IBM’s Cost of a Data Breach Report 2024 reveals a groundbreaking discovery: The implementation of AI-driven automation in prevention has resulted in an average savings of $2.2 million for organizations.
Businesses have been utilizing AI for detection, inquiry, and response for several years. Nevertheless, with the expansion of attack surfaces, security leaders must embrace a more forward-thinking approach.
Here are three paths through which AI is contributing to achieving this objective:
1. Proactive Defense with AI in Attack Surface Management
The increasing complexity and interconnection pose a growing challenge for security teams, as attack surfaces extend beyond what they can oversee manually. As organizations enhance their multi-cloud strategies and incorporate new SaaS tools and third-party code in software development and deployment, the complexity escalates.
Expanding attack surfaces introduce a higher complexity in network interactions and numerous potential entry points for adversaries to exploit. Attack surface management (ASM) introduces AI-driven, real-time safeguards for digital infrastructures, irrespective of their underlying complexity.
Automated ASM significantly enhances manual auditing by offering comprehensive visibility into attack surfaces. Additionally, AI utilizes the monitored data to enhance future detection outcomes, surpassing the speed and scale achievable by humans alone.
While ASM tools are typically portrayed as ready-made solutions and relatively easy to implement, the capability of security teams to interpret the vast volume of generated data is crucial for optimizing their effectiveness.
Explore the 2024 Cost of a Data Breach report
2. AI-Enabled Red Teaming: Taking an Offensive Approach
AI red teaming involves evaluating AI models for potential vulnerabilities and other concerns, such as bias and misinformation. While most models incorporate safeguards to mitigate these risks, attackers persistently attempt to breach them through innovative techniques. Red teams aim to identify these vulnerabilities before adversaries, enabling corrective actions to be taken.
Red teams can utilize AI to pinpoint issues in the data used for training AI models. For example, a significant portion of data breaches are associated with shadow data. If unverified and unmonitored data is utilized in model training, it can lead to significant consequences. AI aids red teams in uncovering shadow data by spotting irregularities and overlooked data sources that may pose security risks. Red teams can also evaluate AI models against each other using adversarial machine learning methods to identify vulnerabilities.
Unlike ASM, red teaming involves custom simulations tailored to the organization’s data and threat landscape. To fully leverage its benefits, organizations need to collaborate with proficient teams capable of accurately interpreting and analyzing results, implementing necessary changes.
3. Ensuring Continuous Security at Scale with Posture Management
Posture management leverages AI’s scalable, real-time monitoring capabilities effectively. While ASM uncovers potential vulnerabilities in attack surfaces, posture management adopts a broader approach by monitoring configurations, adherence to security protocols, and connections between internal and external systems in a continuous, flexible, and adaptable manner.
By automating posture management with AI, security teams can swiftly mitigate risks across intricate multi-cloud infrastructures, ensuring consistent security practices throughout. Additionally, the reduced reliance on manual procedures significantly diminishes the likelihood of human errors.
In instances of breaches, organizations extensively incorporating AI and automation in their posture management strategies can detect and address them almost 100 days quicker than those not leveraging AI at all. The time saved in both prevention and resolution translates into substantial direct and indirect cost savings as well.
AI – Transforming the Landscape, Yet Recognizing the Human Role
The potential of AI in cybersecurity is undeniable. It not only facilitates scaling strategies in increasingly complex environments but also democratizes security by enabling less experienced analysts to interact with security systems using natural language queries.
It’s important to note that AI isn’t intended as a replacement for human expertise but rather as a complementary aspect.
The integration of AI and automation in security has enabled organizations to reduce potential damages and remediation costs significantly; however, individuals are still required to interpret the data and insights provided by AI to maximize its impact.
Managed security services play an increasingly pivotal role in ensuring that the strategic alignment of AI adoption with business objectives is prioritized, rather than simply focusing on cost and labor reduction.