AI Usage at a Glance
Timeline
Jun 15, 2021
ModerationPractice documented: Akamai uses an AI framework in its Bot Manager product to automatically distinguish human web visitors from malicious automated bots across customer websites, apps, and APIs, assigning each request a bot-likelihood score in real time. This capability has been in production for multiple years and handles an average of 40 billion bot requests per day.
Practice DocumentedView practice →Jun 15, 2021
Data AnalysisPractice documented: Akamai uses machine learning in its Account Protector product to assess in real time whether a login or account action is coming from the genuine account owner or an impersonator, generating a risk score that customer businesses can use to block or challenge suspicious requests. The product launched in June 2021 and received expanded capabilities in October 2024.
Practice DocumentedView practice →Jan 1, 2023
Data AnalysisPractice documented: Akamai uses machine learning models in its Akamai Hunt service to automatically detect suspicious movement of attackers through customers' internal networks, surfacing threats that evade traditional security tools. Human security experts then investigate each flagged event before alerting the customer.
Practice DocumentedView practice →Jan 1, 2024
ModerationNew evidence: Bot Manager | Bot Detection, Protection, and Management | Akamai
Evidence AddedView practice →Jan 1, 2024
Data AnalysisNew evidence: Account Takeover and Identity Theft Protection | Akamai
Evidence AddedView practice →Apr 30, 2024
Data AnalysisPractice documented: Akamai uses AI in its Guardicore Segmentation product to automatically map how applications and devices communicate inside a customer's network, then generate ready-to-enforce security policies that limit attackers' ability to move between systems. Major AI-powered updates to this product were announced in April 2024 and March 2026.
Practice DocumentedView practice →Oct 15, 2024
Data AnalysisPractice documented: Akamai offers an AI Assistant embedded in its web security analytics platform that lets security team members query and filter security data using a conversational chat interface, instead of manually navigating dashboards. The feature was announced in October 2024 and initially rolled out to select customers.
Practice DocumentedView practice →Oct 16, 2024
ModerationPractice documented: Akamai uses machine learning in its App & API Protector product to automatically detect and block web attacks, including zero-day exploits, SQL injection, and application-layer DDoS attacks, against its enterprise customers' websites and APIs. This system has been active in various forms since at least 2021 and received major AI-powered updates in 2024 and 2026.
Practice DocumentedView practice →Oct 16, 2024
Data AnalysisNew evidence: Akamai introduces Behavioral DDoS Engine and AI Assistant to strengthen security portfolio
Evidence AddedView practice →Oct 16, 2024
Data AnalysisNew evidence: Akamai Adds Behavioral DDoS Engine to App & API Protector
Evidence AddedView practice →Oct 29, 2024
Data AnalysisNew evidence: Akamai Account Protector Adds New Capabilities to Power the Fight Against Fraud and Abuse
Evidence AddedView practice →Dec 19, 2024
Data AnalysisPractice documented: Akamai uses machine learning in its API Security product to automatically discover undocumented APIs across a customer's infrastructure and detect anomalous or malicious API traffic in real time. The product has been generally available and receives ongoing updates; a Q4 2024 update enhanced threat-blocking capabilities.
Practice DocumentedView practice →Jan 1, 2025
OtherPractice documented: Akamai offers Firewall for AI, a purpose-built security product that inspects both the inputs sent to and outputs generated by customers' AI applications and large language models, detecting threats like prompt injection attacks, data leaks, and toxic content in real time. The product was announced in April 2025 and was in limited availability as of that date.
Practice DocumentedView practice →Jan 1, 2025
Data AnalysisNew evidence: Network and Cybersecurity Monitoring | Hunt | Akamai
Evidence AddedView practice →Feb 27, 2025
ModerationNew evidence: Harnessing Artificial Intelligence for a Superior Web Application Firewall
Evidence AddedView practice →Mar 14, 2025
Data AnalysisNew evidence: Detect and Remediate Attacks: Practical Applications for Machine Learning
Evidence AddedView practice →Mar 27, 2025
OtherPractice documented: Akamai offers Akamai Inference Cloud, a distributed platform that lets businesses run AI models closer to end users by spreading computing power across thousands of edge locations worldwide, reducing delays and costs compared to centralized cloud providers. The service launched in October 2025 and targets organizations looking to deploy AI in real-world production environments.
Practice DocumentedView practice →Apr 29, 2025
OtherNew evidence: Akamai Firewall for AI: Get Powerful Protection for New LLM App Threats
Evidence AddedView practice →Apr 29, 2025
OtherNew evidence: Akamai Firewall for AI Enables Secure AI Applications with Advanced Threat Protection
Evidence AddedView practice →Oct 1, 2025
OtherNew evidence: Akamai Inference Cloud Transforms AI from Core to Edge with NVIDIA
Evidence AddedView practice →Oct 3, 2025
ModerationNew evidence: Akamai Announces Advanced Bot Detections, Global Recognition of Entities
Evidence AddedView practice →Nov 5, 2025
OtherNew evidence: Akamai Inference Cloud Gains Early Traction as AI Moves Out to the Edge
Evidence AddedView practice →Mar 1, 2026
Data AnalysisNew evidence: Akamai Guardicore Segmentation Transforms Zero Trust with New AI-Powered Capabilities
Evidence AddedView practice →Mar 10, 2026
ModerationNew evidence: Build Transformative Security with AI-Powered WAF Detections
Evidence AddedView practice →Mar 24, 2026
Data AnalysisNew evidence: Akamai updates Guardicore Segmentation with AI to automate zero-trust policy enforcement
Evidence AddedView practice →Recent activity
Akamai uses machine learning models in its Akamai Hunt service to automatically detect suspicious movement of attackers through customers' internal networks, surfacing threats that evade traditional security tools. Human security experts then investigate each flagged event before alerting the customer.
Akamai Hunt applies two types of machine learning models: graph neural networks (GNNs) that learn normal patterns of how network assets connect to each other and flag anomalous connections, and TF-IDF analysis that identifies suspicious processes spreading unusually across systems. The service collects telemetry from Akamai Guardicore Segmentation deployments and Akamai's global DNS sensors, and applies AI/ML analysis to these combined data sources. Human security analysts validate findings before alerts are sent to customers.
Akamai uses AI in its Guardicore Segmentation product to automatically map how applications and devices communicate inside a customer's network, then generate ready-to-enforce security policies that limit attackers' ability to move between systems. Major AI-powered updates to this product were announced in April 2024 and March 2026.
The AI engine analyzes application behavior, traffic patterns, and asset metadata to auto-label unknown network assets, model application dependencies, and generate segmentation policies with confidence scoring and explanations. In April 2024, Akamai launched the Akamai Guardicore Platform with an AI assistant that lets security administrators ask natural-language questions about their network. A March 2026 update added continuous discovery, AI-driven policy generation at scale, simulation of policy impact before enforcement, and delegated application owner workflows.
Akamai uses an AI framework in its Bot Manager product to automatically distinguish human web visitors from malicious automated bots across customer websites, apps, and APIs, assigning each request a bot-likelihood score in real time. This capability has been in production for multiple years and handles an average of 40 billion bot requests per day.
Bot Manager's AI framework operates inline on Akamai Connected Cloud, analyzing requests at the edge before they reach customer origin servers. It uses machine learning models, behavioral analysis, device fingerprinting, and browser anomaly detection to assign a score from 0 (human) to 100 (bot). The system includes per-customer deep learning models that learn attack patterns specific to individual high-profile customers and auto-tunes over time. In October 2025, Akamai announced advanced bot detection enhancements including global recognition of trusted users across its entire customer network.
All practices by category
Akamai uses machine learning models in its Akamai Hunt service to automatically detect suspicious movement of attackers through customers' internal networks, surfacing threats that evade traditional security tools. Human security experts then investigate each flagged event before alerting the customer.
Akamai uses AI in its Guardicore Segmentation product to automatically map how applications and devices communicate inside a customer's network, then generate ready-to-enforce security policies that limit attackers' ability to move between systems. Major AI-powered updates to this product were announced in April 2024 and March 2026.
Akamai offers an AI Assistant embedded in its web security analytics platform that lets security team members query and filter security data using a conversational chat interface, instead of manually navigating dashboards. The feature was announced in October 2024 and initially rolled out to select customers.
Akamai uses machine learning in its API Security product to automatically discover undocumented APIs across a customer's infrastructure and detect anomalous or malicious API traffic in real time. The product has been generally available and receives ongoing updates; a Q4 2024 update enhanced threat-blocking capabilities.
Akamai uses machine learning in its Account Protector product to assess in real time whether a login or account action is coming from the genuine account owner or an impersonator, generating a risk score that customer businesses can use to block or challenge suspicious requests. The product launched in June 2021 and received expanded capabilities in October 2024.
Akamai uses an AI framework in its Bot Manager product to automatically distinguish human web visitors from malicious automated bots across customer websites, apps, and APIs, assigning each request a bot-likelihood score in real time. This capability has been in production for multiple years and handles an average of 40 billion bot requests per day.
Akamai uses machine learning in its App & API Protector product to automatically detect and block web attacks, including zero-day exploits, SQL injection, and application-layer DDoS attacks, against its enterprise customers' websites and APIs. This system has been active in various forms since at least 2021 and received major AI-powered updates in 2024 and 2026.
Akamai offers Akamai Inference Cloud, a distributed platform that lets businesses run AI models closer to end users by spreading computing power across thousands of edge locations worldwide, reducing delays and costs compared to centralized cloud providers. The service launched in October 2025 and targets organizations looking to deploy AI in real-world production environments.
Akamai offers Firewall for AI, a purpose-built security product that inspects both the inputs sent to and outputs generated by customers' AI applications and large language models, detecting threats like prompt injection attacks, data leaks, and toxic content in real time. The product was announced in April 2025 and was in limited availability as of that date.
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