How AI Will Improve Security Operations in 2025

Artificial intelligence (AI) has become a hot topic of conversation since OpenAI made it readily available in 2022. It’s reasonable to argue that OpenAI's initiatives made AI more accessible, which raised awareness of the technology, even though AI was applied in a whole different range of industries and contexts prior to 2022.

• AI is being used in email security to fight phishing attempts
• It's used for correlating data in cyber threat intelligence (CTI)
• AI helps with identity and access management (IAM) by analyzing behavior
• It’s used in incident response (IR) to automate and accelerate analysis

It's clear that the technology is continually advancing. When we think about what was available just two years ago and compare it to the AI models we have today, the progress is striking.

Below, we will give 5 predictions on how AI will improve various aspects of security operations in 2025, based on current trends happening today.

1. AI-Backed Automated Forensics

By 2025, AI-powered forensics will employ natural language processing (NLP) and machine learning (ML) models to replace manual labor in incident investigations.

At the moment, security operations centers (SOCs) manually link indicators of compromise (IOCs) from logs, memory dumps, and network traffic captures using human analysts. AI could assist this process by detecting anomalies using pattern recognition to gather forensic evidence automatically.

AI algorithms will outstrip current rule-based networks and detect network movement when suspicious login attempts or unusual data transfers over network segments occur.

While some vendors are marketing solutions with these capabilities now, the technology is still developing. Most current approaches involve some human analysis and intervention. However, it’s fair to expect better forensic tools to transform how security teams function in the future.

2. AI Security Event Correlation and Contextualization

In 2025, we anticipate that AI-driven security event correlation and contextualization systems will leverage ML algorithms, particularly deep neural networks, to process and analyze vast amounts of heterogeneous data in real-time. 

These systems will make use of NLP methods to gather relevant information from disorganized data sources like threat intelligence reports as well as security blogs. The AI will use advanced pattern recognition algorithms to detect subtle relationships between events that may appear unrelated, spanning a variety of network levels and information sources.

It could for instance correlate a suspicious DNS request with an unusual process execution on an endpoint and an abnormal outbound connection based on historical behavior of the entities. The system will likely then use probabilistic reasoning and Bayesian inference to determine if these correlated events represent a real threat.

3. AI-Powered Deception and Honeypot Automation

AI-powered attack deception and honeypot automation will likely revolutionize cybersecurity defenses. Advanced ML algorithms will analyze network traffic patterns, attacker behaviors, and threat intelligence feeds in real-time to dynamically generate and deploy convincing decoys and honeypots.

These AI systems will utilize generative AI capabilities to create authentic-looking fake assets, including realistic code repositories, credentials, and network topologies that are indistinguishable from legitimate resources. 

AI will then continuously adapt the honeypots' configurations, interactions, and vulnerabilities based on observed attack techniques, ensuring they remain attractive and credible targets. When intruders engage with these deceptive elements, the AI will orchestrate complex, automated responses to mislead and contain the attackers while gathering detailed threat intelligence.

4. AI-Based Insider Threat Risk Scoring

AI-based insider threat risk scoring systems are expected to use ML algorithms and deep neural networks to continuously analyze vast amounts of user and device data in real-time. 

These systems will likely utilize clever user and entity behavior analytics (UEBA) to establish baseline patterns for each individual and detect subtle deviations that may indicate potential insider threats. The risk scoring mechanism will likely incorporate multi-dimensional analysis, considering factors such as:

• Access patterns: Frequency, timing, and location of data access attempts
• Transfer activities: Volume, destination, and sensitivity of data being moved
• Communication analysis: Language checking for for sentiment and intent
• Temporal patterns: Time-based anomalies in user behavior
• Contextual information: Integration with HR systems to see role status changes

Emerging Attack Vectors: The Hidden Risks of AI Integration

While all of this is clearly good, it's important to note that integrating AI will introduce new attack vectors that have not been previously considered.

Through 2025, generative AI will cause a spike in cybersecurity resources required to secure it, causing more than a 15% incremental spend on application and data security. — Gartner 2024 (Source)

This is well supported by the forecasts of prominent research firms like Gartner. The discovery, understanding, and securing of new attack vectors is going to be incredibly important.

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