Today’s developers face myriad challenges. On the one hand, today’s threat landscape continues to evolve, with new threats becoming more targeted and sophisticated. On the other hand, the speed of innovation has accelerated to facilitate the ongoing adoption of remote work, cloud-based tools and personal devices, creating more endpoints on an already rapidly expanding attack surface. Because of this, scaling startups and rapidly growing companies are overwhelmed on a daily basis with petabytes of data generated from local networks, cloud-based systems, IoT devices, endpoints, databases and server loads. At the same time, buried within some of these points are nearly untraceable data footprints left behind by threat actors that can open startups up to new vulnerabilities.

In today’s macroeconomic climate, startups are pressured to do more with less. As a result, they want to leverage new processes and technologies to streamline operations and address their needs but may be unsure of where to start. Luckily, AI and ML-powered capabilities can provide them with a key advantage in securing their valuable infrastructure, data, and employees. In fact, a recent survey from Palo Alto Networks found that 49% of C-suite leaders believe AI has the highest impact on security in the realm of more effective threat detection, with 62% saying their startup will invest in AI this year.

Advancements in AI/ML bolster businesses’ ability to keep up with massive amounts of data, minimize risk, keep assets secure and better understand what needs to be protected amid a continuously evolving threat landscape. Here are three actionable ways startups and developers can realistically implement AI and automation to secure as they scale.

Strengthening Modern SOCs Through AI and Consolidation

As the threat landscape becomes more complex and a startup’s attack surface grows, security teams, including SOC analysts, can no longer keep up with the intensive security demands caused by the massive influx of data. Right now, we are relying too much on analysts to collect, log and index data. This reliance sets them up for failure as data processing can be an extremely time-consuming and labor-intensive process.

Additionally, startups have historically relied on adopting ad hoc security solutions to address the many challenges of the ever-evolving threat landscape. However, this tool sprawl can also create more security gaps and complexity that businesses find challenging to manage. In one report, 80% of respondents said they would benefit from a centralized security solution across all their cloud accounts and services — indicating that now more than ever, startups are looking to consolidate services for effective security protection through one solution.

Luckily, AI and automation can be leveraged to modernize the SOC and alleviate the pressures caused by mass amounts of data and the adoption of varied security solutions. Security platforms with built-in AI capabilities enable SOCs to quickly identify the gaps and address security weaknesses before they become enterprise-wide threats. And as more data comes in, AI algorithms and tools can become even more efficient and accurate when detecting cyber weaknesses and reducing response time.

Adopting and Integrating a Shift Left Security Approach for Developers

AI and ML technologies are not standalone technologies but enablers that bring value to security processes and operations for them to be effective. In one survey, they found the top concern for C-Suite leaders is securing data in application security. To strengthen our tools and capabilities, we must collaborate with developers to build with security in mind while also integrating technologies like AI to protect and utilize the data to identify those gaps by providing tools that do not hinder the design process.

AI is not a new concept to the security industry; however, in these contexts, AI can be positioned as a new opportunity for developers to utilize its features to ensure any code that the AI was used to generate can be deployed safely and maintain those guardrails as early in the development process as possible. However, we know in scarce startup environments, AI and ML can be the key to scalability where initially, teams are constrained to work with limited resources while developing new possibilities to address old and new challenges. By leveraging these technologies, startups can improve automation and remove manual processes across security operations to prevent risks from becoming large-scale security incidents.

Leading the Way for Threat Detection and Response

Unlike humans, AI and automation technology can scan a startup’s entire infrastructure – 24×7 – across numerous networks, endpoints and cloud applications to identify abnormal behavior in near real-time. It can prioritize potential threats using sophisticated algorithms, vastly accelerating security analysts’ decision-making processes. Emerging security innovations like extended detection and response have developed to enable ML-based detection models across all data, not just partial data sources, providing a level of analysis to identify and respond to threats faster than ever.

Often, threat actors circumvent signature-based classification techniques, which is how most security teams detect known threats, by using AI to launch zero-day attacks. There’s no way to determine these attacks without behavioral analytics. Humans simply cannot match the computing power that AI technologies bring to scan for unknown threats, assess patterns, and decide whether they are harmful or benign.

AI and Automation Will Provide Startups With Power and Focus

AI and automation alone are not the answer nor the differentiator for security. The differentiator is how they are used and the available data for them to learn from. Similar to how we approach building an autonomous car, we must reimagine the foundation of how we use AI to mitigate attacks and solve age-old problems. Data is the heart that powers the machine, and it needs consistent output to efficiently power and enable AI models. To enable strong security capabilities, startups must take action to prepare data properly to get the most out of their AI models and tools.

AI and automation will continue to be leveraged in new ways to improve security outcomes and processes – from visibility, detection and vulnerability management to actionable items like policy recommendations. Ultimately, AI will provide startups with the power and focus on outmaneuvering threat actors with the same tools, strengthening overall security posture in the future.