A comprehensive strategy for extracting insights from customer data can help a company overcome the difficulties of working with big data and utilizing knowledge that can increase profitability.
A security team’s most fundamental task is to prevent attackers from accessing networks and safeguard data. Early threat detection is essential to classify, neutralize and reduce the effects of a breach once it has occurred.
Numerous “perimeter” tools are available that perform some or all of the following early-detection tasks: endpoint detection and analysis, antivirus and anti-malware control, network traffic analysis, email filtering, and access control. These tools deliver good network and data protection both separately and collectively. The issue is that they frequently work independently of one another and store their data in compartments.
Actionable threat intelligence is knowledge based on facts used to make good decisions about responding to specific threats. It contains contexts, mechanisms, indicators, repercussions, and action-oriented recommendations regarding threats.
Putting raw data through multiple distillation and personalization processes transforms it into actionable threat intelligence, enabling security teams to mitigate pertinent risks and thwart attacks.
How actionable threat intelligence benefits organizations
Based on valuable insights and actionable threat intelligence, organizations can take more concrete measures against threats. Organizations can benefit from it in various ways, such as:
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- Improves the visibility and context of specific attacks: With actionable threat intelligence, security teams can act swiftly to thwart attacks by blocking their sources before the situation deteriorates.
- Releases time and assets: Since computers perform a significant portion of data collection, processing, and contextualization, security teams can concentrate on tasks machines cannot achieve.
- Compatible with current technology stacks: APIs immediately deliver actionable threat intelligence to most security technology stacks, including threat intelligence platforms and other solutions (e.g., SIEM and SOAR).
- Offers a clear path to correction: Actionable threat intelligence, which includes concrete plans of action, provides security teams with efficient and straightforward processes to stop threats, shut down fake accounts and malicious domains, and quickly notify incident response teams about indicators of compromise (IoCs)
Advanced Threat management strategies
Organizations frequently take the approach of trying to patch each vulnerability found when it comes to vulnerability management. The process takes time and can be quite counterproductive because dealing with vulnerabilities according to risk is easier than patching indiscriminately.
To effectively combine internal vulnerability scan results with external data, actionable threat intelligence is used. This intelligence also provides context by revealing the methods, tactics, and techniques used by attackers.
Intelligence gathering is a thriving — and essential — component of today’s information security programs, whether mapping out an enterprise network or evaluating how well security tools protect devices and systems. For the benefit of security teams, intelligence-gathering techniques have advanced.
Automation is increasingly used in actionable threat intelligence to quickly identify threats and even stop them before the systems’ human counterparts are aware of an occurrence. Security teams must stay current on cyber-threat intelligence techniques to protect their systems.
Threat intelligence and machine learning to supplement and improve threat hunting
For fear of getting false positives, most people avoid using AI threat intelligence that employs machine learning to spot cyberattacks. In some contexts, that makes sense, but not in others. The most recent attacks might not follow the patterns that older detection methods typically look for, making them more likely to miss them.
Security teams can use machine learning to find the most recent attacks, but false positive rates may be higher. More reliance on automation using machine learning may make sense to protect those assets if missing attacks are a bigger concern than the resources required to look into additional false positives.
For some purposes, many organizations will find it best to use threat intelligence without machine learning and, for other uses, machine learning-generated insights. For instance, threat hunters may use ML to receive recommendations for things to look into that they otherwise would not have been able to find in sizable threat intelligence data sets.
Additionally, reports from threat intelligence services can give threat hunters invaluable information on the most recent threats. These insights frequently incorporate elements that are difficult to automate into things that machine learning can handle.
Addressing network vulnerabilities as an anti-malware solution
Malware that targets point-of-sale (POS) systems travels between systems and looks for moments of vulnerability throughout a transaction. More online fraud can occur if evidence of widespread malware targeting POS retailers goes undetected. DNS cache poisoning, a method used by malware to target the banking industry, modifies DNS settings to redirect users looking for a legitimate banking website to a fake site. Poisoning the DNS cache is a potent attack.
A threat that is quickly spreading and used in extortion schemes is ransomware. By encrypting files and requesting a ransom payment, the malware locks systems and prevents access to administrative and system-related features.
Actionable threat intelligence has been providing value to enterprises, and this value is only growing. It has also gained prominence as the shift to hybrid work models and digital transformation have increased the attack surface and raised the stakes for defenders to safeguard sensitive data and critical infrastructure. Recent surveys confirm the importance organizations attach to threat intelligence, but they also point out difficulties in turning threat intelligence into action.
Treat intelligence and automation
Making threat intelligence actionable can be aided by automation. Automation alone won’t make threat intelligence actionable; contextualization and prioritization are also necessary so that IT teams can automate and act on the appropriate data at the appropriate time. Actionable intelligence refers to follow-up information, implying the need for a strategic plan to use the information gathered effectively.
This interpretation differs from the use of actionable intelligence in a legal context, where that term refers to information that satisfies the legal standards for a valid (actionable) lawsuit. Actionable intelligence, or actionable insight, is frequently mentioned concerning big data and predictive modelling in information technology (IT). By incorporating intelligence into customer relationship management (CRM), marketing automation, and other operational tools, artificial intelligence (AI) can assist businesses in reducing the gap between customer intent data and actionable insight.
This comprehensive strategy for extracting insights from customer data can help a company overcome the difficulties of working with big data and utilizing knowledge that can increase profitability.
Also Read: Emerging Web 3.0 Security Threats Every Digital Business Need to Identify
Utilizing Current Threat Intelligence
A conventional, check-the-box mentality cannot defend against today’s dynamic and well-resourced adversaries. Outwardly confident, security teams frequently struggle to keep up with the rapidly shifting threat landscape. They desire information that can be implemented across their organization. Security teams are concerned that senior leaders do not fully comprehend the threat’s nature. This concern indicates that senior cybersecurity leaders make decisions without knowing the adversary’s tactics.
This year, several high-profile breaches have made security professionals more aware than ever of the need for improved security procedures. The sheer volume of information is one of the greatest obstacles to constructing stronger defences; organizations must develop more effective strategies for implementing intelligence to regain much-needed focus and establish clear priorities. Organizations must take the initiative, which can only be done by knowing their adversaries, acting quickly to implement changes, and ensuring that cyber-risks are effectively communicated to all stakeholders.
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