Threat actors have already adopted the latest AI technologies to enhance their cyberattack capabilities. Security practitioners need tools that respond at the same speed and breadth as the attackers. However, across disconnected tools and siloed data, many security operations centers (SOCs) find themselves manually fine-tuning their security tools and losing trust in automated response functionalities, leaving them at a continued and constant disadvantage.
Agentic AI systems bring autonomy, adaptability, and real-time decision-making to modern security operations. Designed to operate at machine speed, these models continuously ingest real-time data from across the interconnected environment, including security and business technologies. To gain the full benefit of agentic AI, security teams need technologies that integrate natively into their current security stack.
While SOCs need and want these integrations, many vendors fail to build individual cybersecurity integrations for each customer because the process is time-consuming and cost-ineffective. Customers may find themselves constrained by the integrations a vendor offers, forcing them to choose between a vendor’s current partners or leading to customer churn as they look for a better solution.
An Integration as a Service Platform (IPaaS) answers both these needs by providing standardized, normalized interfaces that reduce integration complexity. To optimize an agentic AI investment, organizations should consider a solution, like Synqly, that provides security integrations, ingests and normalizes data, and sends data to security tools to automate incident response activities.
What is Agentic AI?
Agentic AI is a model with situational awareness and a goal-directed focus that allows the model to act autonomously as it receives new information. While traditional AI models focus only on specific tasks, agentic AI adjusts and learns from new data. The goal-oriented learning models focus on the end results so that the models can adapt to new patterns in data more rapidly.
Agentic AI rests on a technical foundation consisting of:
- Reinforcement Learning: automatically refining actions by learning through trial and error
- Goal-oriented Frameworks: focusing on how to prioritize and execute multiple functions at once
- Adaptive Controls: automatically changing parameters in response to new data
What is the difference between traditional AI and Agentic AI?
Historically, people think about AI as analytics models that focus on specific but limited outputs. Agentic AI differs from these traditional models in three ways that provide value within the context of cybersecurity.
Autonomy
Traditional AI models focus on completing a single task or series of single tasks. Agentic AI is goal-oriented so it can manage and switch off between multiple tasks to achieve the end result, enabling it to complete large and small goals across long-term projects. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Situational awareness
Traditional AI models rely on constant, predictable environments which is why they often require human interaction for fine-tuning outputs. Agentic AI engages in real-time data processing to understand and track input changes, enabling it to make decisions in dynamic environments.
Adaptability
Traditional AI models are rule-based, meaning they only complete the tasks someone tells them to perform. Agentic AI can combine data with goal prioritization to adapt to an environment, enabling it to respond differently to the scenario within a different context.
Applications of Agentic AI in Cybersecurity
Since threat actors continuously evolve their attack methodologies, task-based traditional AI models become cumbersome, especially when the tools require people to manually fine-tune them. Agentic AI’s autonomy, situational awareness, and adaptability make it suitable for various cybersecurity use cases.
Threat detection
Threat actors often evade detection by hiding in normal network traffic or disguising themselves as legitimate users. Since agentic AI can manage and switch between different tasks, it can detect anomalies across multiple potential attack paths rather than the ones that a series of inflexible, chained detections would provide.
For example, as an attacker moves across systems and networks, the agentic AI can ingest new behavioral data to improve alerts by considering the user accounts or resources impacted. By adapting to changing attacker behaviors, the agentic AI reduces false positives and improves response times.
Automated incident response
A security orchestration and automated response (SOAR) solution reduces key metrics, like mean time to contain (MTTC) and mean time to respond (MTTR). However, many security teams find that the automated responses require implementation work to prevent future manual efforts, and even then, people still need to resolve unhandled alerts to make sense of them. As the agentic AI adapts to an environment, it uses this human response input to learn and improve its future response actions.
For example, the agentic AI can learn which network segments support critical applications and the different data flows across them to limit data transmissions more precisely when containing the threat. With agentic AI, security operations centers (SOCs) can build trustworthy automations because the agentic AI continuously and autonomously ingests and analyzes new environment information beyond what the operators coded.
Governance, Risk, and Compliance (GRC)
Similar to the threat detection capabilities, agentic AI’s ability to adapt and reprioritize actions based on new data provides value for compliance monitoring. When the agentic AI detects a compliance violation, it can automatically remediate the issue.
For example, many organizations have overly permissive firewall rules that create compliance risks. Agentic AI can detect and automatically tighten rules to maintain the principle of least privilege in zero trust network architectures.
Challenges Implementing Agentic AI for Cybersecurity
Agentic AI relies on clean, quality data that updates continuously, creating several challenges.
Disconnected Tools and Data Silos
To gain the full value of agentic AI, security teams need to integrate as many security tools as possible so the models have more data to ingest. However, organizations struggle to connect all their security technologies. In many cases, they need to rely on the security vendor to provide the integration. Without aggregating all security data, organizations will still have blind spots, even with agentic AI.
Diverse Data Formats
Different security solutions often use their own, proprietary formats that undermine agentic AI. While more data is always better, security teams often struggle to standardize log formats so they can use the data. For best results and insights, agentic AI models need quality, normalized data so that the analytics models can provide insights.
Vendor Lock-In
As organizations onboard new technologies, they often feel limited by their security vendors’ inability to integrate with one another. SOC teams want and need to use agentic AI, yet they find themselves limited by their providers’ lack of integration capabilities. This creates a tension for security vendors as they either need to build expensive integrations that remove developers from improving the core product or lose customers to competitors.
Time Consuming API Builds
Building and maintaining APIs is time-consuming and expensive, especially when security vendor developers need to learn the nuances of the security tool’s API. Additionally, they need to ensure security, availability, and reliability.
At a glance, the development and maintenance costs primarily come from:
- Code and security testing
- Running continuous quality assurance across the development process
- Deploying integration software in a lab environment
- Writing end-to-end tests against it
- Documenting the testing processes
- Managing the infrastructure
- Complying with data protection laws and frameworks
Synqly: The Integration Layer for Cybersecurity Agentic AI
To optimize agentic AI’s value, SOCs need a way to bring all their security tools together so that the model works with a robust dataset. However, for most organizations – both security vendors and their customers – building a new integration may be cost-prohibitive. Synqly responds to these challenges by offering an IPaaS purpose-built on a foundation of understanding how security tools and their integrations work.
Data Ingestion
The Synqly Multiplex Connector contains everything a customer needs to integrate a security tool into their security stack. Each connector contains:
- A set of APIs
- Language-specific software development kits (SDKs)
- Coding samples
- Testing options
- Documentation
The Synqly IPaaS solution for cybersecurity enables security vendors to meet their customers’ needs by rapidly onboarding new integrations across a diverse set of complementary vendors. For SOCs, these integrations provide a faster return on their security investment, reduce blindspots, and enable them to implement agentic AI models.
For security vendors, Synqly allows security vendors’ developers to focus on maturing their product’s core functions so that customers can more rapidly receive access to the security functionalities they want most. Further with native integrations, security vendors can use these native capabilities as a market differentiator without incurring the costs of building and maintaining them.
Context Normalization
Synqly’s IPaaS solution goes beyond making integrations simple. It parses and normalizes the ingested data using the following industry standards:
- Open Cybersecurity Schema Framework: to standardize log data for use in analytics models
- Structured Threat Information Expression (STIX™): to standardize cyber threat intelligence (CTI)
When organizations use Synqly’s integration platform, they have access to analytics ready, streaming data to optimize their agentic AI models for a more rapid return on investment.
Action Standardization
Synqly’s bi-directional cybersecurity integrations enable comprehensive interoperability across:
- SIEMs
- Ticketing and notification tools
- Vulnerability management solutions
- Identity and access management providers
- Asset management technologies
- Endpoint security solutions
With the ability to input, normalize, and output data across a complex security technology ecosystem, security teams can use Synqly’s normalized query language for trustworthy response automations, including:
- Forcing password resets
- Forcing re-authentication
- Enabling or disabling users
- Changing user groups
- Quarantining devices
Synqly + Agentic AI: Building Trust in Autonomous Systems
Agentic AI is the future of cybersecurity automation, enabling security teams to reduce the time spent fine-tuning their analytics and allowing them to build trustworthy response automations. With Synqly, security teams can build the cybersecurity technology stack that makes sense for their business needs while vendors can focus on meeting customer requirements.
Using Synqly as the foundation of an interconnected security solution, everyone – vendors and their customers – get the most value from their agentic AI because they remove integration complexity and standardize their ecosystem’s data.
Contact us today to see how to unlock the power of autonomous security with Synqly.
