanings
VMware Carbon Black
Their Definition: Correlation of endpoint, network, and identity data in single analytics engine
Contexa telemetry—proprietary data correlation technology
Network + endpoint visibility—unique dual capability
Single console operations—reduces analyst context switching
Real Example: Detects lateral movement by correlating unusual network traffic patterns with endpoint process behavior and user authentication anomalies
Palo Alto Cortex
Their Definition: Multi-domain data fusion across network, cloud, and endpoint
1,000+ integrations feeding unified data lake
Cortex XSIAM platform—AI-driven security operations
Machine learning correlation across all security domains
Real Example: Correlates DNS queries, firewall logs, endpoint telemetry, and cloud activity to identify sophisticated supply chain attacks
"Zero Trust + AI"—Zscaler's Unique Architecture
Zscaler's Context
Their Definition: AI capabilities embedded directly into zero trust network architecture
Inline inspection and enforcement—AI runs in the data path
Zero Trust Exchange—global cloud platform with AI at every node
AI auto data discovery—automatically classifies and protects sensitive data
GenAI governance—monitors AI application usage for security risks
Real Example: When user accesses cloud application, AI instantly analyzes user behavior, data sensitivity, application risk, and network context to enforce granular access policies in real-time
Same Words, Different Worlds: The Vendor Selection Trap
Based on the above analysis, the critical takeaway is that vendor-defined AI means what that vendor says it is, without reference to any external reference framework or industry standard definitions.
"AI-Driven" means:
CrowdStrike: Multi-agent expert system for SOC workflow automation
SentinelOne: Autonomous endpoint decision engine with offline capability
Microsoft: Natural language security assistant for Microsoft stack
Palo Alto: Thousands of specialized ML models for prevention-first security
VMware: Cross-domain telemetry correlation for hybrid visibility
Zscaler: Inline zero trust enforcement with embedded AI
Fortinet: Security fabric orchestration across 40M+ sensors
This terminology overlap creates significant confusion for CIO/CISO vendor selection—vendors are solving fundamentally different problems despite similar marketing language.
What's Next: Deep Dive on Vendor Capabilities
In our next article, we'll provide a comprehensive roadmap to navigate the vendor messaging jungle with:
Detailed architectural analysis of each vendor's AI implementation
Vendor selection decision trees based on infrastructure profiles
Qualification Questions for CISOs to challenge Vendors during evaluations
Vendor Scorecards based on key project success criteria
Don't make a $2-5M decision based on marketing terminology. Decode the vendor lingo and get to project success and payoff.
For more insights on cybersecurity vendor evaluation and AI architecture selection, follow our series or reach out for confidential consultations on your specific vendor evaluation.
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