AI-Driven Threat Detection: Tomorrow’s Threat Detection Demands AI
Threat detection and response is enterprise cybersecurity in a nutshell – it’s the all-encompassing term for the processes and technologies that go into identifying potential security threats. The wide range of attacks and techniques that need to be caught include malware, unauthorized access, data breaches, or any other activities that could compromise the integrity, confidentiality, or availability of an organization’s information systems.
Not only is it the Security Operations Center’s responsibility to keep all of the above in check, the goal is to detect these threats as early as possible to minimize damage. This is a tall order; especially when relying on purely human teams. This article will break down threat detection and response into its components, and see where AI-driven threat detection is poised to make the biggest changes.
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The Gold Standard: NIST Cybersecurity Framework (CSF) 2.0
The NIST CSF 2.0 splits detection and response into five core competencies. Collectively, these dictate how likely a team is to prevent, identify, and respond to an attack in a cohesive and actionable manner.
Identify
The first of the five core competencies, identification is situated at the top of the NIST ‘circle’ for good reason. This first step demands an in-depth understanding of all the assets and suppliers scattered throughout the enterprise. In a lot of organizations, this itself demands a structured, in-depth audit. While it would be ideal to see the entire organization’s assets all in one, the reality of a manual asset assessment is far more piecemeal. Teams will scope and audit a specific business unit or project at a time, creating an inventory as they go.
From there, they then need to marry up the individual assets with the risks facing them. A vulnerability scanning tool helps speed up this process, but it’s worth keeping in mind the sheer amount of effort that goes into the initial asset identification project. And with individual teams conducting the assessments, the vulnerability scanner is all too often analyzing ‘snapshots’ of cordoned-off sections within your enterprise.
Protect
The identity function sets the foundation for protection – which then must actively prevent malicious actors from taking advantage of any gaps within or around them. A lot of classic cybersecurity tools fit into this role, whether it’s identity management and access controls that prevent account takeover, or a firewall that blocks strange network activity.
The classic form of protection – i.e., installing a patch for an application with vuln-ridden code – is becoming increasingly risky. The time window between high-risk CVE publication and their IRL exploitation is often simply too short, with 25% of high-risk CVEs exploited on the very same day they’re published.
Detect
Should an attacker have already slipped past the defenses, a common TTP is to loiter within the confines of a victim’s environment for long enough to establish the next best move. In the cases of insider threat detection, this is an attack’s ground level.
The most prevalent detection tools are still signature-based. These work by analyzing incoming data packets to reveal any sign of suspicious code. The analyzed sections are then compared against an up-to-date database of previous attack patterns.
Respond
When a malicious file or infected network is identified, it’s time to respond; this process defines how well a potential cybersecurity incident is contained. There’s a great deal of pressure at this stage, as a botched response can harm customer reputation even further. For instance, while shutting down all network access would very quickly stop any malware spreading, it would also put the organization into a catatonic state.
Instead, a response demands crystal-clear communication and surgical removal of compromised devices and user accounts.
In complex attacks, affected devices often need to be wiped and the operating system re-installed.
Recover
The final ability for a mature cybersecurity strategy is to recognize the failings that went into a prior breach or event, and come back stronger. The data surrounding response times deeply supports organizations with defined security policies, regular audits, and dedicated CISOs – organizations that start off on this front foot can often recover stock prices within 7 days.
How GenAI Strengthens Every Link of the Chain
Each organization faces its own challenges when optimizing its threat-detection processes. So far, however, AI threat detection has continuously proved its worth in solving some of the biggest issues – particularly within lean teams.
Automatic asset discovery
Real-time analysis
AI’s defensive usage is already as varied as the threats it hopes to thwart. Some of the most interesting developments include the use of ChatGPT to analyze websites for signs of phishing and the ability for LLMs to identify malicious API call sequences, thanks to clusters of suspicious words. AI-driven threat detection is able to reach deep into source code and executable data, granting it far more granular insight than a manual review could.
Behavioral analysis
The true power of AI is in its ability to collect data across incredibly broad swathes of activity occurring. When trained on the highly-diverse datasets of real organizations, this becomes a vital tool for establishing a base of normal network and device behavior. These patterns of activity can then feed into always-on anomaly detection. With this, any behavior that is abnormal can be flagged as cause of concern. To reduce the quantity of false alarms, the same analysis engine can also gather more contextual data surrounding an event to establish its legitimacy.
Finally, all of this can be sent to a human for genuine validation; this feedback is crucial to closing an AI’s feedback loop and ensuring its continued improvement.
Bring AI to Your Arsenal with Stellar Cyber
Stellar Cyber’s Extended Detection and Response (XDR) simplifies the 5-stage threat detection pipeline into a continuous and approachable whole. Rather than frantic snapshots of disparate tools, our XDR provides cross-network analysis to find potential risks in endpoints, apps, email, and more. See for yourself with an in-depth demo today.
