As artificial intelligence accelerates, it is transforming both cybersecurity defenses and threats. Organizations must understand the promise and risks of AI to implement robust security strategies. This article provides businesses and technology leaders an in-depth look at AI security, emerging attack vectors, defense strategies, and expert recommendations to stay protected in 2024 and beyond.
Introduction: The Dual Impact of AI on Cybersecurity
AI shows immense promise for transforming cybersecurity and risk management. By automating tasks, generating insights from massive datasets, and responding at machine speeds, AI enables organizations to analyze threats with unprecedented depth and speed. The global market for AI cybersecurity solutions will reach an estimated $46 billion by 2025, up from $8 billion in 2018.
However, AI also enables new attack vectors and more sophisticated cyber threats. Attackers are weaponizing AI with machine learning and natural language processing to create highly dynamic and personalized attacks that bypass conventional defenses.
This “dual-use” nature of AI will drive the cybersecurity arms race in 2024 and beyond. According to recent research, more than 90% of cybersecurity professionals anticipate AI-enabled cyberattacks. To stay ahead of threats, organizations must implement robust AI security strategies, not just AI-enabled defenses.
How Enterprises Can Apply AI to Strengthen Cyber Defenses
AI and machine learning provide many benefits for cybersecurity and risk management:
Earlier Threat Detection
By continuously monitoring networks, endpoints, servers, and data flows, AI security tools can detect subtle anomalies and signs of compromise much faster than human analysts. Darktrace reports its AI spots in-progress attacks an average of 10.5 hours after the initial breach. Early detection limits damage and recovery costs.
Rapid Attack Response
AI automation speeds investigation and reduces human latency in responding to incidents. Tools like DeepInstinct and SparkCognition contain initial threats in real-time by blocking connections, shutting down processes, password resets, and more. Humans then handle deeper investigation and recovery.
More Accurate Fraud Prevention
Whether spotting fake accounts, credential stuffing, or payment fraud, AI analyzes user patterns to accurately differentiate humans from bots. This is crucial as fraudsters deploy more sophisticated bots to evade rule-based security systems. DataVisor reports a 3-5X improvement in fraud detection over other methods.
Uncovering Hidden and Unknown Threats
Where rule-based tools only detect known attack patterns, AI security leverages deep learning and behavioral analysis to also identify novel threats, zero-day exploits, insider risks, and other blind spots.
Holistic Analysis of Security Risks
By correlating signals across tools, networks, clouds, and endpoints, AI provides integrated risk analysis and prioritizes response efforts for security teams. AI startup JupiterOne claims its platform enables clients to “see their entire digital environment as an interconnected asset graph.”
Key Areas to Apply AI-Enabled Cybersecurity
Every organization needs a strategy to harness AI for enhanced security and risk management. Here are some of the highest impact application areas:
Email Security
With 267 billion emails sent globally each day, the vast scale makes manual threat detection impossible. AI analyzes anomalous language patterns, metadata, attachments, and sender profiles to identify phishing lures, business email compromise scams, and targeted spear-phishing attacks.
Lead players: Ironscales, Abnormal Security, Vade Secure
Network Security
By continuously profiling network activity and users, unsupervised AI models detect novel anomalies indicative of malware, unauthorized access attempts, vulnerabilities, and insider threats.
Lead players: Darktrace, Vectra AI, ExtraHop
Endpoint Detection and Response
AI analyzes endpoint activity patterns and behaviors to identify compromised devices and malicious activity. AI tools can isolate endpoints, kill processes, and remediate threats without human intervention.
Lead players: CrowdStrike, SentinelOne, Cybereason
Identity and Access Management
AI strengthens identity and access controls by analyzing permissions, detecting suspicious access requests, and uncovering accounts compromised by attackers.
Lead players: Azure AD, ForgeRock, Ping Identity
Securing the Cloud
With enterprises rapidly migrating to the cloud, AI protects cloud environments by analyzing events and changes for misconfigurations, unauthorized access, compromised accounts, and malicious activity.
Lead players: Orca Security, Lacework, CloudKnox Security
The Rising Threat of AI-Driven Cyber Attacks
While defenders apply AI for good, attackers are also weaponizing AI to launch highly dynamic and evasive threats. The MITRE ATT&CK framework now includes AI as a tactic for evasion and exfiltration. Some of the rising AI attack vectors include:
Realistic Deepfakes for Social Engineering
Using generative adversarial networks (GANs), attackers can create fake audio, video, and images to impersonate employees and manipulate targets. Deepfakes could evade biometric authentication and enable “CEO fraud.”
AI-Generated Spear Phishing at Scale
Natural language AI can mass customize phishing emails for each recipient to improve response rates. An AI technique called GLTR (grokking legitimate by training on randomness) evaded 84% of phishing detection tools in tests by analytics firm Zix.
Poisoning and Pollution of Machine Learning
By subtly manipulating the training data or live inputs of AI security tools, attackers can degrade the accuracy of models and cause misclassifications or false negatives for malware and intrusions.
Hyper-Personalized Social Engineering
By scanning social media profiles, attackers can tailor psychological manipulation and social engineering attacks using intimate knowledge of targets’ personalities, relationships, interests, and vulnerabilities.
Continuously Evolving Malware
Instead of static code, adversarial AI can create polymorphic malware that continually adapts its code, evasion techniques, and delivery to avoid detection by security AI monitoring networks and endpoints.
Facing the rising threat of AI-enabled cyberattacks, defenders must fight AI with AI. Organizations need strategies to defend both their information networks and their security AI systems.
Best Practices for Securing AI Systems
As AI permeates cyber defenses, security teams must view their AI systems as a new attack surface to be robustly protected. Here are best practices to secure the full AI model lifecycle:
Sourcing Robust Training Data
Carefully curate training data and watch for poisoning attempts. Apply techniques like duplication, shuffling inputs, and retraining on new data to make manipulation more difficult.
Secure Development Processes
Follow standard secure code practices and access controls for AI model development. Conduct extensive input validation and simulation of adversarial conditions during testing.
Continuous Model Monitoring
Monitor models for decreasing accuracy or effectiveness that may indicate manipulation or drift. Re-validate models with known benign and malformed inputs.
Rigorous Model Maintenance
Update models frequently with new training data to adapt to evolving threats. Institute strong version control as models get retrained and refined.
Isolated Deployment
When possible, deploy AI security systems on isolated networks with restricted access to prevent potential data manipulation or model extraction.
Customized Pen Testing
Pressure test AI systems for vulnerabilities specific to machine learning, such as data poisoning, model extraction, and adversarial sample attacks.
Integrating robust protections into the AI model lifecycle will enable security teams to harness AI without increased risk.
Leading Companies Providing AI-Enabled Cybersecurity
Many security vendors now incorporate AI and machine learning into their platforms. Here are some of the top companies driving AI innovation for enhanced threat detection, intelligence, and response:
Darktrace – Leading AI cyber company using unsupervised learning to model normal “patterns of life” across cloud, network, and employees. Claims to detect threats within seconds.
SparkCognition – Focuses on applying NLP and predictive analytics for next-gen defense against malware, network threats, and vulnerabilities.
Vectra AI – Uses continuous behavioral analysis and threat modeling for rapid detection of hidden and unknown cyberattacks.
IBM – Acquires and develops AI capabilities like machine learning and NLP to enhance SIEM, SOAR, threat intel, and managed security services.
FireEye – Leverages machine learning across its Mandiant services, network/email security, forensics, and analysis platforms to detect advanced threats.
As AI-driven attacks grow more sophisticated, clients should look for providers with robust investment and expertise in leveraging artificial intelligence for cybersecurity and risk management.
The Road Ahead for Organizations and AI Security
With AI set to revolutionize both cyber threats and defenses, organizations must develop strategies that encompass both AI-enabled protection and securing their own machine learning systems. Some recommendations include:
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Continuously monitor cyber intelligence for new AI attack vectors and threats as they emerge
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Pressure test internal networks with AI-powered penetration testing
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Prioritize AI cybersecurity vendors for their expertise in data science and algorithms
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Develop policies and controls specifically for procurement, development, and maintenance of AI security tools
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Train security teams on AI fundamentals, adversarial techniques, and best practices
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Monitor model performance for signs of manipulation or decreased accuracy
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Institute controls and code safeguards during in-house AI development
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Isolate and air-gap AI security systems when possible
Cybersecurity will continue getting faster, more sophisticated, and more machine-driven. By combining responsive defense and proactive protection of their own AI systems, organizations can leverage artificial intelligence for enhanced threat detection, intelligence, and response while minimizing risks.
For additional resources on securing your organization with artificial intelligence, see:
AI and Machine Learning for Information Security