Joe is MixMode’s Vice President of Product Marketing. He has led product marketing for multiple cybersecurity companies, including Anomali, FireEye, Neustar, Nextel, and various startups. Originally from New York, Joe lives in the DC suburbs and received his bachelor’s degree from Iona College.
Gartner recently released its annual report. Emerging Technology Impact Radar: Security. This report analyzes 23 new technologies and trends that will shape the future of cybersecurity products and services. We provide insights to help security leaders develop strategies and roadmaps to take advantage of these innovations.
4 main themes
Gartner provided actionable insights in this year’s report identifying four central themes across technology trends.
- Leverage advances in threat detection and response
Continuing advances in AI have enabled attackers to quickly adapt their techniques. Threat detection, investigation, and response (TDIR) products need enhanced detection capabilities to address this. New technologies such as advanced behavioral analytics improve detection speed and reliability.
- Transition to proactive cyber defense
As attacks evolve, detection and response alone are not enough. Security teams are increasing the complexity of attacks and need proactive defenses that reduce risk before an incident occurs. This requires technology for continuous protection and proactive mitigation.
- Secure emerging digital environment
New digital realms such as smart cities, connected vehicles, and the metaverse require customized security platforms and services built in from the start. Security leaders need to partner with smart world technology providers early on.
- Enable hyperautomation
AI and automation can optimize security processes and reduce dependence on scarce human resources. The focus is on combining software and procedural re-engineering with data analytics to drive efficient protection and response.
MixMode mentioned in the report
MixMode, noted as a leading vendor of choice for AI-enhanced security operations and advanced behavioral detection analytics, introduces four innovations that leverage AI to enhance detection, automation, efficiency, and overall security capabilities. It has the features of a unique approach.
Security operations using AI
AI-based security operations is the use of AI and machine learning to automate repetitive tasks in the security operations center (SOC) and augment human security analysts to make faster decisions. point. Key features include alert triage, incident response automation, enhanced threat hunting, analyst coaching, and more.
This is important because AI can help SOCs extract more value from limited staff resources by handling routine tasks and increasing analyst productivity. You can also respond to increased alerts and advanced threats.
Advanced behavioral detection analysis
This technology uses machine learning and artificial intelligence to establish a baseline of normal behavior for users, devices, and systems. Deviations from expected patterns provide early warning of attacks or insider threats.
Advanced behavioral analysis is essential to improve threat detection speed and accuracy compared to traditional rule-based methods. Adding behavioral indicators to your threat intelligence can help you discover new attacks that your rules miss.
Identity threat detection and response
ITDR focuses on monitoring identity services, detecting identity-targeted threats, investigating incidents, and automating response processes. Provides specialized tools to protect your identity infrastructure.
ITDR is important because identities are a prime target for attackers. As organizations adopt distributed architectures, ITDR becomes critical to securing access and privileges. Bring robust protection to your identity systems.
Generative Cybersecurity AI
It uses AI models like ChatGPT to generate natural language, automate security workflows, and augment human capabilities. Use cases include improving detection, coaching analysts, and automating threat hunting.
Generative AI allows security teams to process more data faster and in near real-time. It also improves the user experience with conversational interactions. As threats evolve, AI-powered defense becomes essential.
These four categories leverage AI to improve detection, automation, efficiency, and overall security capabilities. Adopting these emerging technologies is essential for organizations to protect against rapidly evolving and advanced threats targeting identities, networks, and systems.
Radar overview
Each profile provides two layers of analysis: a distance estimate and a mass estimate. Scope — “Scope” refers to Gartner’s estimated time to reach early majority (adoption rate of his target market greater than 16%), rather than when a product leader should act on an investment. Here’s a starter guide to when to invest in a product leader based on your product strategy, taking into account planning, development, and time to launch.
The four categories mentioned above fall under the Now category.
Key technologies and trends
Below are some additional highlights from the 23 technologies analyzed in terms of expected impact and implementation schedule.
Short term (1-3 years)
- AI-powered security operations: Apply AI to automate repetitive tasks and augment human analysts. It will be table stakes.
- Secure Access Service Edge (SASE): Integrate networking and security into cloud services. Adoption is accelerating rapidly.
- Cloud-native application protection platform (CNAPP): Integrates tools to protect cloud-native apps and resources. Adoption rate is increasing.
- Exposure management: Continuously assess asset vulnerabilities and attack surfaces. Driven by explosive growth in exposure.
Middle term (3-6 years)
- Automated Moving Target Defense: Randomize the environment to increase uncertainty for attackers and thwart exploits. Prevent or detect?
- Policy as code: Enable infrastructure-as-code tools to implement security controls and policies. DevSecOps accelerator.
- Software supply chain security: Securing all stages of the software development lifecycle. Demand increased after a high-profile open source breach.
- Machine identity management: Protect and control workload and device identities. will become important as cyber-physical systems grow.
Long term (6-8 years)
- Quantum-enhanced security: Leveraging quantum technology for encryption, communications, and machine learning. Revolutionizing encryption.
- Cybersecurity mesh architecture: Interoperable security services that enable dynamic, adaptive access control and threat response.
- Metaverse security: decentralized identity, access management, and threat monitoring for immersive virtual worlds.
Important points
Gartner predicts significant developments that will use AI to improve threat detection, speed investigation and response, and enable cybersecurity automation. Proactive security functions will become increasingly important. Architectures will shift to support zero trust, cloud-native apps, and new digital environments. Quantum technology brings progress.
Security leaders must proactively evaluate these trends and time investments to leverage those that align with their risk profile, attack surface, and business priorities. Partnering with emerging technology providers is an important way to prepare for the future of cybersecurity.
Click here to download the full report
Other MixMode articles you may like
Buyer’s Guide to AI Threat Detection and Response
Effective use of artificial intelligence in cybersecurity
Bridging the gap: Why ITDR is the missing link in identity protection
Visibility alone is not enough to protect your organization from identity threats
Getting the most out of the MITER ATT&CK framework: Best practices for security teams
