Cyber threats are growing more sophisticated by the day, using advanced techniques like machine learning and automation to carry out attacks. According to Cybersecurity Ventures, global cybercrime costs are predicted to grow by 15 percent per year over the next five years, reaching a staggering $10.5 trillion annually by 2025.
To combat these intelligent threats, organizations need intelligent defense systems powered by artificial intelligence and machine learning. AI-driven cybersecurity leverages capabilities like pattern recognition, predictive analysis, and automated response to secure systems in a quick and scalable way.
This article provides an overview of 10 leading AI-powered cybersecurity platforms that can help protect your company‘s digital assets and sensitive data. For each platform, I cover:
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Company Overview: Brief background on the cybersecurity vendor and their solutions
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AI/ML Capabilities: Details on how the platform applies AI and machine learning techniques
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Key Features: The top capabilities and tools provided to enhance security
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Use Cases: The ideal customers and deployment scenarios for this offering
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Pricing: Overview of the pricing structure to help inform your buying decision
Let‘s get started exploring these AI cybersecurity powerhouses!
1. Darktrace
Darktrace is a cyber AI company founded in 2013 in Cambridge, UK that focuses on enterprise-scale threat detection and response powered by self-learning AI.
AI/ML Capabilities
Darktrace uses unsupervised machine learning, clustering analysis, Bayesian mathematics and AI algorithms developed by specialists from the University of Cambridge to detect novel cyber-threats. It learns a pattern of ‘normal‘ activity for each user and device within an organization to spot even subtle anomalies indicative of emerging threats.
Key Features
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Enterprise Immune System – Mimics human immune system with self-learning AI that continuously adapts and understands normal vs abnormal behavior
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Antigena – Autonomous response technology that takes surgical action against in-progress threats like limiting device functionality or cutting network access
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Cloud support – Detects threats across SaaS apps, IaaS environments and remote work setups
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Email security – Advanced analysis stops sophisticated phishing and Business Email Compromise attacks
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Industrial tech protection – Secures IoT, ICS and OT systems
Use Cases
Ideal for large enterprises across industries needing adaptive cloud-scale cybersecurity powered by self-learning AI. Covers advanced attacks targeting cloud, email, network, IoT/OT environments.
Pricing
Darktrace offers flexible enterprise pricing starting around $30,000/year.
2. CrowdStrike
Founded in 2011 in California, CrowdStrike provides a cloud-delivered solution for next-generation endpoint and cloud workload protection driven by AI.
AI/ML Capabilities
The Falcon platform leverages CrowdStrike Threat Graph, a graph database using trillion of real-time security events and proprietary ML algorithms to stop breaches. This enables AI-powered threat detection, prioritization and automated response.
Key Features
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Real-time detection tuned to identify threats specific to customer environments using ML
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Preventive protection including anti-virus, host firewall, and exploit blocking
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Automated containment of compromised systems and remediation with Falcon Complete
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Threat intelligence feeds with IOCs mapped to MITRE ATT&CK framework
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Covers major OS platforms and both on-prem and cloud environments
Use Cases
Ideal for lean IT organizations wanting an easy, scalable solution for preventing threats and responding to incidents across their endpoints/workloads – with or without dedicated security staff.
Pricing
CrowdStrike uses an annual subscription model priced per endpoint/workload protected. Consulting packages are also available.
3. Vectra AI
Vectra AI is a network detection and response vendor founded in 2011 and headquartered in Colorado. Their flagship Cognito platform delivers AI-driven threat detection and investigation from the network to the cloud.
AI/ML Capabilities
Vectra uses supervised and unsupervised ML algorithms to analyze network metadata and perform non-invasive machine-driven hunting for hidden cyberattackers, detecting threats early and with high fidelity.
Key features
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Behavioral threat detection and analysis
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Prioritized response with threat certainty scores and context
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Cloud platform integrations (Azure, AWS, GCP)
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Detailed investigation workflows with step-by-step guidance
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Coverage across enterprise networks, data centers, SaaS apps
Use Cases
Ideal for security analysts and SOC teams dealing with a high volume of low fidelity alerts. Cognito enhances threat detection, accelerates investigations using automation, and provides clear context for fast incident response.
Pricing
Vectra offers flexible pricing tiers based on deployment size and scope, advanced reporting/analysis needs and level of support. Annual contracts start around $50k.
4. SparkCognition
Founded in 2013 in Austin, SparkCognition offers a portfolio of AI-powered cybersecurity products including DeepArmor, SparkCognition Government Systems, Darwin and DeepNLP. Their solutions are designed for IT/OT infrastructure protection and national security missions.
AI/ML Capabilities
SparkCognition employs proprietary NLP algorithms, dynamic neural networks, expert systems modeling and other AI techniques to allow systems to learn, adapt and make decisions automatically in response to emerging cyber threats or IT events.
Key Features
DeepArmor: Advanced endpoint protection using AI-driven analytics to interpret behavior, identify threats, offer remediation
- Asset Management with dynamic privilege controls
- Microsegmentation for granular policy enforcement
- Adaptive access controls
Darwin: Automates monitoring, analysis, response and reporting for IT and OT
DeepNLP: Derives insight from unstructured data like text, email, system logs using NLP
Use Cases
SparkCognition protects organizations across finance, oil and gas, manufacturing, aerospace and other sectors needing intelligent, autonomous defense tailored to their tech stack, with special support for industrial control systems.
Pricing
Enterprise pricing is available on request. Government, military and Federal contracting pricing models offered as well.
5. SentinelOne
Founded in 2013 and headquartered in California, SentinelOne is an autonomous endpoint protection platform driven by artificial intelligence to defeat attacks across all major vectors.
AI/ML Capabilities
SentinelOne uses machine learning for static AI models and real-time AI engines to identify threats and malicious behavior across networks. Core technologies include:
- Behavioral AI models using ML algorithms like CNN, RNN, transformer networks
- Static AI that evolved malware detection capabilities
- Real-time AI engines (nanoscopic interpretable vectors)
Key Features
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Predictive protection – Stops vulnerabilities before they can be exploited using static and behavioral AI
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ActiveEDR with Storyline – Visual attack representation from beginning to end
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Ranger remote script execution – Broad range of response actions via CLI
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IoT security for both IT and OT devices
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Cloud-based management gives visibility and control across endpoints
Use Cases
Ideal for enterprise businesses prioritizing threat prevention and autonomous response across cloud and distributed environments including IoT and mobile devices.
Pricing
Per-device annual licensing starts around $30 per agent protected. Volume discounts available.
6. Check Point Infinity ThreatCloud AI
Check Point Software Technologies offers the ThreatCloud AI solution as part of the Check Point Infinity Architecture. This unified security platform leverages AI and machine learning for real-time threat prevention and response.
AI/ML Capabilities
ThreatCloud AI uses over 40 AI and ML models to process and correlate huge amounts of data to reveal stealthy attacks masked by millions of benign activities enterprise-wide. This enables early threat detection with over 90% catch rate and low false positives.
Key Features
- Consolidated security across enterprise networks, cloud (AWS/Azure/GCP), remote users
- Real-time threat intelligence sharing across global network
- AI-powered zero phishing engine detects business email compromise attacks
- Botnet protection through command-and-control traffic blocking
- Auto-scaling platform handles unlimited number of users
Use Cases
Ideal for complex, distributed enterprises requiring a unified AI-driven security solution covering email, mobile, endpoint, cloud and network attack vectors.
Pricing
Infinity architecture subscriptions start around $150 per protected gateway annually. Complete pricing is available from Check Point partners.
7. Acronis Cyber Protect
Founded in 2003 in Singapore, Acronis provides cyber protection solutions integrating data protection, cybersecurity, management and storage. Acronis Cyber Protect applies AI and machine learning to safeguard data and systems.
AI/ML Capabilities
Acronis Cyber Protect uses behavioral AI-based anti-ransomware capabilities to detect zero-day ransomware attacks. It analyzes patterns and stops suspicious encryption processes responsible for data locking. AI mapping applied to URL filtering blocks access to known malicious sites.
Key Features
- Unified data protection with AI-powered anti-malware/anti-ransomware
- URL filtering blocks web-based attacks
- Device control for managing external devices
- Vulnerability assessments scan for risks
- Remote desktop protocol protection
Use Cases
Ideal for lean IT teams needing integrated backup and cyber protection for files, systems, and Microsoft 365 accounts. The cloud-based unified dashboard streamlines security monitoring and policy management.
Pricing
Annual subscription pricing starts around $99 per device for Advanced security pack. Volume pricing available for enterprise deployments.
8. Deep Instinct
Founded in 2014 in New York, Deep Instinct applies deep learning to cybersecurity, offering predictive threat prevention safeguarding endpoints against ransomware, malware and zero-days in real-time.
AI/ML Capabilities
Deep Instinct leverages deep learning models trained on raw computing data to extract indicators predictive of malign intent. Their deep neural network is the first purpose-built fully deep learning framework focused solely on cybersecurity.
Key Features
- Zero-day exploit prevention in <20 milliseconds
- Over 85% detection rate with near-zero false positives
- Protection against evasive threats like APT attacks, polymorphic malware, scripts
- Lightweight agent with low CPU/memory resource usage
- Forensic playback of prevented attacks
Use Cases
Ideal for organizations across finance, healthcare, retail and other sectors needing strong endpoint security against advanced threats, malware/ransomware, and file-less attacks targeting memory.
Pricing
Deep Instinct offers annual subscriptions on a per-protected-endpoint basis. Pricing starts around $30 per agent protected annually.
9. LogRhythm NDR
LogRhythm, founded in 2003, provides a security lifecycle management platform addressing threat detection, investigation and neutralization. The LogRhythm NDR module delivers AI-enabled network detection and automated response.
AI/ML Capabilities
Leveraging advanced analytics and machine learning, LogRhythm autonomously surfaces, investigates and prioritizes aberrant network behavior indicative of cyberthreats or compromises. SmartResponseTM Plays enable automated containment upon detection.
Key Features
- Network traffic analysis exposing threats
- Host netflow monitoring on network gear
- AI Engine for automated threat detection/prioritization
- Case management and collaborative investigation
- Out-of-the-box SmartResponse plays – endpoint isolation, netflow rules
Use Cases
Ideal for large enterprises and MSSPs running security operations centers (SOCs) and looking to improve threat visibility, accelerate detection/response and enable security automation.
Pricing
LogRhythm offers subscription-based licensing tiers based on deployment size, optional modules and level of support. Pricing starts around $45 per protected device annually.
10. Cybereason Defense Platform
Founded in 2012 in Boston, Cybereason pioneered malicious operations detection tech for enhanced prevention and active response across IT and OT systems.
AI/ML Capabilities
Leveraging graph theory, analytics engines and machine learning, the Cybereason MalOpTM engine visually uncovers the full scope of multistage attacks unfolding in real-time. This enables faster threat interdiction to prevent damage.
Key Features
- Operation-centric attack interdiction
- Graphic MalOpTM analysis revealing attack sequence
- Automated or on-demand mitigation responses
- Threat hunting unearths deeply embedded risks
- Protection spanning networks, cloud, endpoints
Use Cases
Ideal for large enterprises in finance, energy and utilities, retail, healthcare and other sectors needing added visibility and context into threats playing out within their infrastructure and active policy-based response.
Pricing
The Cybereason Defense Platform is licensed annually on a per-endpoint basis, starting around $38 per protected endpoint per year.
How to Select The Right AI-Powered Cybersecurity Platform
With AI advancing rapidly, there is no shortage of security vendors applying machine learning and automation to help overburdened IT security teams. Here are key considerations when evaluating options:
Identify Gaps – Carefully analyze where your security program has weaknesses – endpoint, network, cloud, etc. Prioritize capabilities filling pressing protection gaps.
Consider Infrastructure – Assess technical compatibility with your environment – integration needs, supported operating systems/platforms, hybrid cloud setups.
Evaluate Threat Prevention – Compare core techniques used by platforms including behavioral analytics, deception tools, threat intel feeds. tuning detection to your industry‘s attack profile.
Review Threat Hunting/Detection Abilities – Critically analyze factors like detection rates, false positive rates, use of deception tools and threat intel feeds.
Assess Automation Capabilities – Review built-in response actions offered and smart policies for automatic containment upon threat confirmation.
Validate Reporting & Analysis – Ensure features like visual forensic analysis and MITRE ATT&CK mapping are available to enhance investigations.
Determine Pricing Model Fit – Carefully weigh operating expense models around endpoints protected, data volume managed and professional services needed.
Limitations of AI Cybersecurity Solutions
While AI and machine learning have revolutionized cybersecurity, these technologies have limitations security teams should recognize as they evaluate options:
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No Silver Bullet – AI cannot fully replace skilled security staff and their threat knowledge, strategic insight and capability for complex decisions factoring business priorities/risks.
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Potential Blindspots – ML models trained on finite datasets may miss threats not represented in that training corpus or struggle detecting subtly modified threats.
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Continued Tuning Needed – To address evolving attacker techniques, ML models require frequent retraining and frameworks to seamlessly deliver updated models.
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Explainability Challenges – Certain techniques like deep learning can behave like black boxes, making logic/predictions opaque and difficult to interpret, tuning and troubleshoot.
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Data Dependency – Effectiveness depends heavily on robust, high quality, labelled datasets used to develop models and patterns – “rubbish in, rubbish out”.
While powerful, AI is not a one-stop solution. Skilled security teams providing supplemental threat expertise, strategic guidance and support for technologies remains imperative as part of a resilient cyber program.
Conclusion
As cybercrime grows more pervasive and crippling across the public and private sector, legacy security tools and manual methods are no longer sufficient. AI and its ability to automatically learn patterns, infer context and take predictive actions represents our best weapon against this hidden enemy.
The platforms explored in this guide showcase leading examples of AI‘s protective power – identifying risks, scoping incidents and driving automated response at machine speed. By leveraging these intelligent systems in conjunction with skilled security staff, organizations can substantially improve their resilience, saving untold sums and safeguarding their future.
I hope this overview has helped frame key capabilities of AI-driven cybersecurity solutions and provided useful insights to help guide your selection process. Please reach out with any questions as you investigate options applicable to your environment. Stay safe out there!
