With the increasing sophistication of cyber threats, organizations are challenged to keep their sensitive data and systems secure. Artificial intelligence (AI) offers great promise in automating data security tasks and improving threat detection and response. By 2024, AI is expected to play a much bigger role in data security.
How AI Will Transform Data Security
AI has the potential to automate many data security functions, freeing up security teams to focus on high-value tasks. Here are some of the key ways AI will transform data security in 2024:
Automated Threat Detection
AI algorithms will be able to rapidly analyze huge volumes of data from endpoints, networks, cloud environments, and more to identify anomalous activity indicating cyber threats. By establishing a baseline of normal behavior, AI can quickly flag deviations that may signify an attack.
Augmented Threat Intelligence
AI techniques like natural language processing (NLP) will enable automated aggregation and analysis of threat data from diverse sources. This augmented threat intelligence will help identify emerging attack patterns, malware variants, and attacker behaviors.
Adaptive Cyber Defense
Self-learning AI systems will continuously adjust defensive measures based on the evolving tactics, techniques and procedures (TTPs) of attackers. This adaptive approach will allow for more agile responses to new attack variants.
Automated Risk Assessment
Using techniques like machine learning, AI can automate the asset inventory process and perform continuous risk assessments. This will provide dynamic insights into the attack surface and allow smarter security resource allocation.
Intelligent Security Orchestration
AI will enable automated, policy-based security responses like blocking IP addresses or disabling user accounts. Security orchestration actions that used to require manual oversight can happen instantly and precisely.
Real-World AI Applications for Data Security
AI is not just hype – there are actual AI solutions either in use today or in the works that demonstrate the potential of the technology:
Darktrace’s Cyber AI Platform
Darktrace uses unsupervised machine learning to model normal behavior across cloud, network, IoT, industrial control systems and more. It detects subtle anomalies indicative of advanced threats.
SparkCognition’s DeepArmor Endpoint Protection
SparkCognition leverages a combination of supervised and unsupervised learning to monitor endpoint activity and rapidly identify malicious actions while minimizing false positives.
IBM’s Cloud Pak for Security
This platform uses AI to synthesize insights across hybrid cloud environments, analyze billions of security events, and automate threat identification and response.
Securonix Next-Gen SIEM with UEBA
Securonix combines behavioral analytics with intelligent threat models and security orchestration playbooks powered by AI and machine learning.
Building an AI-Driven Security Architecture
To fully leverage AI, organizations need to invest in developing an AI-ready data security architecture:
Collecting and Centralizing Security Event Data
The key is pulling together logs, alerts, network traffic metadata, endpoint traces, and other security telemetry into a data lake that serves as a foundation for AI.
Implementing Security Analytics Capabilities
Big data security analytics utilizing machine learning and behavioral analytics will enable AI modeling and threat detection on top of the centralized data.
Developing AI Models
AI algorithms need to be trained on large, high-quality, contextualized sets of security data. The models identify normal vs abnormal activity, rank risks, and suggest responses.
Automating Security Operations
An orchestration engine integrates with security analytics to enact policy-based responses to threats identified by AI, such as blocking users or isolating infected endpoints.
Providing Human-Machine Collaboration Interfaces
Security teams need optimized workflows integrating AI tools into their operations. User-friendly interfaces centralize threat alerts, investigation tools, and reporting.
While increasing automation, the focus should be on augmented intelligence with humans and AI systems working together, not replacing personnel. AI is a force multiplier that allows security teams to achieve more. With the right strategy and architecture, AI adoption will significantly advance data security capabilities before 2024 and beyond.
