Top Use Cases for AI-driven Cybersecurity
In this article
1. Regulatory compliance and risk management
Compliance is a constant pain point for security teams, with regulations shifting and growing more complex each year. AI is starting to play a key role in automating compliance checks, streamlining audits and managing regulatory risks.
One of the most exciting innovations is the Regulatory Response Assistance Solution (RRAS) framework, which uses AI to aggregate and analyze data sources. Through the power of natural language processing (NLP), AI can quickly search through vast datasets to respond to audits or evaluate compliance in real time.
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In the long term, AI-driven cybersecurity will allow organizations to proactively monitor and evaluate compliance against industry regulations, cutting down on audit times and minimizing human error. This kind of predictive analytics could be the future of compliance, providing real-time assessments of an organization's adherence to regulations.
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2. Deepfake and voice clone detection
Deepfakes represent a growing cybersecurity challenge. AI-powered phishing attacks, bolstered by realistic deepfake videos and voice clones, are making it easier for attackers to impersonate executives, employees and vendors.
How can AI fight AI? Deepfake detection tools can analyze audio and video to flag potential fakes. AI can evaluate the effectiveness of these tools by simulating real-world scenarios, such as setting up call centers where real and deepfake voices interact. By testing these tools in a controlled environment, organizations can assess their effectiveness and improve their defenses against social engineering attacks.
The rapid rise of deepfake technology means this is a growing priority for security teams, particularly in industries dealing with sensitive information or financial transactions. AI-driven cybersecurity tools designed to spot deepfakes in real time are a crucial line of defense.
3. Zero-touch help desk
Managing help desk requests — such as identity and access — is a core part of cybersecurity, but many of these tasks are mundane and time-consuming. Password resets, authentication requests and access management can consume hours of valuable time from security teams.
Enter AI-driven cybersecurity solutions. By automating routine tasks, AI can operate as a virtual help desk, freeing up security analysts to focus on higher-level problems. These AI-based systems can handle repetitive identity and access management duties 24/7, improving response times, ensuring critical security tasks don't fall through the cracks and improving user experience.
This is low-hanging fruit for organizations looking to boost efficiency. Deploying AI for routine help desk tasks enhances productivity and reduces human error, making it an obvious win for security teams.
4. Auto briefings and incident summarization
In today's fast-paced environment, security teams are often overwhelmed with data, from incident reports to threat intelligence and risk assessments. Writing detailed summaries can be time-consuming. AI can help automate the process, turning raw data into clear, concise reports.
With natural language processing (NLP), AI can translate complex, technical data into easily understandable content for non-technical stakeholders. Imagine your executive team receiving regular updates on the latest threats, vulnerabilities and emerging attack patterns — all thanks to AI-automated briefings. AI-driven cybersecurity tools can also help create templates for these reports aligned with industry standards, ensuring consistency and thoroughness.
By integrating AI for incident summarization, security leaders can reduce the time and effort spent on routine documentation and focus their attention where it matters most: Securing the organization against threats.
5. Threat hunting
Traditional threat hunting is labor-intensive, requiring cybersecurity analysts to sift through massive amounts of information from telemetry, threat intelligence feeds and historical attack data. AI takes this burden off the human analyst by analyzing security intelligence data at an unmatched scale and speed.
What makes AI particularly valuable here is its ability to "read" through threat feeds and external news, identify recent attacks on other organizations, extract indicators of compromise (IOCs) and then cross-check these IOCs against your own environment. It's like having a personal cybersecurity detective constantly on the lookout.
As this technology advances, the next logical step is automating the entire process. AI will not only identify risks but also develop queries into your organization's data lakes to pinpoint vulnerabilities, turning raw data into actionable insights. The result? AI will uncover threats faster than ever before, helping teams neutralize incidents before they escalate.
6. Incident management and automated detection
In a cybersecurity environment where every second counts, speed is critical during an active security incident. AI-driven cybersecurity systems are reshaping incident management by continuously monitoring network traffic and detecting unusual or malicious activities in real time.
Using contextual analytics, AI can add layers of understanding to security alerts by analyzing data from multiple sources — such as network traffic patterns, user behaviors and system logs. This makes it easier for teams to distinguish between real threats and false positives. AI reduces the chance of overlooking crucial incidents and allows for faster, more accurate responses.
Moreover, AI's predictive analytics capabilities mean that security teams can use historical data and trends to forecast potential security risks. This helps organizations prioritize proactive measures, improving overall security posture by catching issues before they escalate into full-blown incidents.
7. Policy management
Keeping security policies up-to-date is essential, but it's a complex and ongoing task. AI, particularly generative AI (GenAI) tools, can assist by drafting, validating and recommending security policies based on the organization's unique threat landscape, technology infrastructure and business goals.
GenAI can continuously monitor and recommend policy changes that align with best practices or new regulatory requirements. It can also validate existing policies against industry standards, ensuring no gaps exist in an organization's defense strategy. AI's ability to comb through vast datasets and detect vulnerabilities in real time makes it invaluable for efficient policy and vulnerability management.
Final thoughts
Artificial intelligence is not a silver bullet, but its applications in cybersecurity are vast and transformative. From automating reports and briefings to proactively hunting for threats and managing complex compliance tasks, AI is becoming an indispensable tool in every security leader's arsenal. By embracing these AI-driven cybersecurity use cases, organizations can improve the efficiency of their security operations, reduce human error, and stay ahead of the evolving threat landscape.
The future of cybersecurity is here, and AI is leading the charge. Will your organization be ready?