We analyze the attack surface continuously with AI; vulnerabilities are found before attackers find them, and BAS simulations measure defensive effectiveness.
The critical topics this service addresses and the outcome we deliver in each.
With continuous attack-surface discovery we monitor subdomain, open-port and TLS-endpoint changes and make vulnerabilities visible before attackers.
With MITRE ATT&CK-based BAS simulations we regularly verify the real effectiveness of security controls.
With EPSS and CISA KEV-based automatic vulnerability prioritisation we direct remediation resource to the most critical gaps.
We report BAS results with a MITRE ATT&CK heatmap and connect the dashboard to existing operations via SIEM/SOAR or a standalone Power BI/Grafana.
How we phase the service across delivery, governance, and connected service pillars.
Attack-surface discovery: with EASM we continuously discover and monitor the external surface (subdomain, open port, TLS endpoint).
Simulation: with MITRE ATT&CK-based BAS scenarios we test defence layers in a controlled way without running real exploits.
Prioritisation and reporting: with an EPSS/KEV-based workflow we prioritise vulnerabilities and make results visible with a MITRE ATT&CK heatmap.
Illustrative surfaces where this service is commonly activated.
Organisations wanting to continuously and automatically monitor the external attack surface to complement periodic pentests.
Security teams wanting to regularly verify the real effectiveness of SIEM, EDR, firewall and WAF controls with BAS.
Teams wanting to order vulnerabilities with EPSS/KEV-based prioritisation and direct resource to the most critical gaps.
This service's depth on sector-specific technical and compliance topics.
With attack-surface discovery and subdomain, open-port and TLS-endpoint monitoring we keep the external surface continuously visible; it complements, not replaces, pentests.
With platforms such as AttackIQ, SafeBreach or XM Cyber we run MITRE ATT&CK-based controlled attack simulation that does not run real exploits.
With machine-learning anomaly detection and EPSS/KEV feed integration we prioritise vulnerabilities on a risk basis.
Traditional security operations are inherently reactive: they detect attacks after indicators of compromise have been observed in the environment. AI-driven attackers now automate reconnaissance, vulnerability discovery, and initial access at machine speed, creating a window between compromise and detection that human-paced security operations cannot close. Our Preemptive and AI-Driven Security practice shifts the advantage back to defenders by applying AI to anticipate attack patterns, automate preventive countermeasures, and reduce the attack surface before adversaries can exploit it.
Turn the outcome into a measurable target with baseline, owner, and review cadence
Make risk and response indicators visible through measured controls, rehearsed playbooks, and evidence review
The preemptive AI security engagement covers technology deployment, model tuning, integration with existing security operations, and ongoing optimisation. We address the full attack lifecycle with AI-driven preventive controls at each phase: reconnaissance, initial access, persistence, lateral movement, and exfiltration. Deception technology deployment creates additional detection layers that are invisible to attackers but highly valuable for intelligence collection.
Turn the outcome into a measurable target with baseline, owner, and review cadence
Make stakeholder confidence, quality, and adoption outcomes traceable through agreed evidence indicators
Turn the outcome into a measurable target with baseline, owner, and review cadence
Preemptive AI security deliverables demonstrate the shift from reactive to proactive security through quantified metrics. Prevention rate, mean time to prevent, and attack surface reduction are tracked from day one, providing a clear return on security investment narrative for the board and for cyber insurance renewals.
Demonstrate security ROI to the board with quantified attack prevention metrics and dwell time reduction
Provide cyber insurers with evidence of AI-driven preventive controls to secure reduced premiums
Make risk, control, and compliance indicators visible through measured targets and evidence records
Signature-based detection matches known attack patterns and is ineffective against novel threats, zero-days, and living-off-the-land techniques. AI-driven security uses behavioural modelling to establish a baseline of normal activity for users, devices, and network flows, then identifies deviations that indicate attack behaviour even when no known signature exists. This enables detection of previously unseen attack techniques that bypass traditional controls.
We implement a graduated response model: low-confidence detections trigger alerts for human review, medium-confidence triggers automated logging and rate limiting, and high-confidence triggers autonomous containment only for specific pre-approved action types such as blocking a malicious IP or isolating a device showing clear signs of ransomware encryption activity. All autonomous actions are logged, reversible within seconds, and trigger immediate analyst notification.
Yes. Deception assets are designed to be completely invisible and inaccessible to legitimate users following normal workflows. Honeypots and decoy credentials are deployed in locations, applications, and directories that no legitimate user would access in the course of normal business. Any interaction with a deception asset is therefore a high-confidence indicator of malicious activity with near-zero false positives.
AI security platforms continuously retrain behavioural models against observed activity in your specific environment, meaning the model reflects your current baseline rather than a generic industry average. Threat intelligence feeds inject new adversary TTP data to update detection models. We also conduct quarterly model performance reviews to assess detection rates, false positive rates, and coverage against the current MITRE ATT&CK matrix.
We track three primary metrics: prevention rate (percentage of attack attempts blocked before reaching detection phase), mean time to prevent (time from first attacker action to automated countermeasure), and attack surface reduction percentage over time. These are reported weekly in automated dashboards and monthly in executive summaries with trend analysis and comparison to industry benchmarks.
Yes, with appropriate sanitisation. Attacker TTPs and indicators of compromise collected from deception interactions can be anonymised, converted to STIX 2.1 format, and shared via TAXII feeds with trusted ISACs and threat intelligence sharing communities relevant to your sector. Sharing accelerates community-wide detection improvement while any client-specific contextual data is removed before publication.
This short form routes your request to the right support team. We clarify context first, then define the safe sharing method.
Privacy-aware first contact; safe sharing flow when needed; no sales pressure.
