So often, cybersecurity conversations turn inward. Sure, managing the immense data spewing from the SIEM and other tools, the ever-sprawling cloud environment, the shadowy unmanaged endpoints out there in the ether gives security a full plate. Doing so certainly creates a formidable security posture that will deter many attackers.
However, as highly resourced threat groups shift their angles of attack and experiment with new AI-driven tactics, external cyber threat intelligence becomes a key ingredient to a proactive security function.
The game is changing fast. Organizations must stay aware of changes in the threat landscape and incorporate those shifts into their processes — or risk falling victim to more subtle, more disruptive attack patterns.
The Role of Cyber Threat Intelligence (CTI)
Cyber threat Intelligence is the collection and analysis of threat data to anticipate and mitigate cyber attacks. This threat data can come from numerous sources:
- Government agencies and international organizations like the National Security Agency (NSA) and INTERPOL
- Commercial providers of cyber threat intelligence like Palo Alto Networks and Mandiant
- Information sharing and analysis centers (ISACs) dedicated to specific industries like finance, healthcare, and local governments
- Open-source threat intelligence (OSINT) platforms like MITRE ATT&CK and Shodan
- Dark web and cybercrime intelligence firms like Intel 471 and DarkOwl
Cyber threat intelligence comes in a few different types.
Strategic Intelligence
Strategic Intelligence (STI) focuses on long-term trends, geopolitical risks, and high-level insights. This type of intelligence speaks to organizational leaders, CISOs, and policymakers. STI is the latest high-level big picture of what threat actors are doing.
For example, a government agency like CISA may produce a report profiling ransomware attacks aimed at hospitals using phishing tactics. Or a commercial firm like Palo Alto Networks may observe a trend of nation-state actors working to get hired on as in-house IT personnel.
Tactical Intelligence
Tactical intelligence (TTI) focuses on the latest tactics, techniques, and procedures (TTPs). These are the technical methods by which threat actors carry out their threats. While Strategic Intelligence indicates what threat actors are doing, Tactical Intelligence shows how they are doing it.
For example, MITRE may produce a study exploring an urgent zero-day vulnerability in a common software update. DarkOwl may find that threat actors are more often buying stolen credentials from online blackmarkets, instead of stealing credentials directly from users.
Operational Intelligence
Operational Intelligence (OTI) provides immediate, actionable intelligence on specific threats, malware, and indicators of compromise. This info helps incident respondents and security teams mitigate active attacks that are happening in real-time. Such data could include specific malicious IP addresses, file hashes, domains, and malware signatures.
For example, Mandiant may pinpoint malicious IP addresses or file hashes linked to a specific, ongoing botnet attack.
Challenges in CTI
Incorporating cyber threat intelligence into your organization’s security apparatus can be more complicated than it may seem.
- Security teams already struggle with an overwhelming volume of internal alerts and data. Adding yet another stream of data to the mix may exacerbate this challenge.
- CTI feeds may include personal identifiers that could raise flags with data privacy laws like Europe’s GDPR or California’s CCPA. The international nature of threat intelligence reporting may raise additional compliance issues as this data is transferred across regional lines.
- The threat landscape often changes faster than organizations can adapt their processes, leaving the good guys perpetually one step behind. It can be cumbersome to implement new static correlation rules and meticulous firewall updates to defend against the latest threats – at least when these tasks are done manually.
Until recently, the cybersecurity world has struggled to answer these challenges. But the advent of defensive AI finally gives defenders the tools they need to get ahead of threats and take full advantage of their security data, including CTI.
AI’s Expanding Role in Cybersecurity
Improved Threat Detection
- Quantum machine learning (QML) uses quantum computing for faster threat analysis and advanced threat detection.
- Predictive threat intelligence uses AI driven behavioral models to anticipate future cyberattacks.
- Digital twin simulations allow teams to test attack scenarios and validate security controls without impacting the live production environment.
AI-Driven Incident Response and Adaptive Cyber Defense
- Self-healing systems use AI to automatically update cybersecurity controls based on previous attacks and CTI.
- Autonomous response systems use AI to take several response actions (like blocking malicious IP addresses or revoking compromised credentials) before an analyst has to get involved.
- Generative adversarial networks (GANs) produce AI-powered simulations that security teams can practice against.
AI-Enhanced Digital Forensics and Investigations
- AI-powered digital forensics speeds up post-incident investigations by painting a full picture of the incident and performing root cause analysis.
- Cognitive SOCs (Security Operations Centers) use machine learning-driven behavioral profiling to detect subtle attack patterns.
- Neuro-symbolic AI combines logic-based reasoning wiht machine learning for deeper threat analysis.
The Future of AI in Cybersecurity
As attackers and defenders find new ways to use AI in order to outpace, outwit, and outperform each other, the threat landscape will continue to fluctuate. The AI arms race will only escalate.
Governments and organizations will rush to apply AI to new contexts and use cases to work faster and decrease complexity. As AI makes its way into fresh industries, often with transformative effects, security gaps will emerge along with new innovations.
As AI ushers in new opportunities in conjunction with new risks, regulatory bodies will work to formulate balanced AI policies that mitigate risk without stifling innovation. New policies may take aim at anything from protecting personal data to securing industrial systems. Whatever the future holds, cybersecurity will become even more of a team sport. Policy makers, AI researchers, cyberintelligence firms, and commercial business leaders must collaborate to maximize the incredible benefits of this technology, while minimizing the substantial danger that comes with it.
