Hybrid and Post-Quantum Computing
AI-Driven Security: How TSPs are using AI for proactive monitoring and intelligent threat response
The arms race between attackers and defenders has never been more asymmetric. In 2024 alone, global cybercrime costs surpassed $10.5 trillion annually, a figure projected by Cybersecurity Ventures to rise steadily through the decade. Ransomware dwell times have shortened to hours, not days, while phishing campaigns now use AI-generated text and cloned voices indistinguishable from the real thing. The speed and sophistication of threats have outpaced the capacity of human analysts, leaving traditional models of security dangerously exposed
This reality is forcing Technology Success Partners (TSPs) to abandon a reactive posture and embrace a new paradigm: AI-driven security, where proactive monitoring and intelligent response are embedded into the very fabric of the technology used in business environments.
From mainframes to machine learning: a brief lineage
Cyber defence has always evolved in step with computing. Mainframe security was physical. The client–server era brought firewalls and antivirus signatures. Cloud adoption demanded identity-centric controls. Each transition widened the attack surface and demanded a new defensive logic.
Artificial intelligence is not simply the next increment; it is a structural inflection. Unlike previous waves, AI is not just another tool in the security stack, it is increasingly the operating principle. According to Gartner, by 2026 over 60% of enterprises will use AI-enabled security tools as their primary defence, compared with less than 20% in 2022
Why reactive security is no longer viable
The mean time to identify and contain a breach in 2023 was 277 days, according to IBM’s annual Cost of a Data Breach report. Iin financial terms, every extra day equates to millions in potential damages. This latency is not a failure of effort; it is a failure of model. Reactive playbooks assume time is elastic, when in fact adversaries compress it ruthlessly.
AI changes the equation by ingesting and correlating vast telemetry: anomalous logins, subtle endpoint behaviours, unusual API calls. Machine learning can stitch these signals into actionable alerts at machine speed. What was invisible to a human SOC analyst becomes obvious to an AI trained on billions of events.
Proactive monitoring in practice
Proactive monitoring is not just about raising alerts faster; it is about predictive posture. AI systems can learn the normal cadence of an organisation, when employees log in, how data flows across geographies, which APIs are most active and flag deviations that precede attacks.
For example, Microsoft’s Security Copilot has demonstrated the ability to detect lateral movement attempts within minutes, tasks that previously consumed entire analyst shifts. Instead of waiting for signatures, AI observes behaviour. The difference is not quantitative; it is qualitative, shifting security from an ambulance service to an immune system.
Intelligent Threat response: autonomy with oversight
Detection without response is paralysis. Here, the rise of AI agents is critical. These autonomous routines can isolate compromised devices, revoke suspicious tokens, and even generate compliance logs without waiting for human intervention.
The power and the risk lie in their autonomy. False positives could lock out critical systems; bias in training data might miss novel attacks. That is why leading TSPs combine automation with governed human oversight. The model is “autonomy with brakes”: AI acts in seconds, humans audit in minutes.
The limitations and risks of AI defence
To pretend AI is a silver bullet is naïve. Models inherit bias from training data. Attackers are already using adversarial inputs to “trick” detection systems. The energy cost of large-scale inference also raises sustainability questions: one study estimated that a single AI model trained for intrusion detection consumed the equivalent power of 130 US households for a year.
Moreover, the regulatory environment is hardening. The EU AI Act places cybersecurity systems under its “high-risk” category, demanding transparency, auditability and accountability. For TSPs, compliance is not a box-tick, it is an existential requirement.
The TSP advantage: Beyond MSP
Traditional Managed Service Providers (MSPs) have long sold monitoring and patch management. But in the age of AI-driven threats, clients require more than upkeep, they require strategic security partnership. That is the TSP’s remit.
A TSP does not merely deploy tools; it integrates them with governance, business priorities and measurable outcomes. It is not about keeping the lights on, but about giving leadership confidence to innovate without fear. Or, as we frame it at Asygma, it is about Timeback: returning hours otherwise lost to breaches, firefighting and audits.
The future of Timeback security
The direction of travel is clear: security operations will be populated by AI agents, governed by human experts, and benchmarked by compliance regimes. Those who embrace this model will not only reduce incidents but also reallocate human capital towards growth and innovation.
The question is not whether AI will reshape security. The question is who will harness it responsibly, securely and in favour of time.
Talk to Asygma about making your Copilot and Microsoft Security work together securely, proactively and with measurable impact.
Glossary
- TSP (Technology Success Partner): Strategic partner delivering integrated technology, security and productivity outcomes.
- Proactive monitoring: Continuous AI-enabled observation designed to anticipate risks before they escalate.
- Threat response: Containment and remediation actions taken once a threat is identified.
- SOC (Security Operations Centre): A facility or managed service that monitors and responds to security incidents.
- AI agents: Autonomous routines that can plan and execute multi-step tasks with oversight.
References
- Cybersecurity Ventures (2025) Official Cybercrime Report 2025. Cybersecurity Ventures. Available at: https://cybersecurityventures.com/official-cybercrime-report-2025/ (Accessed: 14 September 2025).
- European Commission / AI Act (2024) The Act (texts and explorer). EU AI Act. Available at: https://artificialintelligenceact.eu/the-act/ (Accessed: 14 September 2025).
- Gartner (2024) Information Security Spending Through 2028. Gartner. Available at: https://www.gartner.com/en/articles/information-security (Accessed: 14 September 2025).
- IBM Security (2025) Cost of a Data Breach Report 2025. IBM. Available at: https://www.ibm.com/reports/data-breach (Accessed: 14 September 2025).
- MIT News (2025) 3 Questions: Modelling adversarial intelligence to exploit AI’s security vulnerabilities. MIT CSAIL. Available at: https://news.mit.edu/2025/3-questions-una-may-o-reilly-modeling-adversarial-intelligence-0129 (Accessed: 14 September 2025).
- Strubell, E., Ganesh, A. and McCallum, A. (2019) Energy and Policy Considerations for Deep Learning in NLP. arXiv:1906.02243. Available at: https://arxiv.org/abs/1906.02243 (Accessed: 14 September 2025).
