Defending against attacks that bypass prevention measures requires an additional layer of detection. Deception technology can help by deploying attractive bait in the form of fake assets such as credentials, systems, and services on network endpoints. Attempts to interact with these deceitful resources generate alerts and threat intelligence so security teams can take targeted countermeasures. This significantly reduces false positives and enables teams to engage attackers more quickly, Lets talk about the Benefits of Deception-Based Breach Software in Preventing Cyber Attacks
Maximize Security Resources
Attackers rely on tactics to evade traditional security systems and successfully gain inside the network to complete their objectives. These tactics involve steps from initial compromise through the persistence cycle of privilege escalation, internal reconnaissance and lateral movement. Deception-based breach software can detect these steps and prevent attackers from leaving your data or tampering with business processes and systems. To see an attacker, deception technology deploys a virtual environment miming real IT infrastructure, such as servers, desktops, files, and credentials. Defenders can observe an attacker’s activities in this environment to determine their TTPs and intentions.
Unlike traditional intrusion detection/prevention solutions that rely on signatures and susceptible machine learning algorithms to detect attacks, cyber deception creates threat intelligence by engaging attackers in the deception environment to identify their TTPs, keep them occupied, and reroute them. This enables security teams to focus on detecting and responding to true threats, not false alerts. To deliver the highest value, deception should be deployed across the entire enterprise network – from legacy systems to IoT devices to cloud services – and managed from a centralized console. This will enable the deception assets to be triggered by specific actions taken by attackers, ensuring that the alerts are meaningful and do not generate noise or lead to defender fatigue.
Reduce False Positive Alerts
In a typical network, multiple security point products can produce a high volume of alerts that IT teams must deal with. These false positives cripple productivity and drag IT and security teams through convoluted triage workflows. In addition, the time spent validating an alert could be better spent engaging a bad actor and detecting a breach. Deception technology can reduce the number and severity of false positives by enabling security teams to focus on the attacker and thwart their activities. Unlike most behavior analysis systems that use machine learning to flag anomalies from a baseline, which tends to create false positives, deception uses techniques designed to mimic an attacker’s activity and identify them in real-time. This enables IT teams to respond more quickly and effectively while eliminating a significant amount of the noise from normal operations that would otherwise drown out the detection of real threats. Most participants felt that organizations should start by performing a needs analysis and identifying which gaps the technology can fill to get the most out of deception deployments. They must also be prepared for a leap of faith, as deception is not just another technology but a change in how their SOC operates. This includes shifting their risk appetite to allow for the more frequent occurrence of low-confidence alerts.
Minimize Dwell Time on Networks
During the initial survey and lateral movement phases, attackers spend the most time in your network undetected. The typical security controls in use — firewalls, antivirus, and intrusion detection/prevention systems — cannot stop this persistence cycle, which involves internal reconnaissance, privilege escalation, and access to critical data. Deception technology can, however. By tricking adversaries into a fake IT environment, deception technologies enable security teams to systematically observe their attacks and learn about the tools, techniques, and procedures that their adversaries are using. In the case of deception-based breach software, this is done by deploying deceptive servers and endpoints, files, credentials, and services throughout your enterprise that look similar to real assets. When an attacker interacts with these decoys, a high-fidelity alert is generated, and the attacker’s activity is recorded on a centralized engagement server. This forensic recording can reveal the attack vectors used, and the threat intelligence captured can help close detection gaps, strengthen existing protections, and thwart future attacks. In addition, deception technology can be positioned across your entire enterprise, including in the most difficult-to-reach areas of your network, like SCADA/ICS, IoT devices, and applications. This broad deployment, combined with centralized management of all the deceptive assets from a single console, puts the burden of success on attackers. They must execute a flawless attack to bypass the deceptions and traps placed in their way.
Detect and Prevent Lateral Movement
Deception enables security teams to quickly and accurately detect cyberattacks by populating the network with fake assets that attackers must interact with to continue their attack. This approach prevents them from triggering false alarms, which has become essential to many organizations’ cybersecurity solutions. It also enables them to reduce the time between detecting a threat and taking effective countermeasures, known as the response gap. When a company is a victim of a breach, the time it takes to identify and analyze the incident and then take action against it can cost millions in lost revenue or reputation. By deploying digital breadcrumbs, including decoys and traps, that mimic legitimate IT, cloud, and IoT systems, deception technologies can catch adversaries moving laterally within the network, stealing stolen or guessed credentials, and exfiltrating data. The ability to deceive and direct attackers away from key data and systems reduces their time on the network. It limits what they can steal, ultimately slowing or stopping them altogether. Unlike point solutions that rely on static signatures and susceptible machine learning algorithms to detect anomalies, deception technology uses a proactive and low-false positive detection model, reducing the number of alerts and making it easy for IT to prioritize threats. It also complements security orchestration, automation, and response (SOAR) tools by providing threat intelligence created by bad actors.