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Artificial intelligence (AI) is revolutionizing every industry. The combination of AI and cybersecurity is a particularly potent one: cyber threats are growing more prevalent by the day, but so are the capabilities of AI, for better or worse.
Much like enterprise cybersecurity, the power of AI is multidimensional — hackers may use AI to launch more intense attacks, yet organizations may leverage AI to strengthen monitoring and pattern defense mechanisms. Whether or not organizations leverage its power, they must at least be made aware of AI’s capability, opportunity or otherwise.
This article explores the relationship between AI and cybersecurity:
Let’s get into it.
There’s plenty of talk about the future of AI, but how does AI work in cybersecurity strategy?
By leveraging machine learning algorithms, AI gives organizations vast amounts of data to analyze, enabling pattern identification that may indicate malicious activities. While cybersecurity threats constantly evolve, AI has tipped the scales for cyber defense, helping organizations adapt to attacks and learn in real time. Third party risk management, for example, can be enhanced by leveraging AI to vet and monitor the risks associated with vendors.
However, AI is like a Pandora’s box, extending great power to both sides of the cybersecurity spectrum. For hackers and malicious threats, AI is a testing and learning mechanism for skill advancement. Along with increased capacity for attacks, hackers may take advantage of sensitive data leveraged by LLMs (Large Language Models) to work around direct data security points.
The impact of AI on cybersecurity is two-fold, so how does AI introduce opportunities and risks on a tactical level?
AI can transform an organization’s entire cybersecurity posture. Through transformative threat detection to automated responses, AI technology bolsters cybersecurity into a more automated, self-improving function.
Cybersecurity teams can apply AI for predictive analytics, helping organizations identify and position against risks before they occur. Additionally, AI may monitor user behavior patterns to identify anomalies including malware, insider threats, malicious domain names, etc.
Automation is core to AI’s capabilities — from mundane security tasks to diverting network traffic, AI frees up time for cybersecurity professionals to take on more complex issues. This automation enhances organizational efficiency and reduces the chances of human error leading to cybersecurity vulnerabilities.
AI can power both internal and external access systems, updating faster and safer than manual-first systems. Many data breaches begin with unintentional access leaks — AI continuously monitors access control where human lag times compromise security.
Automatic response protocols can be built into AI and cybersecurity systems, recognizing and neutralizing botnets before damage is caused.
As a self-learning mechanism, AI continuously improves threat detection capabilities with every set of new data. AI can uncover trends, correlations, and security compromise points that might otherwise go unnoticed with human-only intervention.
AI solutions shift security postures from reactive to proactive. Via IBM’s Cost of a Data Breach Report, integrated AI security structures reduce the cost of a data breach by $3.05 million per breach, compared to organizations without AI. Cybersecurity powered by AI reduces financial losses by limiting cyber attack damage, system downtime, legal liabilities, and damage to organizational reputation.
AI can free up labor time associated with routine security tasks — complex threat hunting, analysis, strategic security planning, etc. — leading to higher-value production per hour.
As with any tool or capability, the opportunity in AI has a counter — cyberhackers, scammers, and even whole organizations can use AI and machine learning for harmful effects. AI may expose new vulnerabilities and compound the tactics used by malicious parties in a variety of ways:
The mass data computation ability of AI is a double-edged sword. Because AI models handle large amounts of data, it increases the chances any portion of that data is sensitive and valuable. This makes second party data — provided by organizations to LLMs — an attractive target for cybercriminals.
With the widespread adoption of AI and ML tools like OpenAI, Claude, Gemini, and Midjourney, hackers may write cloaked phishing emails, create deceptive images and words, gamify security authentication, and build undetected malware strains. The blurred line between cyber deception and reality makes cybersecurity more challenging, even for experienced IT professionals, as AI malpractice impacts customer trust.
AI multiplies both the quality and quantity of cybersecurity threats. For example, AI can surge a large network of bots to carry out malicious, human-like behavior through DDoS (distributed denial-of-service) and data capture campaigns. The popularity of this tactic is backed by data — via Imperva, 47.4% of internet traffic comes from bots, with 30% of this traffic being malicious.
Because AI programs adapt with large volumes of sensitive data, regulation is a huge question mark. Personal privacy data, like widely-used facial recognition and biometrics information, are vulnerable to AI-driven attacks. Organizations must ensure cybersecurity systems are well equipped, while not infringing on personal privacy or civil liberties.
AI learns from historical data, which may contain biases or incomplete information. As a result, this may generate false positives i.e. flagging legitimate activities as malicious. Conversely, the AI system may produce false negatives i.e. failing to identify threats. Data at scale comes with inaccuracies, and without fine-tuning, AI programs are at risk of self-destructive threats.
AI-optimized cybersecurity is like most systems — what you put in is what you get out. Organizations need an AI cybersecurity strategy, create an execution plan, and invest in collaborative AI enablement to make the technology efficacious. Here’s our four-step best practice model for AI-powered cybersecurity:
First, recognize where your security posture is at and what your goals with AI are. Where can AI best be leveraged for stronger data protection? Do you have the necessary resources: personnel, hardware, software, etc.? Incrementally stepping into AI is safer than haphazardly trying to go “all in” on a program your organization isn’t ready for.
Identify the guidelines needed to safeguard any sensitive information used to train AI programs. An AI protection strategy also outlines an ethical framework for AI and cybersecurity use — how will technology be used, when, and who is responsible for its integration, deployment, and ongoing management?
Next, AI and cybersecurity require a change management strategy. Concern about AI programs replacing jobs/labor can create friction and animosity if AI strategy isn’t appropriately communication. The cultural impact of AI on cybersecurity must not be ignored — how can you build a collaborative lens around AI, distinguishing human intuition and creativity as critical in the cybersecurity process?
Finally, AI-aware training programs ensure AI is leveraged effectively. Equipping cybersecurity teams with proper training ensures optimized use and informed decision making. Teams can then engage with AI to swiftly respond to cybersecurity threats and create a dynamic, stronger security posture.
Previously, we highlighted improvements in cybersecurity from AI, which lay the foundation for its current use cases. However, AI programs are only useful when taken beyond theory into application. Here are some AI in cybersecurity examples to understand where the technology has evolved to currently:
Threat detection: As the primary AI in cybersecurity example, models analyze network traffic and behavior to detect anomalies and threats. With ML algorithms, these systems continuously learn and adapt to evolving threats, enhancing your organization’s overall security posture.
Vulnerability management: AI algorithms can automate the scanning and monitoring of systems, AI helping security teams focus on addressing critical vulnerabilities. Proactively, cybersecurity is strengthened before attacks occur, and reactively, the window of opportunity for attackers is reduced.
Advanced malware detection: AI can detect malware, including zero-day attacks, through behavioral analysis, then leverage machine learning to identify and mitigate emerging threats before they’re deployed.
User authentication: AI uses behavior recognition to detect suspicious user activity and provide adaptive authentication. This prevents unauthorized access to sensitive systems and data, both internally and externally.
Predictive analytics: By processing vast amounts of data in real-time, AI programs can forecast potential cyber threats and trends, helping organizations stay ahead by strengthening their defenses proactively.
Automated incident response: AI can activate incident response protocols, including containment and mitigation, to minimize the frequency and impact of cybersecurity incidents. AI-driven response mechanisms reduce dwell time and limit potential damage.
Streamlined security review process: Outside the perspective of cybersecurity threats, a different workflow AI in cybersecurity example is worth mentioning. Security reviews are an ordinary step in any TPRM protocol, which involves exchanging relevant cybersecurity information to/from vendors. AI supports both buyers and vendors with security questionnaire automation and other relevant, manually-intensive data exchange processes.
As with all of these AI in cybersecurity examples, “more AI” is never the goal. Rather, solving problems, reducing uncertainty, and building security leverage are the desired outcomes, and AI continues to build a compelling foundation for these use cases.
The AI revolution will continue. Of course, there are as many uncertainties as there are guarantees with AI — the questions of regulation, ethicality, and risk are ongoing challenges to be addressed with AI in cybersecurity.
AI is a learning curve that will continue to enhance the cybersecurity landscape. It holds the potential to enhance cybersecurity systems, bolster the efficiency of IT teams, and dial in the efficiency of cybersecurity prevention. The future of cybersecurity is already here, helping organizations establish a robust cybersecurity defense and stay ahead of threats.
While not without its risks, AI is ushering in systems of automation and comprehensiveness never before seen in cybersecurity. The manual burden of security exchange processes is being reduced with calculated implementations of AI. Trust Centers, for example, leverage AI to automatically answer security questionnaires and reduce the total exchange time between technology sellers and buyers.
Whether working on the front lines to combat cybersecurity threats or retroactively saving your organization hundreds to thousands of hours in process automation, AI is not the future of cybersecurity — it’s the ever-growing present.
SafeBase is the leading Trust Center Platform designed for friction-free security reviews. With an enterprise-grade Trust Center, SafeBase automates the security review process and transforms how companies communicate their security and trust posture.
If you want to see how fast-growing companies like LinkedIn, Asana, and Jamf take back the time their teams spend on security questionnaires, create better buying experiences, and position security as the revenue-driver it is, schedule a demo.
