Keeping up with Gen V cyber threats requires a consolidated security architecture that combines best-of-breed point products with seamless integration. Otherwise, an organization risks losing visibility and protection.
From hackers looking to make a profit to nation-states seeking to disrupt economies and cause bodily harm, threat actors are always searching for opportunities. To combat this, a new generation of security solutions is evolving.
Historically, SOC teams have been required to address threats manually. This involved investigating many alerts to identify potential threats, enriching them with threat intelligence, and deciding what action to take. This often caused “alert fatigue” and made it challenging to prioritize incidents or determine whether a specific alert was relevant.
Security automation reduces these issues by removing the time-consuming and repetitive tasks from SOC teams. This allows teams to better focus on high-priority issues and ensures that threats are detected, contained, and resolved quickly.
The goal of security automation should be to support and complement human capabilities rather than replace them. Therefore, implementing security automation in a controlled and gradual way with the help of an experienced technology company like Arctic IT is essential to success. For example, documenting and transferring all manual processes into automated workflows will help ensure the right balance between security automation and human intervention. A gradual approach also allows for practical evaluation and adjustment of security automation over time.
Before AI, cybersecurity primarily relied on signature-based detection systems that compared incoming network traffic to a database of known threats. If a threat were detected, it would generate an alert and take steps to block or contain the identified attack. This approach could have been more effective against unknown threats and prone to developing high numbers of false positives that consumed valuable human resources.
AI-powered security solutions can detect threats and suspicious patterns using real-time data analysis, enabling them to identify and respond to them before they can cause significant damage. This allows information security teams to reduce breach risks and improve their overall cybersecurity posture.
However, like humans, AI is prone to bias that can impact cybersecurity decision-making and result in discriminatory outcomes. As a result, AI-based solutions must be trained with unbiased and diverse data to avoid biased outcomes and ensure accurate threat assessment. This is critical to reducing human error and ensuring optimal security outcomes.
In addition to firewalls and anti-malware, a solid security foundation includes various tools that analyze data at rest and in transit. Data Loss Prevention (DLP) solutions scan and secure data using content, context, and user behaviour analysis, while Managed Detection and Response (MDR) services offer around-the-clock real-time monitoring.
Threat actors have various motivations, from gaining attention to making money. Malware attacks are increasingly automated, while DDoS attacks can thwart or slow business operations or even shut down a service entirely. Nation-state actors also use software and service supply chain attacks, leveraging vulnerabilities to infiltrate environments.
To combat all these threats, an agency requires a robust cybersecurity infrastructure with a security nerve centre that connects all its technologies on one platform. This allows security teams to better identify and respond quickly to advanced threats. These security systems must be able to identify and address cyber threats at the inception phase when attackers gain entry into networks by exploiting vulnerabilities.
Cyberattacks will continue to get more sophisticated and evolve quickly, so human intuition and creativity will be essential in cybersecurity solutions. As such, humans will augment a variety of software types to complete the tasks needed for a robust cybersecurity system.
For example, traditional malware detection systems rely on signature-based technology that compares an unknown file to a database of known malware signatures. This method is effective against known threats, but cybercriminals constantly modify their attacks to evade detection. AI-based malware detection solutions can spot these new and unidentified threats by analyzing behavior patterns and spotting anomalies.
Another example is HUMINT, which involves infiltrating underground crime networks and threat actor communities to gather information about adversaries’ ecosystems, capabilities, and motivations. This information can then be used to identify risks and thwart attack methods before they are carried out. HUMINT also helps validate data gathered by automated intelligence tools, which may have missed a threat due to its obfuscation or malicious exploitation techniques.