Transforming Security with AI: Use Cases for Enhanced Threat Detection

The advanced persistent threats facing organizations today have outpaced the capabilities of traditional security tools. These attacks utilize dynamic tactics, leverage zero-days, and needle-in-a-haystack anomalies that evade rules-based defenses.

Artificial intelligence and machine learning offer great promise to arm security teams with the sophisticated analytical capabilities needed to uncover today’s most dangerous threats. In this article, we’ll explore key use cases where AI can help transform security operations and threat detection.

Boosting SOC Analyst Productivity

A major pain point for modern SOCs is the overwhelming volume of alerts generated across the environment. Tired analysts struggle to pinpoint actual incidents amidst alerts flooding their dashboards every minute of every day. It’s difficult to spot the signals in all that noise.

AI-driven security analytics platforms leverage unsupervised learning algorithms to ingest huge volumes of security data from across domains. By discovering subtle but meaningful patterns and correlations, AI solutions can identify combinations indicative of emerging threats. This distills billions of data points down to a high-fidelity stream of priority incidents for analyst review.

platforms like Microsoft Azure Sentinel and Splunk apply this AI filtering to elevate the most critical threats from massive data lakes. Analyst productivity improves drastically when high-confidence alerts are automatically surfaced rather than getting buried in noise. AI becomes a SOC’s best friend.

Uncovering Stealthy Adversary Tradecraft

Today’s most dangerous threat groups like APTs and nation-state actors utilize advanced tradecraft to evade detection during campaigns. This includes techniques like “living off the land” using built-in system tools, slow crawling across networks, and blending-in amidst normal user behaviors.

Behavioral analytics powered by artificial intelligence are essential to recognizing these stealthy activities that specification-based defenses miss. By establishing profiles of normal behavior across users, devices, and systems, AI can detect anomalies indicative of emerging threats that humans would easily overlook.

User behavior analytics (UBA) solutions profile typical access patterns for users like time-of-day and peer groups. Machine learning algorithms identity outliers suggesting credential theft or insider threats. Network traffic analytics search for covert exfiltration in legitimate flows. Together, behavioral analytics expose threats trying to hide in plain sight.

Identifying Zero-Day Vulnerabilities

By definition, zero-day exploits take advantage of previously unknown software vulnerabilities. Without awareness of the vulnerability, defenses lack the signatures required to block attacks abusing these flaws. AI techniques offer hope for getting ahead of zero days.

Algorithms can be trained using vast databases of historical vulnerability data to discern patterns predictive of vulnerabilities in source code. AI evaluates code syntax and semantics to flag high-risk segments likely to be susceptible to memory corruption issues, injection flaws, or logical errors. AI-driven code reviews surface these trouble spots for remediation before releases.

On the defensive side, AI behavioral models recognize activity deviations indicative of possible zero-day attacks such as unusual registry or system file access. By flagging suspected zero-day activity, incident responders can contain intrusions stemming from as yet unknown vulnerabilities.

Scaling Threat Hunting Capabilities

Proactive threat hunting led by specialized security analysts is vital for preempting threats before they trigger alerts. But thorough hunting requires considerable time and expertise to pore through massive data sets seeking hidden red flags.

AI and machine learning help automate aspects of the hunting process to enable more extensive, rapid campaigns. Data science techniques identify relationships and anomalies that hunters can pivot off of to drive deeper investigations.

Tools like Deep Instinct accelerate hunting by allowing virtual assistants, chatbots, and automated queries to surface leads from across endpoints, networks, clouds, and applications. AI amplifies the productivity of expert hunters, allowing them to search faster and wider.

The Path Forward with AI

While AI shows enormous potential across these use cases, current solutions still have limitations. Algorithms require extensive training data that sufficiently represents the deploying organization’s environment and threat landscape. Models need continuous maintenance and tuning as new tactics evolve.

However, the machine learning journey starts with initial pilot projects focused on high-value challenges like phishing detection or insider threat discovery. Early successes demonstrate practical benefits, which builds trust in AI among stakeholders. A strategic roadmap accounts for incremental advances in capabilities, data science skills, and model explainability.

Over time, organizations expand AI deployments across core threat detection and response workflows. The future SOC platform will integrate AI engines applied across security telemetry feeds. Threats attempting to hide will eventually have nowhere to run or hide from persistent, multipronged AI.

Partnering with MSSPs and AI-focused security consultancies like DBGM accelerates success. Our data scientists and SOC experts help architect the foundations for an AI-enabled SOC, deliver pilot projects, and provide ongoing tuning of algorithms aligned to your environment. Contact DBGM today to chart your AI in security journey.